Journal Editorial Board Editor-in-ChiefProf. Thomas Byeongnam YOON, PhD.
Founding Editor-in-Chief
ICACT Transactions on the Advanced Communications Technology (TACT) Editors
Prof. Jun-Chul Chun, Kyonggi University, Korea
Dr. JongWon Kim, GIST (Gwangju Institute of Science & Technology), Korea
Dr. Xi Chen, State Grid Corparation of China, China
Prof. Arash Dana, Islamic Azad university , Central Tehran Branch, Iran
Dr. Pasquale Pace, University of Calabria - DEIS - Italy, Italy
Dr. Mitch Haspel, Stochastikos Solutions R&D, Israel
Prof. Shintaro Uno, Aichi University of Technology, Japan
Dr. Tony Tsang, Hong Kong Polytechnic UNiversity, Hong Kong
Prof. Kwang-Hoon Kim, Kyonggi University, Korea
Prof. Rosilah Hassan, Universiti Kebangsaan Malaysia(UKM), Malaysia
Dr. Sung Moon Shin, ETRI, Korea
Dr. Takahiro Matsumoto, Yamaguchi University, Japan
Dr. Christian Esteve Rothenberg, CPqD - R&D Center for. Telecommunications, Brazil
Prof. Lakshmi Prasad Saikia, Assam down town University, India
Prof. Moo Wan Kim, Tokyo University of Information Sciences, Japan
Prof. Yong-Hee Jeon, Catholic Univ. of Daegu, Korea
Dr. E.A.Mary Anita, Prathyusha Institute of Technology and Management, India
Dr. Chun-Hsin Wang, Chung Hua University, Taiwan
Prof. Wilaiporn Lee, King Mongkut’s University of Technology North, Thailand
Dr. Zhi-Qiang Yao, XiangTan University, China
Prof. Bin Shen, Chongqing Univ. of Posts and Telecommunications (CQUPT), China
Prof. Vishal Bharti, Dronacharya College of Engineering, India
Dr. Marsono, Muhammad Nadzir , Universiti Teknologi Malaysia, Malaysia
Mr. Muhammad Yasir Malik, Samsung Electronics, Korea
Prof. Yeonseung Ryu, Myongji University, Korea
Dr. Kyuchang Kang, ETRI, Korea
Prof. Plamena Zlateva, BAS(Bulgarian Academy of Sciences), Bulgaria
Dr. Pasi Ojala, University of Oulu, Finland
Prof. CheonShik Kim, Sejong University, Korea
Dr. Anna bruno, University of Salento, Italy
Prof. Jesuk Ko, Gwangju University, Korea
Dr. Saba Mahmood, Air University Islamabad Pakistan, Pakistan
Prof. Zhiming Cai, Macao University of Science and Technology, Macau
Prof. Man Soo Han, Mokpo National Univ., Korea
Mr. Jose Gutierrez, Aalborg University, Denmark

Copyright ⓒ 2017 GiRI (Global IT Research Institute)Dr. Youssef SAID, Tunisie Telecom, Tunisia
Dr. Noor Zaman, King Faisal University, Al Ahsa Hofuf, Saudi Arabia
Dr. Srinivas Mantha, SASTRA University, Thanjavur, India
Dr. Shahriar Mohammadi, KNTU University, IranProf. Beonsku An, Hongik University, korea
Dr. Guanbo Zheng, University of Houston, USA
Prof. Sangho Choe, The Catholic University of Korea, korea
Dr. Gyanendra Prasad Joshi, Yeungnam University, korea
Dr. Tae-Gyu Lee, Korea Institue of Industrial Technology(KITECH), korea
Prof. Ilkyeun Ra, University of Colorado Denver, USA
Dr. Yong Sun, Beijing University of Posts and Telecommunications, China
Dr. Yulei Wu, Chinese Academy of Sciences, China
Mr. Anup Thapa, Chosun University, korea
Dr. Vo Nguyen Quoc Bao, Posts and Telecommunications Institute of Technology, Vietnam
Dr. Harish Kumar, Bhagwant institute of technology, India
Dr. Jin REN, North china university of technology, China
Dr. Joseph Kandath, Electronics & Commn Engg, India
Dr. Mohamed M. A. Moustafa, Arab Information Union (AIU), Egypt
Dr. Mostafa Zaman Chowdhury, Kookmin University, Korea
Prof. Francis C.M. Lau, Hong Kong Polytechnic University, Hong Kong
Prof. Ju Bin Song, Kyung Hee University, korea
Prof. KyungHi Chang, Inha University, Korea
Prof. Sherif Welsen Shaker, Kuang-Chi Institute of Advanced Technology, China
Prof. Seung-Hoon Hwang, Dongguk University, Korea
Prof. Dal-Hwan Yoon, Semyung University, korea
Prof. Chongyang ZHANG, Shanghai Jiao Tong University, China
Dr. H K Lau, The Open University of Hong Kong, Honh Kong
Prof. Ying-Ren Chien, Department of Electrical Engineering, National Ilan University, Taiwan
Prof. Mai Yi-Ting, Hsiuping University of Science and Technology, Taiwan
Dr. Sang-Hwan Ryu, Korea Railroad Research Institute, Korea
Dr. Yung-Chien Shih, MediaTek Inc., Taiwan
Dr. Kuan Hoong Poo, Multimedia University, Malaysia
Dr. Michael Leung, CEng MIET SMIEEE, Hong Kong
Dr. Abu sahman Bin mohd Supa'at, Universiti Teknologi Malaysia, Malaysia
Prof. Amit Kumar Garg, Deenbandhu Chhotu Ram University of Science & Technology, India
Dr. Jens Myrup Pedersen, Aalborg University, Denmark
Dr. Augustine Ikechi Ukaegbu, KAIST, Korea
Dr. Jamshid Sangirov, KAIST, Korea
Prof. Ahmed Dooguy KORA, Ecole Sup. Multinationale des Telecommunications, Senegal
Dr. Se-Jin Oh, Korea Astronomy & Space Science Institute, Korea
Dr. Rajendra Prasad Mahajan, RGPV Bhopal, India
Dr. Woo-Jin Byun, ETRI, Korea
Dr. Mohammed M. Kadhum, School of Computing, Goodwin Hall, Queen's University , Canada
Prof. Seong Gon Choi, Chungbuk National University, Korea
Prof. Yao-Chung Chang, National Taitung University, Taiwan
Dr. Abdallah Handoura, Engineering school of Gabes - Tunisia, Tunisia
Dr. Gopal Chandra Manna, BSNL, India

Copyright ⓒ 2017 GiRI (Global IT Research Institute)Dr. Il Kwon Cho, National Information Society Agency, Korea
Prof. Jiann-Liang Chen, National Taiwan University of Science and Technology, Taiwan
Prof. Ruay-Shiung Chang, National Dong Hwa University, Taiwan
Dr. Vasaka Visoottiviseth, Mahidol University, Thailand
Prof. Dae-Ki Kang, Dongseo University, Korea
Dr. Yong-Sik Choi, Research Institute, IDLE co., ltd, Korea
Dr. Xuena Peng, Northeastern University, China
Dr. Ming-Shen Jian, National Formosa University, Taiwan
Dr. Soobin Lee, KAIST Institute for IT Convergence, Korea
Prof. Yongpan Liu, Tsinghua University, China
Prof. Chih-Lin HU, National Central University, Taiwan
Prof. Chen-Shie Ho, Oriental Institute of Technology, Taiwan
Dr. Hyoung-Jun Kim, ETRI, Korea
Prof. Bernard Cousin, IRISA/Universite de Rennes 1, France
Prof. Eun-young Lee, Dongduk Woman s University, Korea
Dr. Porkumaran K, NGP institute of technology India, India
Dr. Feng CHENG, Hasso Plattner Institute at University of Potsdam, Germany
Prof. El-Sayed M. El-Alfy, King Fahd University of Petroleum and Minerals, Saudi Arabia
Prof. Lin You, Hangzhou Dianzi Univ, China
Mr. Nicolai Kuntze, Fraunhofer Institute for Secure Information Technology, Germany
Dr. Min-Hong Yun, ETRI, Korea
Dr. Seong Joon Lee, Korea Electrotechnology Research Institute, korea
Dr. Kwihoon Kim, ETRI, Korea
Dr. Jin Woo HONG, Electronics and Telecommunications Research Inst., Korea
Dr. Heeseok Choi, KISTI(Korea Institute of Science and Technology Information), korea
Dr. Somkiat Kitjongthawonkul, Australian Catholic University, St Patrick's Campus, Australia
Dr. Dae Won Kim, ETRI, Korea
Dr. Ho-Jin CHOI, KAIST(Univ), Korea
Dr. Su-Cheng HAW, Multimedia University, Faculty of Information Technology, Malaysia
Dr. Myoung-Jin Kim, Soongsil University, Korea
Dr. Gyu Myoung Lee, Institut Mines-Telecom, Telecom SudParis, France
Dr. Dongkyun Kim, KISTI(Korea Institute of Science and Technology Information), Korea
Prof. Yoonhee Kim, Sookmyung Women s University, Korea
Prof. Li-Der Chou, National Central University, Taiwan
Prof. Young Woong Ko, Hallym University, Korea
Prof. Dimiter G. Velev, UNWE(University of National and World Economy), Bulgaria
Dr. Tadasuke Minagawa, Meiji University, Japan
Prof. Jun-Kyun Choi, KAIST (Univ.), Korea
Dr. Brownson ObaridoaObele, Hyundai Mobis Multimedia R&D Lab , Korea
Prof. Anisha Lal, VIT university, India
Dr. kyeong kang, University of technology sydney, faculty of engineering and IT , Australia
Prof. Chwen-Yea Lin, Tatung Institute of Commerce and Technology, Taiwan
Dr. Ting Peng, Chang'an University, China
Prof. ChaeSoo Kim, Donga University in Korea, Korea
Prof. kirankumar M. joshi, m.s.uni.of baroda, India
Dr. Chin-Feng Lin, National Taiwan Ocean University, Taiwan
Dr. Chang-shin Chung, TTA(Telecommunications Technology Association), Korea

Copyright ⓒ 2017 GiRI (Global IT Research Institute)Dr. Che-Sheng Chiu, Chunghwa Telecom Laboratories, Taiwan
Dr. Chirawat Kotchasarn, RMUTT, Thailand
Dr. Fateme Khalili, K.N.Toosi. University of Technology, IranDr. Izzeldin Ibrahim Mohamed Abdelaziz, Universiti Teknologi Malaysia , Malaysia
Dr. Kamrul Hasan Talukder, Khulna University, Bangladesh
Prof. HwaSung Kim, Kwangwoon University, Korea
Prof. Jongsub Moon, CIST, Korea University, Korea
Prof. Juinn-Horng Deng, Yuan Ze University, Taiwan
Dr. Yen-Wen Lin, National Taichung University, Taiwan
Prof. Junhui Zhao, Beijing Jiaotong University, China
Dr. JaeGwan Kim, SamsungThales co, Korea
Prof. Davar PISHVA, Ph.D., Asia Pacific University, Japan
Ms. Hela Mliki, National School of Engineers of Sfax, Tunisia
Prof. Amirmansour Nabavinejad, Ph.D., Sepahan Institute of Higher Education, Iran

Copyright ⓒ 2017 GiRI (Global IT Research Institute)

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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Evalution Procedure

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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Journal Procedure
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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Journal Submission Guide
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Copyright ⓒ 2017 GiRI (Global IT Research Institute)Volume. 6 Issue. 6

6J

1 A Detailed Large-Scale Radio Propagation Characteristics Approaches with Time and Spatial Ratio

1060

Jinhyung Oh, Young Jun ChongRadio Resource Research Institute, Electronic Telecommunications Research Institute (ETRI), Daejeon, Republic ofKorea

2 A Process-Aware Drone-Equipped 3D Engine and Wireless Control Measurement Platform for Integrated

1067

Management of SOC Facilities
Young-Geol Lee*, Younlae Lee**, Yeonyi Jang***, Minjae Park**Department of Computer Software, Daelim University, South Korea**R&D Center, KGI, LTD., South Korea***Department of Applied Information Technology and Security, Ansan University, South Korea

3 Metric of Vulnerability at the Base of the Life Cycle of Software Representations

1073

Mikhail Buinevich*, Konstantin Izrailov*, Andrei Vladyko**The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, Saint Petersburg, Russia

Copyright ⓒ 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 2017

1060

A Detailed Large-Scale Radio Propagation
Characteristics: Approaches with Time andSpatial Ratio
Jinhyung Oh, Young Jun ChongRadio Resource Research InstituteElectronic Telecommunications Research Institute (ETRI), Daejeon, Republic of Korea
jinhyung@etri.re.kr, yjchong@etri.re.kr
Abstract—The 3.5GHz band was determined as the mobile
communication frequency in IMT-2020. Basically, TD-LTE insmall cell environment and supplementary downlink in hotspot
area will be used in 3.5GHz band. In addition, the frequency
resources are becoming insufficient over time due to the
explosive increase in the radio equipment. If we use frequency
resources in a time and space based sharing manner, it isexpected that we will be able to efficiently use the scarcefrequency resources. According to recent trends, the channelsounder for the 3.5GHz band consists of a universal device such
as NI equipment for baseband and transceiver and its own
modules which are high power amplifier, RF switch, timing
module and antenna. In this paper, we introduce the channelsounder and have verified various measurement parameterssuch as path loss, delay spread, K-factor and channel capacity
for actual radio measurements through this channel sounder inurban and suburban areas in the 3.5GHz band. Additionally, weshow the result of the path loss modelling with time and space
rate using this system.
Keyword—Channel sounder, Radio propagation, Time andspatial ratio, Frequency sharing

R

I. INTRODUCTION

ESEARCH on radio wave propagation characteristics
and development of propagation models has become an
indispensable research area for the design, introduction,
performance analysis and verification of new radio
communication systems. The studies on the propagation
characteristics and modeling for the sub-6GHz band havebeen continuously studied for stable positioning of the newcommunication system in major countries [1]-[3]. Theinternational standards organizations such as ITU, IEEE,———————————————————————
Manuscript received September 2, 2017. This work was supported by the
ICT R&D program of MSIP/IITP [No.2017-0-00066, Development oftime-space based spectrum engineering technologies for the preemptive
using of frequency] and a follow-up of the invited journal to the conference
paper of the 19th International Conference on Advanced Communication
Technology (ICACT2017).
Jinhyung Oh is with radio resource research group, Electronics andTelecommunications Research Institute, Daejeon, Republic of Korea(phone:+82-42-860-5275;
fax:+82-42-860-5199;
e-mail:jinhyung@etri.re.kr).
Young Jun Chong is with radio resource research group, Electronics andTelecommunications Research Institute, Daejeon, Republic of Korea(e-mail: yjchong@etri.re.kr).

3GPP, and WINNER propose standard analysis methods andmodeling results. In recent years, major domestic andinternational standards organizations are examining 5G
wireless communication systems operating in various
sub-6GHz bands such as 400MHz, 700MHz, 4GHz and5GHz. From the viewpoint of radio propagation
characteristics, radio wave characterization and thedevelopment of new radio models, the consensus anddedication of major international standards organizations ismore important than individual countries. The reason is thatthe acquisition and use of the spectrum and its impacts are notmerely affecting individual countries. We will now explain
why the 3.5GHz band is important for radio measurement.
First, the 3400-3600MHz band was selected as the mobile
communication frequency in IMT-2020 along with the1427-1518MHz band [4]. The second is that major
companies such as Qualcomm are claiming to use the 3.5GHz
band for small cell TD-LTE services [5]. Finally, the 3.5GHz
band is likely to be used as a downlink supplemental link due
to the explosion of frequency resources [6]. Because of thesemany reasons, the 3.5 GHz band is considered to be an
important frequency resource for future mobile
communication environments. When many communication
devices and systems are going to use the 3.5 GHz band, thebest and most efficient way is to share frequency resources inspace and time. If a particular frequency band is not always
used temporally or spatially, then each communication device
and system will be able to share frequency resources.
Therefore, in this paper, we introduce the 3.5GHz band radio
propagation measurement system and analyze themeasurement result of the corresponding band using thissystem to confirm the temporal or spatial propagation
characteristics according to frequency sharing.
II. SYSTEM SPECIFICATIONIn this section, we introduce the specification of ourdeveloped radio measurement system. As shown in Fig. 1
below, our radio measurement system consists of five main
components: baseband digital signal processing module, RFtransceiver module, timing module, antenna, monitoring
panel and storage module.

Copyright ⓒ 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 2017

1061

TABLE I
IMPLEMENTATION PARAMETERS FOR RADIO MEASUREMENT SYSTEM
Parameters

Fig. 1. System Total Shape

A. Baseband Digital Signal Processing Module
The baseband digital signal processing module is basicallydeveloped using universal NI PXI modules and the output
signal is generated as direct sequence spread spectrum(DSSS)BPSK with 100Mcps. In baseband digital signal processing
module, it consists of PXI 8135 for the computing function,
NI 5792 for the receiving adapter module, NI 5793 for thetransmitting adapter module and PXIe 7966 for the FPGA
module.
B. RF Transceiver Module
The RF transceiver module is also made up of NI universal
modules which are Tx/Rx IF modules and RF up/down
converter. The RF upconverter raises the signal with thevariable IF frequency below 3GHz to the RF frequency of
5GHz. And the RF downconverter serves to lower the signal
having an RF frequency of 5GHz to an IF frequency of 3GHz.
And the high power amplifier(HPA) utilizes RFHIC
RUM43020 commercial product with output powers of up to
20 watts.

Specification

Frequency

400MHz-4GHz

Bandwidth

5MHz-130MHz

Channel Impulse
Response

68dB(below 10ns resolution, above 65dB)

Transmit PowerMax 7dBm

Receive Auto Gain
Control (AGC)

Min -70dBm

File Save and Replay

Receive data save and replay possible

Transmit and Reception
Filter

0.1-1(SRRC Filter, roll-off factor variable)

E. Monitoring Panel and Storage Module
In the monitoring panel, key measurement data such as
power level, spectrum mask, I/Q data and channel impulse
response(CIR) are displayed. As shown in the Fig. 2-a below,
the left side of the monitoring screen is configured to change
the frequency, bandwidth, transmission power, receive auto
gain control(AGC) and filter roll-off factor for operation of
the radio wave measuring system. The measurement system
operating parameters and current measurement statusmonitoring screens are implemented in NI LabVIEW and canbe modified if functional modifications are required. Because
measurement data is stored every second, a huge amount of
measurement data accumulates on NI hard RAID system
which is a massive storage device and a PCI express interface
is built between NI PXI and NI hard RAID system for fast
data storage and processing. The detailed parameters of ourradio wave measurement system are shown in Table 1.

C. Timing Module (TIMU)
To produce the synchronized timing signal on the RF
module and baseband, a rubidium oscillator with a 10MHz
reference signal is utilized. To keep the time synchronization
between the transmitter and the receiver of the radio
measurement system, an RF cable is connected between thetransmitter’s TIMU and the receiver’s TIMU. The clock
signal is also inserted to the RF and baseband modules fromthe TIMU.
D. Antenna
The antennas used in the radio measurement system aredeveloped to operate in the band between 3.5GHz and3.7GHz. The antenna spacing is lambda(=85.7mm@3.5GHz)
and the antenna gain is designed around 4-5dBi to obtain
additional HPA output. The horizontal antenna pattern of theantenna is omni-directional, and the vertical antenna gain isdesigned to be distributed within the upper and lower 45
degrees.

Fig. 2-a. Receiver Display

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 20171062

propagation environment with the system introduced in thispaper, time synchronization should be established between
the transmitter and receiver. The magnitude and time delay of
the received signal can be checked through cross-correlation
between the received signal and the known PN sequence. The
resolution of the time delay is closely related to the bandwidthof the signal. For example, a signal with a bandwidth of
100MHz has a time delay resolution of 10ns, so it can bedetected up to 10ns in a multipath signal.

Fig. 2-b. Transmitter Display

F. Transmit Signal Generation and Receive Signal
Processing Procedure
Fig. 3 shows the procedure for generating the transmit
signal. The length of the PN sequence is 4096, and thissequence is oversampled twice using zero padding. To
remove the adjacent signal from the transmitter, a squaredroot raised cosine filter with a roll-off factor 0.22 is used. The
oversampled PN sequence is upconverted to IF frequency
with a 200MHz sampling rate.
As shown in the Fig. 4, the received signal through thewireless channel performs A/D conversion at a sampling rateof 200MHz. After generating I/Q data, DC offset and I/Qphase offset should be removed. Next, the received signal iscross-correlated with a previously known PN sequence after
processing the squared root raised cosine filter.

Fig. 5. Transmission and Receiving Antenna Configuration

Fig. 3. Transmit Signal Generation Procedure
Fig. 6. Measurement Location and GPS Information of the Tx Location

Fig. 4. Receive Signal Processing Procedure

III. MEASUREMENT
A. Synchronization
In order to perform cross-correlation between the received
signal and the known PN sequence before measuring theB. Scenario
In this subsection, we briefly describe the measurement
environment and scenarios. As shown in Fig. 5, thetransmission antenna is installed on a pole with a height of
7.3m and the receiving antenna is installed on a vehicle with aheight of 2m. Measurement are carried out near the Daejeon
Metropolitan City Hall to analyze the propagation
environment in the small cell urban environment. The
location of the measurement area and the GPS information of

Copyright ⓒ 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 20171063

the transmission point are shown in Fig. 6. The measurement
scenario depends on the time and space rate measurement,
and the detailed measurement contents according to the timeand space rate will be described below.
1) Time Ratio Measurement Scenario:
Since the measurement of the radio wave according to thetime rate is performed by fixing the position of the transmitterand receiver, the transceiver is arranged as shown in Fig. 7.
The measurement time is conducted for seven and half hours
from 11:30 a.m. to 19:00 p.m., considering lunch and work
time.

Fig. 8. Radio Measurement Map for Space Ratio

Fig. 7. Radio Measurement Map for Time Ratio2) Spatial Ratio Measurement Scenario:
The radio wave measurement is carried out while driving
near the Daejeon Metropolitan City Hall. The transmitter isplaced in the same point as the time rate measurement, and thereceiver is installed in a moving vehicle. Most of themeasurement areas are high-rise buildings and large-scaletransportation corridors. The measurement results are stored
every one second while the vehicle carrying the receiver istravelling within an area of approximately 0.5km from thetransmitter. The moving path of the receiver-equipped vehicle
for the measurement of the space rate is shown in Fig. 8.

Fig. 9. Detailed Steps for Measurement Data Analysis

IV. PERFORMANCE RESULTS
As can be seen in Fig. 9, there are several steps to analyze
the radio wave measurement results. In the transformmeasurement data step in Fig. 9, because our measurement
and analysis program is based on MATLAB, we convert thefiles stored in the TDMS file format which is saved in NI
HDD RAID system to the MAT file format that can be read by
MATLAB. In manufacture analyzing data step after filetransforming, among the measured PN sequences that are
repeatedly stored four times, the first and fourth PN sequencesthat may have been contaminated at the receiving process are
removed, and only the PN sequences at the second and third
positions are selected to analyze. Through the measurement
time, location from the GPS and the channel impulse
response(CIR) stored in our radio measurement system, we
can derive radio wave measurement parameters such as pathloss and delay spread, etc.

Fig. 10. Path Loss Result

A. Radio Wave Parameter Analysis
1) Path Loss:
We derive path loss results as shown in Fig. 10. This graphshows the results inside the ETRI which is a suburban area,
and the results near the Daejeon Metropolitan City Hall area,
which is an urban area. The path loss measured in the urban
area near the Daejeon Metropolitan City Hall is larger thanthat in the suburban area as the distance increases. This isbecause there is a higher and denser building in the urban area.
In (1), there are several variables to express the path loss
formula. In urban area, L0 is 20.06, n is 4.204 and Xσ is

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 2017

8.91dB. In suburban area, L0 is 4.367, n is 3.431 and Xσ is11.26dB.
Path Loss = L0  10n log10  d dref   X (1)2) Delay Spread:
Figure 11 shows the results of CDF in terms of time delay.
The results in the blue graph, urban area, show a large
distribution in the case of long time delay compared to theresult in the red graph. Because there are a lot of highbuildings in the urban area, many reflection waves are
occurred. So, the large time delay results are relatively
happened in the urban area. In (2), A is 0.0186 and B is 0.88
in suburban. In urban area, A is 0.3611 and B is 0.3593 in (2).

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The time rate refers to the statistical properties of the fieldstrength measured at a fixed location and indicates how much
more than a few dB have been measured in the corresponding
percentage of the total measurement time. The graph on theleft side of Fig. 12 shows the strength of the received signal
according to the total measurement time. As shown on theright graph of Fig. 12, the received signal strength values
according to the 10%, 50% and 90%-time rate are -4.86dB,-6.19dB and -8.07dB, respectively. And Fig. 13 is a graphrepresenting the magnitude of the received signal when the
50%-time ratio is assumed.

Fig. 14. The Variation Value for Signal according to the 50% Time Percentage

We show the probability distributed function (pdf) graphaccording to the strength of the received signal and search the
distribution with similar tendency to the derived measurement
result pdf graph. As shown in the right side of Fig. 14, a graphhaving similar distribution to the measurement results isderived, and its distribution is a normal distribution with astandard deviation of 1.47. Table 2 shows the path loss results
according to the time rate application.
TABLE IIMEASUREMENT ENVIRONMENT CONSTANT VALUE

Fig. 11. Delay Spread Result

Delay Spread = A  d B(2)

B. Performance Results for Time Rate
The measurement results for the time rate are shown as
below figures.

C. The Performance Results for Spatial Ratio
The result of the path loss due to the mobile measurement isshown in Fig. 14 below.
Fig. 12. Measurement Results for Time Ratio and Receiving Values according
to Time Ratio

Fig. 13. The Variation Value for Signal according to the 50% Time Percentage

Fig. 15. Measurement Data with Mobile Vehicle

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 5, September 2017

To analyze the measurement results related to the spatial rate,
we should divide the results in Fig. 15 into the LOS andNLOS regions. The LOS signal distribution is shown in Fig.
16, and the curve fitting formula is expressed in (3).

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10% and 90%.

Fig. 18. NLOS Signal Distribution for Spatial Ratio

Fig. 16. LOS Signal Distribution for Spatial Ratio d 
LLOS 20.55  5.109log10  f   47.93log10  d 
 ref (4) d 
LLOS 99.89  31.65log10  f   16.31log10 (3) d 
 ref To verify the distribution of the signal with spatial rate, we
obtain the difference of the LOS path loss model of equation(3) from the LOS actual measurement value. The distributionof the results is shown in Fig. 17.

Fig. 19. Deduction for NLOS Signal Probability Distribution

Fig. 17. Inference for LOS Signal Probability Distribution

From Fig. 17, we have obtained the LOS signal pdf andconfirmed that extreme value distribution has the most similar
distribution to our measurement results. The path loss model
equation of NLOS is obtained from the actual measured data
curve fitting, and the graph and equation are shown in Fig. 18
and formula (4). From Fig. 18 and equation (4), we confirm
the pdf result and the most similar distribution function is
t-location scale function as shown in Fig. 19. Based on theabove results, we obtain path loss results according to thespatial rate as shown in Fig. 20. We can confirm the path loss
value for 10%, 50% and 90% spatial ratio is -46dBm, -49dBm
and -78dBm, respectively, when the distance between
transmitter and receiver is about 200m. And we can check thatthe path loss is increased as the spatial rate increases from

Fig. 20. Path Loss Results according to the Spatial Ratio

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V. CONCLUSIONSIn this paper, we describe the results of measurement
analysis in our radio measurement system and 3.5GHz bandsmall cell environment. We measured the actual environment
through the system we developed and derived a path loss
model based on time and space ratio. Due to the shortmeasurement period, the reliability of the analysis ofmeasurement results may be somewhat insufficient. However,
the tendency of analyzing the whole measurement result iswell confirmed. In the future, we will try to get more accurate
results by drawing sufficient measurement results.
ACKNOWLEDGMENT
This work was supported by Institute for Information &communications Technology Promotion (IITP) grant fundedby the Korea government (MSIP) (No.2017-0-00066,
Development of time-space based spectrum engineering
technologies for the preemptive using of frequency)
REFERENCES
[1]

[2]

[3]

[4]
[5]
[6]

Sun Kun, Wang Ping and Li Yingze, ”Path loss models for suburban
scenario at 2.3GHz, 2.6GHz and 3.5GHz,” Antennas, Propagation and
EM Theory, 2008. ISAPE 2008. 8th International Symposium on,
Kunming, 2008, pp. 438-441.
M. Rizwan and S. A. Abbas, ”Median path loss, fading and coveragecomparison at 3.5GHz and 700MHz for mobile WiMax,” Multitopic
Conference, 2008. INMIC 2008. IEEE International, Karachi, 2008,
pp. 266-271.
Shanjian Liu, Yan Zhang, Yanhua Rui, Chunhui Zhou, Shidong Zhou
and Tingchang Wang, ”5.8GHz broadband channel characteristic
analysis using measurement data,” Wireless, Mobile and Multimedia
Networks, 2006 IET International Conference on, hangzhou, China,
2006, pp. 1-4.
http://www.dcenr.gov.ie/communications/en-ie/spectrum/Pages/Agenda-Item-1.1-results.aspx
https://www.qualcomm.com/media/documents/files/lte-tdd-the-global
solution-for-unpaired-spectrum.pdf
http://www.fiercewireless.com/tech/story/verizon-qualcomm-anderics
son-partner-field-trials-35-ghz-spectrum-sharing/2014-07-13

Jinhyung Oh received his B.S degree in ElectricalCommunications Engineering from Korea Advanced
Institute Science and Technology (KAIST), Daejeon,
in 2008 and M.S. degree in Electrical and Electronic
Engineering from Korea Advanced Institute Scienceand Technology (KAIST), Daejeon, in 2010. In 2010,
he joined the Electronic and Telecommunications
Research Institute (ETRI) where he is a researcher ofthe Radio Technology Department. His researchinterests include wireless communications, interference analysis and digital
beamforming.
Young-Jun Chung received the B.S. degree from the
Jeju University, Jeju Island, Korea, in 1992, and the
M.S. degree in electronics engineering from Sogang
University, Seoul, Korea in 1994, and Ph.D degree inElectronic Engineering from Chungnam National
University, Daejeon, Korea in 2005.
Since 1994, he has been with ETRI, Daejeon, Korea,
where he is a leader of spectrum engineering researchand section principle member of the research staff ofthe Radio Technology Department. His research interests include RF circuit,RF systems, and spectrum engineering.

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1067

A Process-Aware Drone-Equipped 3D Engine
and Wireless Control Measurement Platform
for Integrated Management of SOC Facilities
Young-Geol Lee*, Younlae Lee**, Yeonyi Jang***, Minjae Park**Department of Computer Software, Daelim University, South Korea**R&D Center, KGI, LTD., South Korea***Department of Applied Information Technology and Security, Ansan University, South Korea
yglee@daelim.ac.kr, candy143@daum.net, isnowi@ansan.ac.kr, mjpark@daelim.ac.kr
Abstract— Disasters such as earthquakes, floods, landslides,
and bridge collapses are socially costly. We intend to integrate
facilities management to effectively manage the incidents ofthese disasters effectively. In order to utilize and automate
various IoT technologies at this time, we propose aprocess-aware drone-equipped 3D engine and wireless control
measurement platform for integrated management of SOC
facilities.
Keyword— Process-Aware System, SOC, IoT, Sensor, Drone,
3D EngineI

I. INTRODUCTION

n recent years, large-scale earthquakes have been
increasing in Korea, and they can cause huge loss of lifeand economic loss in a wide area when an earthquake occurs,
and can cause national disaster such as paralysis of thenational neural network. Due to the large scale of disasters,
new and complex disasters, the overall disaster risk of society
has increased, and the scale of damage has also become larger,
and the public's interest in disaster and safety has become
very high. The Republic of Korea has established andimplemented a 'Comprehensive Plan for Disaster and Safety
Technology Development' in order to promptly respond toand recover from disasters and other accidents such as
national protection, disaster, accident prevention, anddamage, data, etc., there is not enough integrated
management system for systematic management and———————————————————————
Manuscript received on August 1, 2017. This research was supported by agrant(code17CTAP-C129818-01) from Technology Advancement Research
Program (TARP) funded by Ministry of Land, Infrastructure and Transportof Korean government.
Youn-Geol Lee is with Department of Computer Software, Daelim
University, 29, Imgok-Ro, Dongan-Gu, Anyang-Si, Gyeonggi-Do, 13916,
South Korea (first author, phone: +82-31-442-4423, fax: +82-31-442-4428,
e-mail: yglee@daelim.ac.kr)
Yoonlae Lee is with R&D Center, KGI CO. LTD., 129, Gaetbeol-ro,
Yeonsu-gu, Incheon-Si, Gyeonggi-Do, 21999, South Korea (co-author,e-mail:candy143@daum.net)
Yeonyi Jang is with department of Applied Information Technology andSecurity, Ansan University, 155, Ansandaehak-ro, Sangnok-gu, Ansan-si,
Gyeonggi-do, 15328, South Korea (co-author, isnowi@ansan.ac.kr)
Minjae Park is with department of Computer Software, Daelim
University, 29, Imgok-Ro, Dongan-Gu, Anyang-Si, Gyeonggi-Do, 13916,
South Korea (corresponding author, phone: +82-31-442-4434, fax:
+82-31-442-4428, e-mail: mjpark@daelim.ac.kr)

utilization. In addition, it is required to develop interworking
technology that collects and analyzes various types of regularand non - disaster data in order to collect the increasing
disaster response strategies and to solve the problems. In
order to manage various SOC facilities, it is necessary toprovide detailed infrastructure information at the member
level as well as simple facilities information, which can besolved through the combined virtual space information
technology combining 3D model shape information andsensor information. In order to maintain the accurate andefficient facility maintenance, it is necessary to automaticallyconstruct a 3-D facility model by using the drone so that it
can manage the three-dimensional position in the facility inconnection with the field measurement equipment.

Fig. 1 Various Types of Disaster Situations
We propose a smart maintenance management integrated
platform for process-aware 3D facility management that canbe automated by combining IoT technology with surveillancesystem (image information, measurement equipment, etc.)
for disaster preparedness.

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1068

Fig. 2 Process Meta-Model for Proposed Platform
II. RELATED WORKS
Our research is based on researches related to drone,
wireless and process management technologies. Recently,interest in drones and IoT is very high, and there are many
related studies. In particular, [1, 2, 3] are drone studies related
to the disaster directly related to this study. [1] is a study that
responds to disaster situations. [2] is a study that is for drone
applications for supporting disaster management. [3] is a
study that is for data management of drone-based 3D modelreconstruction of disaster site. We focus on drone-equipped
3D engine for drones, and there are differences from otherstudies. And, some studies[4, 5, 6] have attempted to extend
the application of the workflow model. [4] is a workflow
model for data-intensive workflow processing. [5] is a
workflow model for IoT processing. [6, 7] are a fairly classic
studies with color and fragment concept workflow models
that can add new information to workflow models. Based onthese various previous studies[4, 5, 6, 7], we will describe an
extended workflow model tailored to the disaster situation. In
the area of facility management, interest in facility
management is very high, and conceptual and experimental
researches[8] on the remote facility monitoring field havebeen continuously carried out. [9] was implemented in asegmented manner with respect to the history and current
status of specific facility facilities. Especially, the real-timemonitoring part is not able to realize the complete system dueto the communication cost problem and performance limit.
Facility monitoring[10] that needs to take precautionary
measures is difficult to utilize positively by checking thestatus of fragmentary situation and there is no possibility ofdata sharing and it is very likely to be expanded to complex
accidents and disasters. Therefore, we will try to overcome
these problems.

III. A PROCESS-AWARE DRONE-EQUIPPED 3D ENGINE AND
WIRELESS CONTROL MEASUREMENT PLATFORM
We propose a process-aware drone-equipped 3D engine andwireless control measurement platform. Its core isprocess-aware platform, drone-equipped 3D engine, andwireless control measurement platform, each of which can be
described as follows.
A. Process-Aware Platform
We propose a platform that introduces process
management
technology.
The
platform
supports
drone-equipped
3D-engine
andwireless
controlmeasurements. The platform has expanded its process
management technology to support those functions. The mostimportant feature of this process management technology
consists of modeling technology and execution technology.
Because this platform needs the model specific to IoTenvironment, we want to extend existing workflow model.
We will extend ICN[11, 12, 13] among well-known
workflow representation methods. In addition, we extend
BPMN 2.0[14, 15, 16, 17] standard method for actual system
execution.
Process-aware platform technologies- model: modeling methodologies (ICN, BPMN 2.0)
- process management: process/workflow engines
Fig 2 represents an extended ICN(Information ControlNets) meta-model for disaster management automation. It
can be defined by adding new elements for disastermanagement and facility management automation to original
elements. We add context information for disaster situations
and situation handling for facility management toappropriately handle invoked applications for execution in
the components and components of the main workflow
process. And BPMN 2.0, the standard modeling method, can
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 6, November 2017

also be extended. BPMN classifies and divides tasks by typeto perform unit activities. We can implement this task byadding a task for the proposed situation to this generalized
type.

Fig. 3 Extended BPMN Task Types
In order to design processes for proposed platform, we canuse a new type task or activity. The defined process istransformed to an executable language in the form of xml for
automated execution. An example of a BPMN process and its
conversion to an executable language is described in Fig 4.

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The defined process model can be executed by the process
management system. A process-aware platform is basicallysplit into two phases for its definition and execution. The firstis the process model described earlier, and the second is the
management of the defined process model. The process ofcreating a model can be divided into build-time, and theprocess of managing is run-time. The two-step process is
described in Fig. 5. The process in build-time means defining
the process, and the process in run-time means that thedefined process is executed and monitored.
B. Drone-Equipped 3D Engine
Drone-Equipped 3D Engine is a mobile mapping platform
which multiple sensors and measurement systems have been
integrated to provide three-dimensional near-continuous
positioning of the platform’s path inspace and simultaneously
collected geo-spatial data.
Current drone application techniques are used for a brief
topographic survey. In the future, 3D image acquisition
technology for management facilities based on drone is anecessary element technology in next generation spatial data
scanning field, and it is highly possible to utilize it in various
creative ways.
In the case of linear facilities (levees) or large facilities(roads, bridges), it is not possible to perform accurate
management using only the GPS location information offacilities. In case of embankment, the distance between
starting point and ending point is long and it is not a straightline. Therefore, the 3D engine technology using the drone isessential for accurate maintenance and diagnosis as well as
the location of the facilities in the facilities. In the present
study, the application part of the drones was developed not
only to manage the points of the facility but also to manage
the three-dimensional position of the facilities such as the line
and the area,

Fig. 4 A BPMN Model Example with Context Type Task and Its Executable Language
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1070

Fig. 5 A Process-Aware Platform Concept

Fig. 6 Drone-Equipped 3D Engine

C. Wireless Control Measurement
Drone-Equipped Wireless Control Measurement is a
software system with data-centric language for sensing,capturing and detecting of remote point of interest or sensor
device using unmanned aerial vehicle’s autonomous andprogrammable functions.
Data dissemination system that can share the data needed
for social safety in real time in relation to the status and status
of SOC facilities is also helpful for national safety
management. In addition, new applications can be created inconjunction with climate, social phenomena, and traffic. In
order to easily and flexibly apply the network structure forreal-time control and measurement in various fields, astandard interface is devised so that various facility
management subjects can utilize it, and various information
can be extracted from a single view through sharing. This
technology is interworking with the wireless control metering
sensor based on the information from the drones based 3D
engine which can manage the position of the sensor
information. It is the basic technology for establishing smart
integrated maintenance system of SOC facilities. With thisFig. 7 Wireless Control Measurement

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 6, November 2017

technology, it is possible to make an accurate and quick
judgment on a disaster site, and minimize disaster damage.
D. A process-aware drone-equipped 3D engine andwireless control measurement platform

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wireless control measurement technologies.
In the future, we will specifically study in detail the corefunction of the drone-equipped 3d engine, and we need theresearch on the processing technology for the wirelesscontrol measurement. And we will try to automate it using
process management technology. It will be scenarioized, onecase study will be created, and the completed platform will beapplied to the disaster scene.
We strongly believe that this platform will handle thedisaster scene quickly and efficiently.
ACKNOWLEDGMENT
This
researchwassupportedbyagrant(code17CTAP-C129818-01)
fromTechnology
Advancement Research Program (TARP) funded by Ministryof Land, Infrastructure and Transport of Korean government.
REFERENCES[1]

Fig. 8 A Process-Aware Drone-Equipped 3D Engine andWireless Control Measurement Platform
By changing the recovery-oriented disaster management
system into a preventative management system, and byproviding a risk definition tool that can author a risk
prediction model, especially in the private and public sectors,
various facility managers can predict disasters and risks inadvance. It can also be automated and processed using
process management techniques.
Based on the technology described above, this technology
can automate the judgment of a disaster site. Of course, thedisposal of the facility management section for the disastersite is divided into unit tasks, and the definition andmanagement can be made flexible because it can be partiallyor totally automated. Since the contents of the unit work areextended by using the process model defined above,
information about the situation is considered now. However,as technology develops in the future, it is possible to haveinformation processing using technologies such as a new
artificial intelligence.
IV. CONCLUSIONSIn this paper, we propose a process-aware platform that
supports drone-equipped 3D engine and wireless control
measurement. We have described a drone-equipped 3D
engine and wireless control measurement capabilities andprocess management functions to automate it partially or
fully. Although it is approach from a conceptual point of view,it seems to be meaningful in that it provides the basis ofpossibility of automated drone-equipped 3D engine and

DANIEL, Kai, et al. AirShield: A system-of-systems MUAV remotesensing architecture for disaster response. In: Systems conference,
2009 3rd Annual IEEE. IEEE, 2009. p. 196-200.
[2] RESTAS, Agoston. Drone applications for supporting disastermanagement. World Journal of Engineering and Technology, 2015,
3.03: 316.
[3] KIMA, C.; MOON, H.; LEEA, W. Data Management Framework ofDrone-Based 3d Model Reconstruction of Disaster Site.
ISPRS-International Archives of the Photogrammetry, Remote Sensing
and Spatial Information Sciences, 2016, 31-33.
[4] Park, Minjae, et al. "A scientific workflow model designer based on
scientific information control nets." Advanced Communication
Technology (ICACT), 2015 17th International Conference on. IEEE,
2015.
[5] Kim, Meesun, et al. "Modeling process-aware Internet of Things
services over an ARDUINO community computing environment."
Advanced Communication Technology (ICACT), 2017 19thInternational Conference on. IEEE, 2017.
[6] JEONG, Woo-Jin; KIM, Kwang-Hoon. A Colored Workflow Modelfor Business Process Analysis. Journal of Internet Computing andServices, 2009, 10.3: 113-129.
[7] KIM, Hyung-Mok; KIM, Kwang-Hoon. A Cooperative Workflow
Modeling Methodology Using Fragment-ICNs. Journal of Internet
Computing and Services, 2005, 6.6: 107-115.
[8] Ok Hyun and Kim Seong-Jin, “Application Method of Remote Site
Monitoring in Public Road Construction Projects,” Journal of the
Korea Academia-Industrial cooperation Society, Vol. 14, No. 12 pp.
6550-6557, 2013
[9] Kyoon-Tai Kim, “Development of a Mountainous Area Monitoring
System based on IoT Technology,” Journal of the Korea
Academia-Industrial cooperation Society, Vol. 18, No. 3 pp. 437-446,
2017[10] Dong-wook Lee, Hyun-byung Chae, Soo-kwon Chae, “A Study on theDevelopment of Remote Sensing System for Rectangular Sewer Ductto Prevent and Respond to Disasters,” J. of Korean Society ofEnvironmental Technology, Vol. 18, No. 2, April 2017, pp. 103-114[11] ELLIS, Clarence; MALTZAHN, Carlos. The Chautauqua workflow
system. In: System Sciences, 1997, Proceedings of the Thirtieth
Hawaii International Conference on. IEEE, 1997. p. 427-436.
[12] KIM, Kwanghoon; ELLIS, Clarence A. ICN-based Workflow Modeland its Advances. Handbook of Research on Business Process
Modeling, 2009, 34-54.
[13] ELLIS, Clarence A.; NUTT, Gary J. Modeling and enactment ofworkflow systems. In: International Conference on Application andTheory of Petri Nets. Springer, Berlin, Heidelberg, 1993. p. 1-16.
[14] SILVER, Bruce; RICHARD, Bruce. BPMN method and style. Aptos:
Cody-Cassidy Press, 2009.
[15] WHITE, Stephen A. Introduction to BPMN. IBM Cooperation, 2004,
2.0: 0.
[16] WHITE, Stephen A. BPMN modeling and reference guide:
understanding and using BPMN. Future Strategies Inc., 2008.
[17] RODRÍGUEZ,
Alfonso; FERNÁNDEZ-MEDINA, Eduardo;
PIATTINI, Mario. A BPMN extension for the modeling of security
requirements in business processes. IEICE transactions on informationand systems, 2007, 90.4: 745-752.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 6, November 20171072

Young-Geol Lee is a full professor of computer
software at Daelim University, South Korea. He
received B.S., M.S., and Ph.D. degrees in computerscience from Inha University in 1993, 1995, and 1999,
respectively. His research interests include Database,Spatial Database, Geographic Information System,
SpatialWarehousing,Data-centric
Constraint
Language and Process-aware facility management
systems.
Younlae Lee is the director of KGI Corp., the South
Korea Software Development Co. He received B.S.
degrees in physics from Inha University and M.S.
degrees in civil engineering from Kangwon University.
He developed BlastAZ, the blasting simulation program.
And his Research interests include 3D modeling andImaging processing, Simulation systems, IOT(Internetof Things) and location-based infrastructure
maintenance systems.
Yeonyi Jang is an assistant professor of Applied
Information Technology and Security at Ansan
University, South Korea. She received Ph.D. degrees inAdvanced Imaging Science Multimedia, and Film from
Chung-ang University in 2011. Her research interests
digital image processing, 3D-Modeling andInformation Security.

Minjae Park is an assistant professor of computer
software at Daelim University, South Korea. He
received B.S., M.S., and Ph.D. degrees in computerscience from Kyonggi University in 2004, 2006, and2009, respectively. His research interests include
groupware, workflow systems, BPM, CSCW,
collaboration theory, process warehousing and mining,
workflow-supported social networks discovery andanalysis, process-aware information systems, data intensive workflows, andprocess-driven Internet of Things and process-aware factory automation
systems.

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 6, November 2017

1073

Metric of Vulnerability at the Baseof the Life Cycle of Software Representations
Mikhail Buinevich*, Konstantin Izrailov*, Andrei Vladyko**The Bonch-Bruevich Saint-Petersburg State University of Telecommunications, Saint Petersburg, Russia
bmv1958@yandex.ru, konstantin.izrailov@mail.ru, vladyko@sut.ru
Abstract— This article investigates the problem of the origin ofsoftware vulnerabilities in terms of their life cycle. For thispurpose, the process of creating a software by a person is
examined in detail and partially formalized with inphilosophical categories – «Form vs Essence». There is proposed
main representations of life cycle and intermediate stages of thetransformation between it. As an example there's hypotheticalcomparison of two software transformations with a graphicalinterpretation of the security level of each of final
representations. This article is a author's works continuation on
the fundamental research of the security problems of thesoftware domain.
Keyword— information safety, machine code, software
representation, vulnerability, methodology, metric, life cycle

I.

INTRODUCTION

Current problem of information security is existence ofvulnerabilities in the software (further – SW). It is necessaryfor effective counteraction to vulnerabilities all-round studies
of properties of this object, thus not only static, but also
dynamic. Last define lifetime of vulnerabilities according toa scale of process of creation of a SW. So, the analysis ofpoints of appearance of vulnerabilities will allow to evaluate
the current security level of a product and to make its forecast
for different variations of development process. The analysisof points on a scale where vulnerabilities can be guaranteed
found, will allow to construct the systems of theirneutralization more effectively.
As process of creation SW is interactive and step-by-step,and points of emergence of vulnerabilities have to correspondto steps of interaction of the person with him – at the sametime vulnerabilities can possess various degree ofpremeditation from the person. It will allow to create
typification of vulnerabilities on points of their emergenceand prerequisites to it.
———————————————————————
Manuscript received data is October 13, 2017. This work is a follow-up
of the invited journal to the accepted conference paper of the 18th
International Conference on Advanced Communication Technology.
M. V. Buinevich is with The Bonch-Bruevich Saint-Petersburg State
University of Telecommunications, Russian Federation, Saint-Petersburg,
22-1 Prospekt Bolshevikov (e-mail: bmv1958@yandex.ru).
K. E. Izrailov is with The Bonch-Bruevich Saint-Petersburg State
University of Telecommunications, Russian Federation, Saint-Petersburg,
22-1 Prospekt Bolshevikov (corresponding author to provide phone:+7(921)555-2389; fax: none; e-mail: konstantin.izrailov@mail.ru).
A. G. Vladyko is with The Bonch-Bruevich Saint-Petersburg State
University of Telecommunications, Russian Federation, Saint-Petersburg,
22-1 Prospekt Bolshevikov (e-mail: vladyko@sut.ru).

Processing of a program code automatic means can beconsidered an analog of interaction of the person with a code
as the rules put in them are also created by the person – fordisposal of monotonous work. The set of steps of interactionin this case will be similar.
II.

AREAS OF LIFE OF VULNERABILITIES INSUBMISSIONS OF THE SOFTWARE

The research of dynamic properties of vulnerabilities
begun in author's article [1] is under construction on states
SW in certain points of life cycle. Such states can be
described by means of the philosophical categories reflecting
interrelation of two parties of everyone representations of aprogram code (further – Representation): his form (external
manifestation – the flowchart [2], a programming language,etc.) and essence (inner meaning – logic of use of elements ofschemes, operators of language, etc.). In fact, the form is only
one of possible reflections of essence within the current
environment (in particular it is various for the person – thetext and images, and machine – a binary code). Justificationof the chosen division into states it is possible to find possible
use at construction SW appropriate design means: experts ofa certain profession and the standard automating programs(further – Utilities). Each of means according to own mission
carries out transformation of Representation from previous to
the subsequent (and with application a reverse engineeringand in the opposite direction [3, 4, 5, 6], for example, for the
benefit of search of vulnerabilities). Offered in above to thementioned author's article of Representation, describing thecreations SW typical process for telecommunication devices,
and involved for this means, are given in Table 1.
TABLE 1.
REPRESENTATIONS SOFTWARE AND DESIGN MEANS FOR THEIR
TRANSFORMATIONS
OrderInitialProject tool for transformation№
representations
Name
Type
--Creator
Specialist
1
Main idea
Concept developer
Specialist
2
Concept model
Design architect
Specialist
3
Architecture
Algorithmist
Specialist
4
Code algorithms
Coder
Specialist
5
Source code
Compiler
Utility
6
Assembler code
Assembler
Utility
7
Machine code
LinkerUtility
8
Image file-----

Note. The first Representation arises only owing to activityof the creator, and the last finishes process of creation SW.
It should be noted that a profession the algorithmist and thecoder extremely seldom meets in modern practice, though it

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 6, November 2017

was demanded at initial stages of development of the regionof programming. In current state of IT area they are united bya profession the programmer, leading to the fact that expertsof the last are forced to be engaged in transformation of two
Representations with various appointment at once (see No. 3
and No. 4 in Table 1). Authors consider that such situation iscaused by purely economic reasons and hypothetically is theindirect source of vulnerabilities influencing quality (in senseof safety) the final product.
Considered in [1] states point to significant jumps between
forms and essence SW, lowering details of transformations
between the next Representations. And as states in itself arecompletely static – any changes without participation ofdesign means (from Table 1) are impossible – that cognitive
stages of transformations between Representations are of
special interest as in them new vulnerabilities ([7, 8]) can
appear.
III.REPRESENTATION TRANSFORMATION STAGES
According to the interpretations of form and essence
entered above, reflection of any Representation in theobjective (physical) world can be created by means of only
his form which is obviously consisting of the system ofothers, smaller – elementary forms. In the subjective (mental)
world, Representation has an appearance of the complete
essence consisting also of the system of elementary essence.
And as the structure and properties of forms of various
Representations are, as a rule, essentially various and don't
give in to external regularity, it is possible to maketransformation only consciously and only through theiressence storing all communications lacking for this purpose.
A rare exception are transformations between extremely
similar Representations by means of trivial rules (forexample, the translation of a binary code from hexadecimalIntel HEX in binary record). We will repeatedly note that incase of use of Utilities the situation essentially doesn't change
as though the last and make a transfer automatically, however
generally these means will transform an entrance form to own
internal representation – i.e. work with essence, and thengenerate the last according to an output form.
We will consider abstractly separate taken transformationof Representation of i to the subsequent Representation of i+1(further – Representation of j) which is carried out by acertain project tool. The essence of work of any such tools
comes down to three fundamental phases: analysis ofRepresentation of i, its processing and synthesis ofRepresentation of j.
As an example we will use conversion of algorithms of aprogram code (Representation of i) to the source code
(Representation of j) the coder (the expert – project means forcreation of the code implementing the given algorithm). So,
the coder studies the given algorithm (for example, it has
flowchart appearance), realizes its sense, selects a suitable
programming language (if it is not set in advance) and
implements on it a necessary functionality. Obviously, thecoder shall the be sign, both with the form of the flowchart ofan algorithm, and with a paradigm of a programming
language and its syntax; also his abilities and experience shallallow to work with the corresponding essence ofRepresentations.

1074

A. Phase 1. Analysis
The phase of the analysis of Representation by the person
consisting in his understanding is rather difficult anddebatable; nevertheless, the existing researches (in particular,
in the field of Text Comprehension [9]), allow to allocate thefollowing stages of this process.
Stage 1. Perception
At this stage of people makes initial recognition of a formof Representation of i by means of sense organs (as a rule,sight). As it isn't possible to person to capture all form ofRepresentation entirely (as though the flowchart consisted of
the only graphic element), his breakdown on elementary
forms is made. At the same time the elementary sense (or interms of the entered categorial couple – essence) compared toeach such elementary form remains not certain so far.
Representation of i from area of the physical world passes
into own world of the subject studying him. So, the coder,studying the flowchart, obtains initial visual information on
her elements and their communications.
Stage 2. Understanding
At this stage of person compares the received elementary
forms to certain elementary essence according to own
thesaurus (the system of comparisons of terms and theirconcepts). Representation of i acquires a certain semantic
essence, though rather separate. So, the coder begins toperceive elements of flowcharts with a condition (a symbol –the Decision) as conditional transitions to elements with dataprocessing (a symbol – Process).
Stage 3. Interpretation
At this stage person synthesizes the uniform essence ofRepresentation of i which is the coordinated whole in a brain,using the elementary essence understood by him. At the sametime, as a rule, the total sense of Representation is much moredifficult, than summation of separate semantic elements fromthe previous stage – classical synergetic effect. Judgment ofRepresentation by semantic interpretation of the understoodforms is in this way made. This way a coder understands thegeneral sense of work of an algorithm.
B. Phase 2. Processing
As Representations of i and j are based on own uniqueelements of essence (corresponding to the missions), between
the last the corresponding converting is necessary. The phaseof processing of representation in working memory of theperson [10] consisting of the only stage is for this purpose
intended.
Stage 4. Re-comprehension
The stage can be considered the most difficult and the leaststudied – at it the sense SW constructed on elements ofessence of Representation of i in identical sense on elementsof essence of Representation of j will be transformed. The
stage is similar to modeling process where the essence ofRepresentation of i has an appearance of a certain internal
model in a brain of the person which then is investigated forthe purpose of receiving (or transformations in her) the newmodel corresponding to the essence of Representation of j.
So, the coder, using own cognitive skills, not just finds

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 6, November 2017

unambiguous compliance of steps of an algorithm toprogramming language designs, namely will transform themin more difficult way. At the same time the rule «one in one»doesn't work as the flowchart of an algorithm already is 2D(the unidirectional count with cycles), and a program code –1D (a set of lines with own structure). The situation manytimes becomes complicated if the algorithm corresponds to
one paradigm of programming (for example, imperative), andthe required programming language – another (for example,
functional).
C. Phase 3. Synthesis
The synthesis phase following a reconsideration phase has
to construct his form on the essence of Representation of j –to transfer Representation from the subjective world of theperson to objective surrounding. Thus, the phase reasonably
can consist of the stages belonging to an analysis phase, butwhich are carried out upside-down. There are even utilities
with the close purposes and the principles of work (forexample, SWIM [11]).
Stage 5. Shaping
At this stage person breaks the uniform essence ofRepresentation on elementary, ready for reflection in thephysical world. There is a certain structure of maintenance ofRepresentation of j though without any specification
concerning her form. So, the coder from conscious sense of1075

work of an algorithm (step-by-step) receives the scheme of
performance according to a programming paradigm (in thiscase, imperative).
Stage 6. Exposition
At this stage person compares to each elementary essence
the same elementary form. The return thesaurus (the systemof comparisons of concepts and their terms) is for thispurpose used. Programming language forms are compared tosemantic elements of Representation of j. So, the coder
presents the scheme of performance in the form of set ofdesigns of language.
Stage 7. Decoration
At this stage person makes concrete entry ofRepresentation of j strictly in the required look – graphic,
text, binary and so forth. Representation has a uniform
consistent form. So, the coder makes the end result of theactivity – writes down a program code of an algorithm.
Stages can be considered intuitive and logical on a set ofexamples. The structure of phases and stages and also graphic
interpretation of their work in categories of a form/ essence isgiven in the Fig. 1. Zone 1, 2 and 3 correspond to conditional
degree of sensibleness of essence by the person for each of
Phase and Stage of transformation of Representations.

Fig. 1. Scheme of transformation of Representation and its graphic interpretation

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1076

Explanations to the diagram in the Fig. 1 for the example
consisting in creation of the source code of function of achoice maximum of two numbers according to the given IV.TYPIFICATION OF VULNERABILITIES WHEN TRANSFORMING
REPRESENTATION
flowchart of an algorithm are provided in Table 2.
TABLE 2.
EXPLANATIONS TO THE SCHEME OF TRANSFORMATION OF
REPRESENTATION AND EXAMPLE OF CODING
Result of
Elements of the scheme of transformation (for№transformation
example)Submission of theArguments:
Begin
X, Y
flowchart of analgorithm
Input: X, Y
(Representation i)
T = Y

No

X > Y

Yes

T = X

Output: T
End

1

Visual elements ofthe flowchart of analgorithm(Form 1, 2)

2

Sense of elementsof the flowchart ofanalgorithm(Essence 1, 2)

3

Sense
of
all
algorithm(Total Essence)
Sense of the sourcecode implementing
thealgorithm(Total Essence’)
received by theintermediate
models(Model, Model’)Sense of elementsof the source code
(Essence 1’, 2’, 3’)

4

5

6

Constructions of
the source code
according to alanguage
syntax(Form 1’, 2’, 3’)

7

Representation
listing of the source
code(Representation j)

Return: T«Begin» with receiving two variables: X and Y;«Decision» on transition (conditional branch)with a condition of comparing of two variables«X > Y»;two «Processes», each of which appropriates totemporary variable T one of two X and U
variables;«End» with return of a variable T; unidirectional
connectors of all elements;
Obtaining value of the X and Y variables;
comparing of the X and Y variables;
if X > Y, then assignment of a variable T ofvariable X value , else – value variable Y;
resetting of variable T value;Comparing of values of the X and Y variables
and resetting maximum of them through variable
T value;
Determination of function with arguments X andY returning the value, maximum from them;
use for storage of result of temporary variable T;

As it was specified, vulnerabilities can appear in the courseof change of Representations (incremental and step by step),
it is expedient to enter their typification in the place in which
they appeared – i.e. to the stage initiating vulnerability. So,
the erratic understanding the coder on Stage will lead 2 sensesof an element of the conditional branching (for example, theincorrect sign of comparing) to incorrect interpretation of asense of all algorithm and, as a result, to writing of theincorrect source code (for example, returning the minimum
number instead of maximum) even if all remained stages
were executed absolutely truly.
The premeditation level corresponding to a conscious
participle of the person to vulnerability appearance can beadditional typification reasonably. The following can be suchtypes of premeditation for the given stages:
 unwitting – shown in the mistakes made by the person
not consciously i.e. owing to its physical or
psychological state, or because of external factors;
 malicious – based on conscious entering of mistakes
into Representations, for example, owing to personal
ambitions, the order or the compelled reactions.
It is obvious that if the casual nature of vulnerabilities isinherent with different degree in all stages of transformationof Representations, then malicious can belong only to a stageof reconsideration and the next to him. It follows from thefact that around a stage there is sufficient degree ofsensibleness of essence by the malefactor (corresponding tozones in the Fig. 1); at all other stages person just tries tounderstand sense of Representation through perception, andthen to describe it.
V.ASSESSMENT OF SAFETY OF TRANSFORMATION OF
REPRESENTATION

Targetinga signature of function witharguments of X and Y; comparing values of the
X and Y variables of construction IF-ELSE;
depending on result of comparing locating in
temporary variable T of one of values of the X
and Y variables;
resetting from function variable value T;
Function title with two arguments X and Y;
beginning and end of a function body;declaration of temporary variable T;
conditional GOTO statement for «X > Y»;
branches of the conditional branch on theseparate units of a code containing assignment ofa variable T of the X or Y variables;
return statement from function of a variable T;
int funct(int x, int y) {int t;
if (x>y) {t = x;} else {t = y;}
return t;}

Note. First column «№» means order number of Stage.

We will consider process of receiving each new
Representation of safety change, previous from a position.
We will enter the level of safety of Representation of i as
density of his essence without vulnerabilities (or the relationof «volume» of essence without vulnerabilities to general«volume») and we will designate him the 𝑆𝑖 parameter. It is
obvious that for Representation without vulnerabilities of
𝑆𝑖 =1. Safety of the following Representation j is defined by
𝑆𝑗 ; at the same time 𝑆𝑗 < 𝑆𝑖 condition will be always met.
Equality of 𝑆𝑗 = 𝑆𝑖 is possible only at automatictransformation by the entrusted Utilities and won't beconsidered here. The situation when 𝑆𝑗 > 𝑆𝑖 meaning that asa result of transformation, a part of vulnerabilities has
disappeared and in general is extremely improbable (thoughsome Utilities of assembly can signal about use of unsafe
operations and offers options of their correction).
We will consider features of each of the specified SW
development process stages and also types of vulnerabilities
from the point of view of premeditation. For this purpose wewill compare coefficient of increase in level of insecurity ofRepresentation with each stage – 𝐾𝑁𝑃 (𝑡), corresponding to
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 6, November 2017

probability of emergence of vulnerability like P (U –Unwitting, M – Malicious) at the Stage N (from 1 to 7) intimepoint of t (a point on a scale of performance of stages).
The value of coefficient can lie in the range [0..1]. Thencoefficients influence the level of safety of Representation as:
𝑆𝑗 = ∏ (1 − 𝐾𝑁𝑃 (𝑡)) × 𝑆𝑖 .
𝑁=1..7

Thus, each stage due to «nonzero» probability ofemergence of vulnerabilities inevitably reduces safety of new
Representation.
The coefficient 𝐾𝑁𝑃 (𝑡) has dependence on timepoint
because of stages aren't carried out instantly, and have acertain duration in process of which (from the beginning of astage before its end) vulnerabilities can appear with various
speed (the corresponding derivative from 𝐾𝑁𝑃 (𝑡) in a certain
timepoint of 𝑡0 ). Such assumption is competent as at somestages of vulnerability practically don't appear (because of
simple subconscious work of a human brain), on some havethe increased intensity (because of need to the person to workwith the large volume of memory), and on some have growthrate, time-dependent (because of complex processing by abrain both the initial, and constantly obtained new
information). We will consider coefficient 𝐾𝑁𝑃 (𝑡) for each ofa stage and type of premeditation of vulnerability.
For definition of a temporary type of coefficient of increasein level of insecurity we will use the simplified reasoning –
but, nevertheless, reasonable logic and experience as
definition of an exact formula for 𝐾𝑁𝑃 (𝑡) demands carryingout a full-fledged research on a set of statistics (and its
processing) concerning places and types of emergence ofvulnerabilities on each of stages of transformation ofRepresentations. Also we will divide a type of conversion
within each stage from the point of view of topology into 4groups: «one to one» (further, «1:1») or the transformation of
one element to other element; «one to several» (further, «1:N») or the transformationof a uniform element to a set of other elements; «several to one» (further, «N:1») or the transformation
a set of one elements in a uniform element; «several to several» (further, «N:N») or thetransformation a set of one elements to a set of other elements.
VI.INFLUENCE OF SUBJECTIVE COMPONENTS OF THE PERSONThe Stage 1 is defined by consecutive perception ofelementary forms of Representation of i from a uniform form
and is carried out by the person with good degree ofautomaticity, though in the form of «1:N». As essence by the
person is still not understood, emergence only of casualvulnerabilities is possible. Thus, the stage can becharacterized by the average value of coefficient having alinear appearance:
𝐾 𝑈 (𝑡) = 𝑘1𝑈 × 𝑡{ 1 𝑀.
𝐾1 (𝑡) ≡ 0
The Stage 2 is defined by consecutive understanding ofelementary forms of Representation of i by their1077«replacement» by the corresponding elements of essence, i.e.
in the form of «1:1». As the understanding of elementaryessence doesn't give a full picture of sense an Idea, emergence
only of casual vulnerabilities is possible. Thus, the stage canbe characterized by low value of coefficient and have a linear
appearance:
𝐾 𝑈 (𝑡) = 𝑘2𝑈 × 𝑡{ 2 𝑀.
𝐾2 (𝑡) ≡ 0
The Stage 3 is defined by consecutive interpretation ofelementary essence of Representation of i in uniform essence
which is carried out by the person with good degree ofautomaticity, though in the form of «N:1». As the person at
the end of a stage already has an idea of full essence, besides
casual vulnerabilities emergence of rare malicious
vulnerabilities is possible. Thus, the stage can becharacterized by average value of coefficient for casual andvery low for malicious vulnerabilities and have a linear
appearance:
𝐾 𝑈 (𝑡) = 𝑘3𝑈 × 𝑡{ 3𝑀.
𝐾3 (𝑡) = 𝑘3𝑀 × 𝑡
The Stage 4 is defined by transformation of sense ofRepresentation of i to sense of Representation of j (by theiressence) which is made by the person most consciously.
Transformation is similar to process of «the solution of atask» – poorly studied creative action. Nevertheless, theapproximate type of coefficient of 𝐾4𝑃 can be received as aresult of the following reasonings. Firstly, the stage isn't
consecutive (on extremely measure, in the same degree asothers) since there is no uniform algorithm of the decision any
tasks – it is creative with emergence of Insight. Secondly,
though transformation is also similar to a look «1:1»,
nevertheless, because of their dimensions of keeping ofpeople is capable to work only with their parts that leads to a
type of «N:N». And, thirdly, standard feature of work with
non-standard tasks is existence of a step of assessment i.e. as
far as the decision has come or has achieved the objectives.
Thus, it is possible to assume that generally the stage will
gradually transform one parts of essence of Representation ito other parts of essence of Representation j, checking at thesame time each new received part for coordination both withinitial, and with already received from the point of view ofproximity to a goal.
According to the made assumption, with eachtransformation of a part of essence the number of the madeactions increases by one – because of need of coordination ofeach following part for a limit with all previous. The lastcorresponds to the arithmetic sequence (which sum ofmembers is equal: 1 + 2 + ⋯ + 𝑛 =(𝑛+1)×𝑛2) and directly

influences emergence of casual vulnerabilities.
As the person has complete idea of the essence ofRepresentations, this stage is most preferable to thevulnerabilities introduced by the malefactor; at the same time,
their addition can be carried out in process of transformation.
Thus, the stage can be characterized by coefficient with thehigh value for casual vulnerabilities having a squareappearance and an average for malicious vulnerabilities and

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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 6, November 2017

to have a linear appearance:
𝐾 𝑈 = 𝑘4𝑈 × 𝑡 2{ 4𝑀.
𝐾4 = 𝑘4𝑀 × 𝑡
The Stage 5 is defined consecutive decomposition of
uniform essence of Representation of j in elementary essence
which is carried out by the person with good degree ofautomaticity, though in the form of «1:N». As the person at
the beginning of a stage still operates with full essence,
besides casual vulnerabilities emergence rare malicious ispossible. Thus, the stage can be characterized by averagevalue of coefficient for casual and very low for malicious
vulnerabilities and have a linear appearance:{

𝐾5𝑈 (𝑡) = 𝑘5𝑈 × 𝑡.
𝐾5𝑀 (𝑡) = 𝑘5𝑀 × 𝑡

The Stage 6 is defined by consecutive formation ofelementary forms of Representation of j by «replacement» ofthe corresponding elements of essence with them, i.e. in theform of «1:1». As work with elementary essence doesn'tallow to operate with full sense of Representation, emergence
only of casual vulnerabilities is possible. Thus, the stage canbe characterized by low value of coefficient and have a linear
appearance:

1078

existing (diverse and specialized for own tasks, for example[12, 13, 14, 15]), the entered metrics at rather high level ofabstraction sets safety of the gained Impression on everyone
a stage of interaction of people code. At the same time, themetrics allows to predict safety level in the future as it
depends only on the chosen life cycle of creation SW.
In case of more real reflection of safety the matrix of M
won't be single since unwitting and malicious vulnerabilities
exert impact at each other on each of stages of transformationof Representations. Formalization of this situation is moredifficult and demands carrying out additional researches.
Graphic interpretation of coefficient 𝐾𝑁𝑃 (𝑡) for various
stages and types of vulnerabilities, illustrating their
distinctions and features, has the following stylized
appearance (Fig. 2):

Stage 1

Stage 2

Stage 3

Unwitting

Unwitting

Unwitting

Malicious

Malicious

Malicious

Stage 4

𝐾 𝑈 (𝑡) = 𝑘6𝑈 × 𝑡{ 6 𝑀.
𝐾6 (𝑡) ≡ 0
The Stage 7 is defined by the consecutive description of a
uniform form of Representation of j from elementary forms
which is carried out by the person with good degree ofautomaticity, though in the form of «N:1». As the person havecompletely departed from the essence of Representation,
emergence only of casual vulnerabilities is possible. Thus, thestage can be characterized by average value of coefficient andhave a linear appearance:
𝐾 𝑈 (𝑡) = 𝑘7𝑈 × 𝑡{ 7 𝑀.
𝐾7 (𝑡) ≡ 0
It is expedient to write down the level of insecurity ofRepresentation a two-component vector which elements set
density of each of types of vulnerabilities (casual andmalicious): 𝑆𝑖 ≡ [𝑆𝑖𝑈 , 𝑆𝑖𝑀 ]. The general deterioration in safety
in this case and also according to the entered coefficients, will
be defined by the equation[𝑆𝑗𝑈 , 𝑆𝑗𝑀 ] = 𝑀 × [𝑆𝑖𝑈 , 𝑆𝑖𝑀 ], whereM – a metrics (in the form of a single matrix) increases in
insecurity when transforming Representations. The metrics isdefined how:М ≡ ∏ ([
𝑁=1..7

1 − 𝐾0 𝑈
𝑁

00

1 − 𝐾0 𝑀
𝑁] × [𝑆𝑖𝑈 , 𝑆𝑖𝑀 ]) ,where 𝐾0 𝑃𝑁 – coefficient of increase in level of insecurity on
the termination of a stage N for type of premeditation ofvulnerability of U or M. Thus, the metrics doesn't depend ontime and her components are calculated as number. Unlike

Unwitting
Malicious

Stage 5

Stage 6

Stage 7

Unwitting

Unwitting

Unwitting

Malicious

Malicious

Malicious

Fig. 2. Graphic interpretation of coefficient of increase in level of insecurityof Representations for each phase

VII.COMPARISON OF SAFETY OF TRANSFORMATION OF
REPRESENTATIONS FOR STANDARD SCENARIOS OF SOFTWAREDEVELOPMENT

As an example of potential applicability of the explained
approach to assessment of safety we will make hypotheticalcomparing of two methods of creation of a program code
(Representation 5) on its architecture (Representation 3) –with use of the intermediate algorithmized representation(Representation 4) and without it. In the second case theprogrammer receives the essence of Representation ofalgorithms of a code implicitly in the course ofreconsideration of essence of Representation of architecture
in Representation of the source code, passing thereby phasesof synthesis of algorithmized Representation, its explicit
processing and the subsequent analysis.
With sufficient degree of convention, but without loss of
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ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 6, November 2017sense, change of the general coefficient of increase in level of
insecurity of Representations 𝐾 𝑃 (𝑡) (as compound of 𝐾𝑁𝑃 (𝑡)on each phase N) for both types of vulnerabilities ( 𝑃 ={𝐼, 𝑀}) in a case from use of algorithmized Representation(further – Case 1) and without him (further – Case 2) is shownon the following schedules (Fig. 3).

KP

1079

to smaller increase in insecurity of Representation 5 becauseof malicious vulnerabilities, than in Case 1.
According to the above-mentioned analysis, it is possible
to claim that the choice of each of the described ways ofcreation SW results in various probability of emergence ofvulnerabilities in Representation, at the same time thequantity of their one type can increase, and another to go
down.
VIII.CONCLUSION

Unwitting
Malicious

t
1

2

3

4

5

6

7

1

Representations: 3 → 4

2

3

4

5

6

7

Representations: 4 → 5Stagea) transformation through Representation 4 (3→4→5)

KP

Unwitting
Malicious

t
1

2

3

4

5

6

7

Stage

Representations: 3 → 5b) transformation without Representation 4 (3→5)
Fig. 3. Graphic interpretation of the general coefficient of increase ininsecurity when transforming Representations 3 in 5

At the Fig. 3 the dashed line has shown coefficient 𝐾 𝑃 (𝑡)with square dependence on t time. Units of measurements ofschedules and the scale of Stage 4 on graphics 3b are
conditional, designed to reflect the general behavior of
coefficient and difference between him for two options oftransformation.
The analysis of graphics 3) and 3) in the Fig. 3 allows to
draw the following conclusions. First, in Case 1 using ofintermediate Representation 4 means emergence of additional
Stages 5, 6, 7 (for transformation of Representations 3 → 4)
and Stages 1, 2, 3 (for transformation of Representations 4 →5) that inevitably leads to accumulative increase in insecurityof Representation 5 because of casual vulnerabilities.
Secondly, in Case 2 the refusal of use of intermediate
Representation 4 means increase in Stage 4 twice that leads
to a bigger increase in insecurity of Representation 5 becauseof casual vulnerabilities (because of a square type ofcoefficient 𝐾4𝑈 (2𝑡)). Thirdly, existence in Case 2 twice ofsmaller quantity of Stages 3, 4, 5 having not zero 𝐾𝑁𝑀 (𝑡) leads

The described approach to safety assessment SW forvarious stages of his creation allows to create the formalized
mathematical apparatus, as for assessment of current state ofsafety SW, and his forecasting (for example, as in [16]). The
last one helps to make the reasonable choice among different
variations of SW development, calculating at the same timepotentially dangerous places. Collecting expert opinions andstatistical data which will allow to set necessary invariant
parameters of the device is necessary for transformation of atheoretical research into practical tools. The future ofdevelopment of a research can develop into full processing of
all process of creation SW – removal of one of his
representations, addition of others, modification of the third– that will allow to receive final SW set safety level. Also,
adaptation of process of creation safe SW under the field of
its application, such for example as telecommunication
devices is possible.
REFERENCE[1]

M. Buinevich, K. Izrailov, A. Vladyko, "The life cycle ofvulnerabilities in the representations of software for telecommunication
devices," in Proc. 8th IEEE International Conference on Advanced
Communication Technology (ICACT), South Korea, PyeongChang,
2016, pp. 430-435.
[2] K. Xinogalos, “Using flowchart-based programming environments for
simplifying programming and software engineering processes,” IEEE
Global Engineering Education Conference (EDUCON), Germany,
Berlin, 2013, pp. 1313-1322[3] M. Buinevich, K. Izrailov, A. Vladyko, "Method for Partial Recovering
Source Code of Telecommunication Devices for Vulnerability Search,"
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Communication Technology (ICACT), South Korea, PyeongChang,2015, pp. 76-80..
[4] M. Buinevich, K. Izrailov, A. Vladyko, "Method and Prototype of
Utility for Partial Recovering Source Code for Low-Level andMedium-Level Vulnerability Search," in Proc. IEEE 18th International
Conference on Advanced Communication Technology (ICACT),South Korea, PyeongChang 2016, pp. 700-707.
[5] M. V. Buinevich, K. E. Izrailov, "Method and utility for recovering
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Copyright ⓒ 2017 GiRI (Global IT Research Institute)

ICACT Transactions on Advanced Communications Technology (TACT) Vol. 6, Issue 6, November 20171080

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Mikhail Buinevich received the D.Sc degree inEngineering from the Naval Institute of RadioElectronics, St. Petersburg, Russia, in 2010.
He has 20 years of experience in research anddevelopment in IT security. Now he is a Professor at
The
Bonch-Bruevich
Saint-Petersburg
State
University of Telecommunications and supervises
post graduates.
Prof. Buinevich is the Editor-in-chief of “Proceedingsof Telecommunication Universities” scientific journal.
Konstantin Izrailov defense his degree of PhD at
The Bonch-Bruevich Saint-Petersburg State
University of Telecommunications, Russia in 2017.
Now he is an Associate Professor at that University.
Mr. Izrailov interests include: Information & Network
Security; Software-Defined Networking; Internet ofThings.

Andrei Vladyko (IEEE member (M'14)) acquired
his degree of PhD at Komsomolsk-on-Amur State
Technical University, Russia in 2001.
At present he is a head of R&D department of The
Bonch-Bruevich Saint-Petersburg State University of
Telecommunications.
Mr. Vladyko major interests include: Information &Network Security; Software-Defined Networking;Internet of Things; Wireless Sensor Network.

Copyright ⓒ 2017 GiRI (Global IT Research Institute)