Miracle-dz/UniFAR-FTUs huggingface dataset:
Breaking ACORN at Bitstream Level

Assuring the security of the Internet of Things (IoT) is much more challenging than assuring the security of centralized environments, like the cloud. A reason for this is that IoT devices are often deployed in domains that are remotely managed and monitored. Thus, they cannot be protected from physical attacks as reliably as data centers. Up till now, implementations of many established, standardized algorithms including AES and SNOW 3G have been broken by physical attacks. In this paper, we show that even the most recently designed algorithms are also vulnerable. We attack an SRAM-based FPGA implementation of ACORN v3 stream cipher, a finalist of CAESAR cryptographic competition for authenticated encryption. By modifying the content of several look-up tables directly in the bitstream, we inject faults which reduce the nonlinear feedback function of ACORN to a linear one. As a result, it becomes possible to extract the full key from $2^{15.34}$ bits of faulty keystream by an algebraic attack using $2^{35.46}$ operations. Our results, once again confirm the necessity to rethink the way cryptographic algorithms are implemented in FPGAs.
Analysis and modelling of multi-stage attacks

Honeypots are the information system resources used for capturing and analysis of cyber attacks. Highinteraction Honeypots are capable of capturing attacks in their totality and hence are an ideal choice for capturing multi-stage cyber attacks. The term multi-stage attack is an abstraction that refers to a class of cyber attacks consisting of multiple attack stages. These attack stages are executed either by malicious codes, scripts or sometimes even inbuilt system tools. In the work presented in this paper we have proposed a framework for capturing, analysis and modelling of multi-stage cyber attacks. The objective of our work is to devise an effective mechanism for the classification of multi-stage cyber attacks. The proposed framework comprise of a network of high interaction honeypots augmented with an attack analysis engine. The analysis engine performs rule based labeling of captured honeypot data. The labeling engine labels the attack data as generic events. These events are further fused to generate attack graphs. The hence generated attack graphs are used to characterize and later classify the multi-stage cyber attacks.
COVID-19 (Coronavirus Disease) Outbreak Prediction Using a Susceptible-Exposed-Symptomatic Infected-Recovered-Super Spreaders-Asymptomatic Infected-Deceased-Critical (SEIR-PADC) Dynamic Model

Extension of SIR type models has been reported in a number of publications in mathematics community. But little is done on validation of these models to fit adequately with multiple clinical data of an infectious disease. In this paper, we introduce SEIR-PAD model to assess susceptible, exposed, infected, recovered, super-spreader, asymptomatic infected, and deceased populations. SEIR-PAD model consists of 7-set of ordinary differential equations with 8 unknown coefficients which are solved numerically in MATLAB using an optimization algorithm to fit 4-set of COVID-19 clinical data consist of cumulative populations of infected, deceased, recovered, and susceptible. Trends of COVID-19 in Trends in Gulf Cooperation Council (GCC) countries are successfully predicted using available data from outbreak until 23rd June 2020. Promising results of SEIR-PAD model provide insight into better management of COVID-19 pandemic in GCC countries.
Sensitive Information Detection based on Convolution Neural Network and Bi-directional LSTM

Electronic documents can carry lots of information and are widely used in daily lives. It will cause substantial economic losses to individual users, enterprises, and governments when the documents containing sensitive information are leaked. How to detect sensitive information to prevent data leakage is still a challenge in the field of information security. This paper mainly focuses on the detection of unstructured documents containing sensitive information. Governments, military, and other institutions can actively mark whether the electronic documents contain sensitive information according to the detection results. We propose a reliable method to detect sensitive electronic documents automatically and compare it with other basic methods. The algorithm structure can extract the characteristics of the data more comprehensively to obtain better detection results. Our model outperformed the other models with 93.44 % accuracy. Our model can also reduce the time cost, which is beneficial for realistic production.
Cross Platform IoT- Malware Family Classification based on Printable Strings

In this era of rapid network development, Internet of Things (IoT) security considerations receive a lot of attention from both the research and commercial sectors. With limited computation resource, unfriendly interface, and poor software implementation, legacy IoT devices are vulnerable to many infamous mal ware attacks. Moreover, the heterogeneity of IoT platforms and the diversity of IoT malware make the detection and classification of IoT malware even more challenging. In this paper, we propose to use printable strings as an easy-to-get but effective cross-platform feature to identify IoT malware on different IoT platforms. The discriminating capability of these strings are verified using a set of machine learning algorithms on malware family classification across different platforms. The proposed scheme shows a 99% accuracy on a large scale IoT malware dataset consisted of 120K executable fils in executable and linkable format when the training and test are done on the same platform. Meanwhile, it also achieves a 96% accuracy when training is carried out on a few popular IoT platforms but test is done on different platforms. Efficient malware prevention and mitigation solutions can be enabled based on the proposed method to prevent and mitigate IoT malware damages across different platforms.
Anomaly Detection on Dynamic Bipartite Graph with Burstiness

Many real-world network data can be modelled as a bipartite graph, such as client-server in the computer network and trader-stock in the stock market. Detecting anomalies on these graphs benefit many applications, e.g., network intrusion detection and illegal trading detection. In real-world networks, these bipartite graphs are not static. Their vertices may change from time to time, and a bipartite graph may demonstrate unexpected burstiness in the form of intermittent increases or decreases in activities between vertices. In this paper, we propose a framework named BEA, a general anomaly detection framework that can effectively detect anomalies on a dynamic bipartite graph with burstiness. We evaluate our proposed framework on three public datasets against the state-of-the-art baselines. We also conduct a case study on the PayPal CSP log network. Through experiments, we show BEA's practicability and effectiveness.
SpoxelNet: Spherical Voxel-based Deep Place Recognition for 3D Point Clouds of Crowded Indoor Spaces

With its essential role in achieving full autonomy of robot navigation, place recognition has been widely studied with various approaches. Recently, numerous point cloud-based methods with deep learning implementation have been proposed with promising results for their application in outdoor environments. However, their performances are not as promising in indoor spaces because of the high level of occlusion caused by structures and moving objects. In this paper, we propose a point cloud-based place recognition method for crowded indoor spaces. The method consists of voxelizing point clouds in spherical coordinates and defining the occupancy of each voxel in ternary values. We also present SpoxelNet, a neural network architecture that encodes input voxels into global descriptor vectors by extracting the structural features in both fine and coarse scales. It also reinforces its performance in occluded places by concatenating feature vectors from multiple directions. Our method is evaluated in various indoor datasets and outperforms existing methods with a large margin.
Exploratory Adversarial Attacks on Graph Neural Networks

Graph neural networks (GNNs) have been successfully used to analyze non-Euclidean network data. Recently, there emerge a number of works to investigate the robustness of GNNs by adding adversarial noises into the graph topology, where gradient-based attacks are widely studied due to their inherent efficiency and high effectiveness. However, the gradient-based attacks often lead to sub-optimal results due to the discrete structure of graph data. To this end, we design a novel exploratory adversarial attack (termed as EpoAtk) to boost the gradient-based perturbations on graphs. The exploratory strategy in EpoAtk includes three phases, generation, evaluation and recombination, with the goal of sidesteping the possible misinformation that the maximal gradient provides. In experiments, EpoAtk is evaluated on benchmark datasets for the task of semi-supervised node classification in different attack settings. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art attacks with the same attack budgets11Our reproducible code is available at https://github.com/EpoAtk/EpoAtk.
Optical Filter Bank Modeling and Design for Multi-Color Visible Light Communications

Multi-color visible light communication (MC-VLC) can achieve a multi-fold rate by taking advantage of multiple color channels. The optical filters for separating different colors are often designed for the normal incidence case only. However, the center wavelength (CWL) of the optical filter shifts as the angle of incidence (AOI) varies, leading to performance degradation. To address the issue caused by the CWL shift, we propose a novel concept of the optical filter bank that uses a group of narrow-band optical filters and divides the whole visible light spectrum into many narrow bands of wavelengths. The optical filter bank can flexibly combine the useful signal of each monochromatic color and effectively avoid the undesired interference, even if the CWL shifts. Based on the optical filter bank, we establish the channel model and signal model of the MC-VLC system, and then jointly design the linear precoding and equalization for MC-VLC. As illustrated by simulations, the proposed optical filter bank transceiver can achieve a high rate and stable performance at varying AOIs.
Feature Extension of Gut Microbiome Data for Deep Neural Network-Based Colorectal Cancer Classification

Colorectal cancer (CRC) is the third most deadly cancer worldwide. The use of gut microbiome in early detection of the disease has attracted much attention from the research community, mainly because of its noninvasive nature. Recent achievements in next generation sequencing technology have led to increased availability of sequence data and enabled an environment for the growth of gut microbiome research. The use of conventional machine learning algorithms for automatic detection of CRC based on the microbiome is limited by factors such as low accuracy and the need for manual selection of features. Despite their success in other fields, Deep Neural Network (DNN) algorithms have limitations in microbiome-based CRC classification. These limitations include high dimensionality of microbiome data and other characteristics associated with sequence data such as feature dominance. In this paper, we propose a feature augmentation approach that aggregates data normalization methods to extend existing features of a dataset. The proposed method combines feature extension with data augmentation to improve CRC classification performance of a DNN model. The proposed model obtained area under the curve (AUC) scores of 0.96 and 0.89 on two publicly available microbiome datasets.
FTPB: A Three-stage DNS Tunnel Detection Method Based on Character Feature Extraction

The domain name system(DNS) protocol is one of the most versatile protocols in the world. If a hacker can control the DNS protocol to pass messages and control the host of victim, then no firewall can effectively intercept the DNS protocol. In recent years, the DNS tunnel is one of the most dangerous threats in the field of steganography, which supports a wide range of criminal activities. In order to detect and distinguish different types of DNS tunnels, we present a three-stage DNS tunnel detection method based on character feature extraction. This novel method named FTPB which uses feature extraction to filter out the domain names of the DNS tunnels by feature extraction classifier and then converts them into a high-dimensional vector by term frequency-inverse document frequency(TF-IDF), and reduces the dimension to 2 by principal components analysis(PCA). Ultimately, the data is classified by a binary vector classifier. Our method only needs to extract the character information of the domain names, and can effectively discover different types of DNS tunnels. Compared with traditional detection methods that can only detect DNS tunnels based on content, our research method can also have the ability to detect DNS tunnels based on codebooks, and the outcome of the evaluation substantially prove the efficacy of our method with accuracy and precision are more than 99%.
Machine Learning and Caching based Efficient Data Retrieval Framework

The explosive growth of wireless data and traffic, accompanied by the rapid advancements in intelligence and the processing power of user equipments (UEs), poses a very difficult challenge to the data providers to maintain the high data rate with sustainable quality-of-service (QoS). A lot of data can be saved by using caching based communication techniques, which would save the service providers a fortune and will make internet connectivity even more affordable. Also, there is room for saving bandwidth and using the limited number of servers and towers efficiently outputting a steadily healthy QoS. We propose an efficient data retrieval framework that uses caching based on the popularity of the pages where the popularity of pages is determined by the number of hits it gets over a month, which is the learning phase of the model and how frequently a given web page is requested. The proposed framework uses a causal decision tree in the background to determine the popularity of pages according to which the algorithm decides whether a given page is worthy of being cached or not. Results show that our proposed model outperforms the conventional data retrieval models in terms of cache missed probability.
Ultra-Low-Power and Performance-Improved Logic Circuit Using Hybrid TFET-MOSFET Standard Cells Topologies and Optimized Digital Front-End Process

Tunnel FET is recognized as one of the most promising candidates for ultra-low power applications due to its ultra-low off current and CMOS compatibility. However, some characteristics of TFET caused by asymmetric device structure and special conduction mechanism may make conventional topologies of logic circuits no longer applicable. Our previous work has reported that TFET stacking will result in severe current degradation, which makes traditional logic cells not applicable. In this paper, two solutions are proposed: first, from a logic cell perspective, novel hybrid TFET-MOSFET topologies of standard logic cells are proposed, which achieve more than 2 times lower hardware cost and intrinsic delay, hence up to 4 times lower area-power-delay product (APDP) than that of conventional TFET logic circuits. Compared to MOSFET logic circuits, the designs achieve almost 2 orders of magnitude lower power and up to 34 times lower APDP. Second, from a large-scale circuit perspective, an optimized digital front-end (DFE) is proposed. Taking serial peripheral interface (SPI) as an example, SPI circuit using the optimized DFE achieves 46% lower delay and 4 times lower APDP than that of traditional TFET SPI, and 3 orders of magnitude lower static power and APDP than that of MOSFET SPI.
False Alarm Reduction in Atrial Fibrillation Screening

Early detection of AF is essential and emphasizes the significance of AF screening. However, AF detection in screening ECGs, usually recorded by handheld and portable devices, is limited because of their high susceptibility to noise. In this study, the feasibility of applying a machine learning-based quality control stage, inserted between the QRS detector and AF detector blocks, is investigated with the aim to improve AF detection. A convolutional neural network was trained to classify the detections into either true or false. False detections were excluded and an updated series of QRS complexes was fed to the AF detector. The results show that the convolutional neural network-based quality control reduces the number of false alarms by 24.8% at the cost of 1.9% decrease in sensitivity compared to AF detection without any quality control.
Network-Aware Locality Scheduling for Distributed Data Operators in Data Centers

Large data centers are currently the mainstream infrastructures for big data processing. As one of the most fundamental tasks in these environments, the efficient execution of distributed data operators (e.g., join and aggregation) are still challenging current data systems, and one of the key performance issues is network communication time. State-of-the-art methods trying to improve that problem focus on either application-layer data locality optimization to reduce network traffic or on network-layer data flow optimization to increase bandwidth utilization. However, the techniques in the two layers are totally independent from each other, and performance gains from a joint optimization perspective have not yet been explored. In this article, we propose a novel approach called NEAL (NEtwork-Aware Locality scheduling) to bridge this gap, and consequently to further reduce communication time for distributed big data operators. We present the detailed design and implementation of NEAL, and our experimental results demonstrate that NEAL always performs better than current approaches for different workloads and network bandwidth configurations.
Compact WLAN/C-Band Multiple-Input Multiple-Output Antenna

Low correlation multiple-input multiple-output (MIMO) antenna design for WLAN and C-band is presented in this paper. Two equivalent elements that have a co-plane radiator with microstrip-line and ground plane in the truncated rectangular form are the substructure of this MIMO antenna. Multiple bent stubs are employed to obtain a dual-band function. Low envelope correlation and high isolation between two antenna elements are achieved by placing antenna elements orthogonally. It is reported that the impedance bandwidth less than −10 dB for │S11│ covers frequency ranges of 2.20-2.91 GHz (710 MHz, 28%) and 6.24-8.82 GHz (2580 MHz, 35%). These spectrums can support systems that use WiBro/WiMAX/ Bluetooth/LTE/WLAN, and C-band. The MIMO antenna has a compact area of 20 mm × 40 mm and it exhibits a stable omnidirectional radiation performance through its working frequency bands. This quality is considered a favorable factor in the industry for relevant requests.
Toward Enabling a Hundred Drones to Land in a Minute

Currently, drone research and development has received significant attention worldwide. Particularly, delivery services employ drones as it is a viable method to improve delivery efficiency by using a several unmanned drones. Research has been conducted to realize complete automation of drone control for such services. However, regarding the takeoff and landing port of the drones, conventional methods have focused on the landing operation of a single drone, and the continuous landing of multiple drones has not been realized. To address this issue, we propose a completely novel port system, "EAGLES Port," that allows several drones to continuously land and takeoff in a short time. Experiments verified that the landing time efficiency of the proposed port is ideally 7.5 times higher than that of conventional vertical landing systems. Moreover, the system can tolerate 270 mm of horizontal positional error, ±30° of angular error in the drone’s approach (±40° with the proposed gate mechanism), and up to 1.9 m/s of drone’s approach speed. This technology significantly contributes to the scalability of drone usage. Therefore, it is critical for the development of a future drone port for the landing of automated drone swarms.
Diabolo Orientation Stabilization by Learning Predictive Model for Unstable Unknown-Dynamics Juggling Manipulation

Juggling manipulation is one of difficult manipulation to acquire since some of such manipulation is unstable and also its physical model is unknown due to the complex non-prehensile manipulation. To acquire these unstable unknown-dynamics juggling manipulation, we propose a method for designing the predictive model of manipulation with a deep neural network, and a real-time optimal control law with some robustness and adaptability using backpropagation of the network. In this study, we apply this method to diabolo orientation stabilization, which is one of unstable unknown-dynamics juggling manipulation. We verify the effectiveness of the proposed method by comparing with basic controllers such as P Controller or PID Controller, and also check the adaptability of the proposed controller by some experiments with a real life-sized humanoid robot.
Processes Regulating Soil Emissions of NO and N^2O in a Seasonally Dry Tropical Forest

While much is known about control of production of NO and N[sub 2]O by nitrifying and denitrifying bacteria at the cellular level, application of this knowledge to field studies has not yielded unifying concepts that are widely applicable and that foster understanding of global sources of these atmospheric trace gases. We applied a simple conceptual model to the investigation of sources of NO and N[sub 2]O and the environmental factors affecting fluxes in a drought-deciduous forest of Mexico. Fluxes of NO and N[sub 2]O were higher in the wet season than the dry season, but addition of water to dry soil caused large pulses of CO[sub 2], NO, and N[sub 2]O emissions. Immediate increases of extractable soil NH[sub 4][sup +] and high rates of gross N mineralization and gross nitrification also were observed following wetting of dry soil. Soil NO[sub 2][sup [minus]] had accumulated during the dry season, and that NO[sub 2][sup [minus]] plus the pulse of increased soil NH[sub 4][sup +] were mostly consumed within 24 hours of wetting. This dynamic microbial processing of soil inorganic N coincided with the pulses of NO and N[sub 2]O production following wetting of dry soil. Acetylene inhibition experiments indicated that NO productionmore » was dependent on nitrification, that nitrification was the dominant source of N[sub 2]O when the soil was wetted at the end of the dry season, and that denitrification might be an important source of N[sub 2]O during the wet season. Post-wetting soil moisture was correlated negatively with NO fluxes and positively with N[sub O] fluxes. These results support a conceptual model in which N trace gas production is generally constrained by the rates of N mineralization and nitrification, while the specific ratios of NO and N[sub 2]O fluxes and the contributions from nitrifying and denitrifying bacteria are controlled largely by soil moisture. 42 refs., 5 figs., 4 tabs. « less