Reviewerly/exHarmony huggingface dataset:
A New Boundary of Minimum Private Key on Wiener Attack Against RSA Algorithm

Cryptography is divided into two types, namely symmetric cryptography and asymmetric cryptography. In symmetric cryptography, the key used in the encryption and decryption process is the same, while in asymmetric cryptography, the encryption and decryption processes have their keys. One of the most famous public key algorithms is the RSA algorithm, created by Rivest, Shamir, and Adleman in 1987. The strength of the RSA algorithm is based on the Integer Factorization Problem (IFP), in which when there is an integer, it will be challenging to determine the factors of that number. In its development, several attacks have been tried to find the vulnerabilities in RSA. One example of a successful attack is the attack by Wiener and Bunder & Tonien. This attack uses a continued fraction that exploits the vulnerability of using private values that are too small, which causes the need for a minimum private value boundary to overcome the vulnerability. From the research that the author has done, it is found by derivative function such that the minimum boundary of Wiener and Bunder & Tonien on private values has been successfully upgraded.
Feature Extraction Methods and Classification for Malware Incident News

Studies related to data mining are one of the topics that have received much interest recently, including for the form of unstructured data. One that is commonly discussed is the automatic classification process using machine learning methods. A large amount of data is the main obstacle in the manual classification process. However, there are still many people who have difficulty determining the right combination between feature extraction and classification methods, so with this, we provide suggestions for using a variety of ways that can produce better accuracy in text classification. This research compares several feature extraction methods, including Bag-of-Word (BoW), Term Frequency - Inverse Document Frequency (TF-IDF), and Word2Vec with focusing on the Skip-gram model. On the other hand, this research also uses several classification methods, which include Support Vector Machine (SVM), Decision Tree, Logistic Regression, Gaussian Naive Bayes, K-Nearest Neighbor, Neural Network, Random Forest, and Doc2Vec. This research used two hundred crawled articles from several web blogs that have been labeled manually and have been split into two classes, malware incident news, and non-malware incident news class. The dataset quality was also measured using an open-source Python library known as “Cleanlab”.
Performance Test of Hash Functions in Proof of Work Schemes in Blockchain

Blockchain is a decentralized system mechanism on each node. There are many aspects required to build a good blockchain system. One of the important aspects is a hash function in cryptography. Each block generated will depend on the hash value of the previous block, and the last block in the blockchain network will become the data material to generate the next block. The speed and effectiveness of the hash function algorithm for generating a hash value in the blockchain scheme are some of the reasons why this hash function algorithm was chosen. In a block, a lot of transaction data will be recorded and then computed by the hash function algorithm to produce a block hash value. The faster the computation of the resulting hash function algorithm will maintain the stability and speed of transactions in the blockchain network. This research wants to test several hash function algorithms in a simple blockchain scheme.
Verifiable Timed Commitments f or Fair Sealed-bid Auctions

Timed commitment is a type of commitment scheme that allows the recipient to open the committed value in the future without needing a committer. It has found widespread use where “time dependency” is essential. Fair contract signing, sealed-bid auctions, cryptocurrencies, and multiparty computation are the areas where timed commitments provide added value. Verifiability and fairness are key considerations for many of these applications, as they aim to foster a trusted environment. In this work, the primary emphasis is on first-price sealed-bid auctions, where we believe that both verifiability and fairness hold equal significance. With the help of verifiable timed commitments, it is possible to design an auction in a verifiable way. In addition, the construction of such a fair environment is important in building a foolproof mechanism. We propose a two-stage protocol to conduct an auction process as trustworthy as possible. Initially, the seller makes an auctioneer and bidders sign a contract with a multi-signature protocol. Later, bidders encrypt their bids and send them via verifiable timed commitment (VTC) so that they can be verifiable and forced open by the auctioneer after some predefined time.
Implementation of ChaCha20 Algorithm with Elliptic-Curve-Diffie-Hellman in Server Room Monitoring System to Prevent MITM and Reused Key Attack

A server room in a building or office usually stores essential items and must always be supervised, such as electronic devices, switches, routers, etc. One aspect of supervision in the server room is to keep the temperature and humidity stable so that the appliances in it will not be damaged due to temperatures that are too high. This supervision can be realized by creating a server room monitoring system using ESP32 and DHTII. The system can automatically display temperature and humidity data on the user interface via a smartphone. The system implements the ChaCha20 algorithm with the HTTP protocol. This algorithm tends to use the same key every time it is implemented, so in this study, additions be made to create a shared-secret key with a key exchange scheme using Elliptic Curve Diffie Hellman (ECDH). The results of the ChaCha20 and ECDH algorithms are used to ensure data confidentiality by encrypting the entire data to avoid Man-In-The-Middle-Attack (MITM) and Reused Key Attack attacks. From the results of attack testing, the ChaCha20 algorithm can prevent MITM attacks, and the application of ECDH to create a key or shared-secret for different encryption on each data transmission can prevent Reused Key Attacks.
A Novel Digital Forensic Readiness (DFR) Framework for e-Government

The increasing reliance on e-Government services has amplified the importance of digital forensic readiness (DFR) in ensuring effective incident response to mitigate the impact of cyber incidents. A specialized DFR framework would streamline incident response procedures within e-Government services, facilitating prompt identification, containment, and remediation of cyber threats. One of the most notable characteristics of DFR for e-Government is the implementation of a formal policy on forensic readiness. This paper proposes novel DFR steps to guide the formulation of such a policy. The proposed framework is tailored by combining two DFR frameworks that Claims and Elyas et al. contributed. The new framework involves identifying crucial DFR parameters and mapping domain parameters between both frameworks. The framework was then implemented in a local government organization in West Java, Indonesia. The result is a security policy recommendation for Digital Forensic Readiness, which consists of 8 domains and 33 subdomains. The security policy provides a practical approach to enhance the DFR capabilities of e-Government services to enhance incident response and cyber resilience.
Comparative Analysis of Recovery Tools For Digital Forensic Evidence Using NIST Framework 800-101 R1

The existence of technological developments extends to smartphones that are easy to use. This provides an opportunity for perpetrators to commit crimes by utilizing smartphones as a medium to commit crimes. One of these crimes can be revealed based on digital evidence obtained by mobile forensics on the perpetrator’s device. This research aims to compare the performance of forensic tools in restoring deleted data in the form of contact data, call logs, and messages used as evidence in the trial. This research process was tested with three scenarios based on NIST SP 800-101 R1 with structured preservation, acquisition, analysis, and reporting stages that ensure investigators follow the research steps so that the results can be accounted for. The result of this study is that the digital evidence obtained by the UltData Android tool is 95%. In comparison, the percentage of evidence found by the Wondershare Dr. Fone tool is SO%, and for the EaseUs Mobisaver tool, the percentage calculation is 20%. From the results obtained, UltData Android is better at restoring deleted data, and the digital evidence obtained can be used to solve crimes in court.
Evaluating the People, Process, and Technology Priorities for NIST Cybersecurity Framework Implementation in E-Government

Implementing a cybersecurity framework plays a critical role in protecting e-government systems and infrastructure from cyber threats and attacks. However, there is no consensus on prioritizing the five core components: identify, protect, detect, respond, and recover. It is also unclear whether these components should be implemented equally or customized. Similarly, no agreement exists on the importance of people, processes, and technology in implementing a cybersecurity framework This paper aims to address this gap by evaluating and comparing the priorities of the five core components and three aspects using the Analytic Hierarchy Process (AHP). The evaluation outcomes emphasize that the identify component takes precedence over the other core components, underscoring the significance of preventive measures against cyberattacks. In addition, the people aspect received the highest priority among the three implementation aspects, which highlighted the critical role of individuals in successfully implementing process-related measures and leveraging technological advancements. This research provides valuable insights for designing strategies and implementing effective and sustainable cybersecurity measures in e-government. By prioritizing the identify component and recognizing the importance of people, organizations can improve their ability to protect against threats and ensure the resilience of e-government systems.
Cyber-Risk Management Planning Using NIST CSF V1.1, ISO/IEC 27005:2018, and NIST SP 800-53 Revision 5 (A Study Case to ABC Organization)

Digital transformation continues to be carried out throughout the government and private sectors which has led to an increase in the quality of service to the community. The rapid process of digitalization is directly proportional to the cyber threats that lurk in organizational assets. So as to mitigate the risks that occur to organizational assets, it is necessary to carry out cyber risk management planning. As an organization engaged in information technology, ABC organization has an ICT unit that supports the duties and responsibilities in managing infrastructure, information systems, and organizational services. However, this unit has never done risk management planning for its assets, so it is likely that the risks that have occurred can be repeated without mitigation. Therefore, this research was made to design a cyber risk plan aimed at the ICT unit of the ABC organization using 3 security standards, including NIST CSF v1.1 as the main framework, ISO/ IEC 27005: 2018 as a supporting framework, and NIST SP 800-53 revision 5 as a recommendation for actions taken for identified risks. From the results of this study, 105 risk scenarios were obtained in the ICT unit with details of 64 accepted risks and 43 mitigated risks with 86 control recommendations. The provision of control recommendations is adjusted to the risks experienced by the organization, with the aim of controlling cyber risks.
A Linear Cryptanalysis On Simplified Kuznyechik 2

This research focuses on the application of linear cryptanalysis to Simplified Kuznyechik 2 (S-KN2). This research aims to assess the vulnerability to linear attacks on the S-KN2 algorithm. This research applies linear cryptanalysis to the S-KN2 algorithm by finding the best bias value of the S-KN2 linear approximation for each active s-box in each round, which then derives a linear equation that can be used to find a key candidate that matches the expected key. In this study, we used 5000 samples of generated plaintext and ciphertext pairs to return the subkey values in the last round. As a result, the candidate keys have bias values that are close to the theoretical bias value divided by 10. Based on these results, it is assumed that the L-Transformation has an effect on the linear approximation of S-KN2, and further research is needed in that regard.
A Low-cost Disk Imaging Device Using Raspberry Pi 4 for Digital Forensics

As with crimes that occur in the real world, cybercrime cases require investigation to gather evidence for court proceedings. Digital forensics is a great way to investigate evidence from cybercrime. Investigating a cybercrime to find digital evidence cannot be done inappropriately. Mishandling of evidence can make it invalid. It is necessary to copy the evidence before, so the investigation process is not done directly on the original digital evidence. Intended to avoid or at least minimize damage to evidence. In conducting an investigation, an investigator needs tools and supporting devices that have high accuracy, reliable, and affordable. However, most of the devices on the market are available at quite expensive prices. Therefore, in this study, performed design and construction of a low-cost disk imaging device were carried out using the Raspberry Pi 4 and implemented using Python languages to copy evidence in the digital forensics process. Testing results and analysis of the proposed device show a good performance in terms of speed and accuracy of copies.
Implementation of N-Gram Method For Automating News Ticker Generation Based on Indonesian Language

A news ticker is brief news displayed at the bottom of the television screen during specific news programs. The news ticker has a time-concerned nature, where the presentation of news heavily relies on time. The faster news is delivered through the news ticker mechanism, the better it is for the information recipients. Currently, news ticker writing still depends on human resources, which has various limitations such as limited availability for 24 hours and obstacles caused by human factors themselves. Therefore, researchers propose the implementation of Natural Language Generation using the N-Gram method to automate news ticker writing and help overcome the limitations of the existing news ticker writing mechanism The results of this study show that the N-Gram method, particularly Bigram, successfully generates sports news automatically. Furthermore, the evaluation, which involved individuals aged between 20 until 35 and language experts, found that the news ticker generated by the system was rated highly in terms of naturalness. The overall assessment ranged from very high to perfect.
A Proposed Architecture for The Indonesia’s National Blockchain

The rapid growth of digital systems and the increasing demand for secure and reliable identity management have driven the need for a robust blockchain architecture specifically designed for Indonesia’s national identity management system, known as IDNat-Blockchain. This research paper proposes a blockchain architecture that addresses the unique challenge faced by Indonesia in managing its vast population and diverse digital economy. The design incorporates multiple layers, including the hardware/infrastructure layer, network layer, consensus layer, and application layer, to ensure a secure, scalable, and interoperable system. This research paper focuses on the design and implementation of a robust blockchain architecture specifically tailored for IDNat-Blockchain, the Indonesia’s National Blockchain system. Our research found that the best-fit network for IDNat-Blockchain is private network, consortium network for IDGov-Blockchain and IDVII-Blockchain, and hybrid network for IDPub-Blockchain. We also found that Proof of Authority (PoA) combined with Practical Byzantine Fault Tolerance (PBFT) for IDNat-Blockchain, IDGov-Blockchain, IDVII-Blockchain, and IDPub-Blockchain. Layer 0 would fit to IDNat-Blockchain, Layer 1 for IDGov-Blockchain, Layer 1 combined with Layer 2 for IDVII-Blockchain, and Layer 1, Layer 2, and Layer 3 combination for IDPub-Blockchain. The choices highlight its security, decentralization, scalability, energy consumption, complexity, efficiency, transparency, cost, user, immutability, and speed consideration.
Automating News Tweet Writing Based on Indonesia Language Using NLG Approach

Over time, after the advent of the internet, the dissemination of news media has undergone developments ranging from websites to social media. However, news posted on social media is still written by reporters and undergoes editorial checks, resulting in a relatively long news processing time for posting. Therefore, to overcome these limitations, this research proposes an NLG (Natural Language Generation) approach using LSTM algorithms to assist in processing news data obtained from reporters into news tweets in the Indonesian language with the system called Automated Sports News Tweet Generator (ASNTG). The study’s findings highlight the effectiveness of this method in generating news tweets sourced directly from on-location reporters through a Telegram chatbot. By doing so, it enables the production of real-time news updates, allowing readers to stay promptly informed about the latest events. Furthermore, the generated news tweets exhibit excellent naturalness aspects, as assessed by social media users and language experts who participated as respondents.
On a Small Class of Binary Modified de Bruijn Sequences of Maximal Linear Span

The linear complexity of a periodic binary sequence is the degree of its minimal polynomial. It has been conjectured that, for any positive integer n ≥ 4, there exist binary modified de Bruijn sequences of order n whose linear complexity is $2^{n}-2$, which is the highest possible. A proof remains elusive, much less a constructive general approach to such modified binary sequences, given any arbitrary order n. This work focuses on a small class of modified binary de Bruijn sequences whose linear spans are maximal or near maximal. We devise a method to determine the liner span of the sequences in this class and show heuristically that the sequences have high linear spans, often reaching the maximal values for their respective order n.
Research on algorithm of FMCW radar level gauge

FMCW radar level gauge is widely used in industrial production. In industrial control, the FMCW radar level gauge has been used more and more widely in recent years because of its high measurement accuracy, strong anti-interference ability, convenient debugging and installation. In the FMCW radar ranging, the signal processing method used in modern FMCW radar ranging is FFT transformation, processing the difference frequency signal, calculating the difference frequency signal frequency, and obtaining the range information. The FFT algorithm can find the approximate position of the frequency signal, but not the exact frequency point. Increasing the sampling point can improve the measurement accuracy, but at the same time the calculation amount is also increased. Firstly, the composition and ranging principle of FMCW radar system are analyzed in this paper. Secondly, this paper studies the algorithms to improve the measurement accuracy of the difference frequency signal, namely chirp-z algorithm and zoomfft algorithm. After analyzing these two algorithms in detail, zoomfft algorithm is adopted. The Matlab simulation results show that the algorithm can quickly obtain the center frequency of the difference frequency signal and improve the accuracy of the ranging of the liquid level meter. Then added the CFAR analysis, the reason is that in the radar level measurement system, the liquid may be affected by the external environment to produce a splash, which will make the radar echo signal contains noise and clutter, the echo containing target information may be submerged by noise and clutter, it is difficult to detect the target information. Finally, the target information can be detected in the signal containing noise and clutter through Matlab results.
Reversible Data Hiding Algorithm in Encrypted Domain Based on Matrix Secret Sharing

Focused on the problems of user privacy disclosure and ciphertext data management in cloud service environment, this paper proposes a reversible data hiding algorithm in encrypted domain based on matrix secret sharing. Firstly, the matrix secret sharing of the pixels of the encrypted image was used to obtain n shares, in the process of constructing shared vectors for secret segmentation, the random variables involved in encryption and the location index of ciphertext feedback were used to construct the mapping relationship between them and the secret data to be embedded to generate redundant space for data embedding. Finally, the overflow pixels were processed, and the edge information was embedded by differential extension technology to obtain n marked encrypted images. The experimental results showed that the algorithm has complete reversibility. In contrast to the current algorithms, the embedding rate of the algorithm was greatly improved. The maximized embedding rate of the (3,4)-threshold scheme was 4.00bpp, and the maximized embedding rate of the (5,5)-threshold scheme was 6.40bpp, and the statistical characteristics such as correlation coefficient of the marked encrypted images were consistent with those of random image, and the security was high. The algorithm not only has the functions of privacy data protection and ciphertext authentication management, but also has the disaster recovery feature to realize the multi-party security storage of data.
Partially Shared Sparse Representation With Graph Regularizer For Multi-View Clustering

Recently, multi-view clustering has been widely studied as multi-view data can provide more useful information for improving the clustering. The performance of existing multi-view clustering algorithms is often closely related to the learned representation. Thus, how to learn an effective representation is a fundamental problem. Considering the consistency and the complementarity of the multi-view data, this paper develops a partially shared sparse representation method. To be specific, the learned sparse representation for each view is composed of two parts: one is shared by all views and the other is a view-specific part. In addition, the graph regularizer is added to the proposed model to capture the geometric structure of multi-view data for better clustering. To solve efficiently the proposed model, we design an alternating minimization algorithm based on the accelerated proximal gradient (APG) scheme. In the end, experiments on three real-world datasets are conducted to show the superiority of our approach over the state-of-the-art algorithms, including both negative data and positive data.
A Trading Scheme of Power Blockchain Based on Identity-Based Ring Signature for Anonymity and Anti-Forgery

Privacy is important in power blockchain. For privacy of distributed power transactions, we propose a SM9-compliant anonymous transaction scheme combining identity-based cryptography and ring-signature techniques, solving the complex management problem of public key and certificate while maintaining user privacy. We prove the unforgeability and anonymity of this scheme under the stochastic prediction model, which provides a secure and trustworthy anonymous transaction mechanism in the absence of a trusted transaction center. Experiments show that compared with the RSA ring signature, the scheme in this paper is superior in terms of communication cost and resource consumption, and can provide support for distributed power transactions.