VdaBSC: A Novel Vulnerability Detection Approach for Blockchain Smart Contract by Dynamic Analysis by Rexford Nii Ayitey Sosu, Jinfu Chen, Edward Kwadwo Boahen, Zikang Zhang

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Cancelable finger vein authentication using multidimensional scaling based on deep learning by Mohamed Hammad, Mohammed ElAffendi, Ahmed A. Abd El-Latif

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Blockchain covert communication scheme based on the cover of normal transactions by Pengkun JIANG, Wenyin ZHANG, Jiuru WANG, Shanyun HUANG, Wanshui SONG

“…With the development of computer technology, the situation of modern network attack and defense is becoming increasingly severe, and the problem of secure transmission of secret information needs to be solved urgently.Covert communication technology embeds secret information into the carrier and transmits the information safely through the covert channel.However, the traditional covert channels face the challenges of data damaging, attack, detection and so on, which cannot meet the higher security requirements.As a public data platform, blockchain can embed secret information under the cover of a large number of transactions.With its tamper proof, anonymity, decentralization and other characteristics, blockchain can well solve the problems of traditional covert channels and achieve secure covert communication.However, the existing blockchain covert communication schemes are limited by low communication efficiency and poor security.How to improve safety and efficiency of covert communication is a research focus of blockchain covert communication.Motivated by this issue, a blockchain covert communication scheme based on the cover of normal transactions was proposed.The hash algorithm was used to build a transmission-free password table to embed secret information without changing any transaction data.Using the elliptic curve feature, transactions with hidden information can be quickly screened out from a large number of transactions, to extract secret information quickly.This scheme improves the security and efficiency of covert communication and has strong portability.Theoretical analysis shows that attackers cannot distinguish between ordinary transactions and special transactions.This scheme has high anti-detection and scalability.Besides, the experimental results of the bitcoin test network show the high efficiency of this scheme.…”
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Blockchain-Based Value-Added Tax System: A Systematic Review by Vahideh Ghanooni Shishavan, Shaban Elahi, Sadegh Dorri Nogoorani, Ali Yazdian Varjani

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Efficient Deep Learning-Based Cyber-Attack Detection for Internet of Medical Things Devices by Abigail Judith, G. Jaspher W. Kathrine, Salaja Silas, Andrew J

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Multidisciplinary Contributions and Research Trends in eHealth Scholarship (2000-2024): Bibliometric Analysis by Lana V Ivanitskaya, Dimitrios Zikos, Elina Erzikova

“…Different types of eHealth apps were supported by research on infrastructures: networks, data exchange, computing technologies, information systems, and platforms. …”
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A Novel Framework for Financial Cybersecurity and Fraud Detection Using XAI-RNN-SGRU by Smarajit Ghosh

“…Cyber threats involve unauthorized access, alteration, or deletion of private information, extortion, and disruption of business operations. Traditional network security methods need more scalability, data protection, and difficulty detecting advanced threats. …”
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Leveraging self attention driven gated recurrent unit with crocodile optimization algorithm for cyberattack detection using federated learning framework by Manal Abdullah Alohali, Hatim Dafaalla, Mohammed Baihan, Sultan Alahmari, Achraf Ben Miled, Othman Alrusaini, Ali Alqazzaz, Hanadi Alkhudhayr

“…Cybersecurity includes decreasing the risk of mischievous computer, software, and network attacks. Novel techniques have been combined into emerging artificial intelligence (AI) that attains cybersecurity. …”
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Botnet Detection Using Support Vector Machines with Artificial Fish Swarm Algorithm by Kuan-Cheng Lin, Sih-Yang Chen, Jason C. Hung

“…The number of mobile devices used globally substantially increases daily; therefore, information security concerns are increasingly vital. The botnet virus is a major threat to both personal computers and mobile devices; therefore, a method of botnet feature characterization is proposed in this study. …”
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Sylph: An Unsupervised APT Detection System Based on the Provenance Graph by Kaida Jiang, Zihan Gao, Siyu Zhang, Futai Zou

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Review of malware detection and classification visualization techniques by Jinwei WANG, Zhengjia CHEN, Xue XIE, Xiangyang LUO, Bin MA

“…With the rapid advancement of technology, network security faces a significant challenge due to the proliferation of malicious software and its variants.These malicious software use various technical tactics to deceive or bypass traditional detection methods, rendering conventional non-visual detection techniques inadequate.In recent years, data visualization has gained considerable attention in the academic community as a powerful approach for detecting and classifying malicious software.By visually representing the key features of malicious software, these methods greatly enhance the accuracy of malware detection and classification, opening up extensive research opportunities in the field of cyber security.An overview of traditional non-visual detection techniques and visualization-based methods were provided in the realm of malicious software detection.Traditional non-visual approaches for malicious software detection, including static analysis, dynamic analysis, and hybrid techniques, were introduced.Subsequently, a comprehensive survey and evaluation of prominent contemporary visualization-based methods for detecting malicious software were undertaken.This primarily encompasses encompassed the integration of visualization with machine learning and visualization combined with deep learning, each of which exhibits distinct advantages and characteristics within the domain of malware detection and classification.Consequently, the holistic consideration of several factors, such as dataset size, computational resources, time constraints, model accuracy, and implementation complexity, is necessary for the selection of detection and classification methods.In conclusion, the challenges currently faced by detection technologies are summarized, and a forward-looking perspective on future research directions in the field is provided.…”
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Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets by Saeed Ullah, Junsheng Wu, Zhijun Lin, Mian Muhammad Kamal, Hala Mostafa, Muhammad Sheraz, Teong Chee Chuah

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Enhancing Autism Spectrum Disorder Classification with Lightweight Quantized CNNs and Federated Learning on ABIDE-1 Dataset by Simran Gupta, Md. Rahad Islam Bhuiyan, Sadia Sultana Chowa, Sidratul Montaha, Rashik Rahman, Sk. Tanzir Mehedi, Ziaur Rahman

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Optimized Ensemble Deep Learning for Real-Time Intrusion Detection on Resource-Constrained Raspberry Pi Devices by Muhammad Bisri Musthafa, Samsul Huda, Tuy Tan Nguyen, Yuta Kodera, Yasuyuki Nogami

“…The rapid growth of Internet of Things (IoT) networks has increased security risks, making it essential to have effective Intrusion Detection Systems (IDS) for real-time threat detection. …”
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SwinTCS: A Swin Transformer Approach to Compressive Sensing with Non-Local Denoising by Xiuying Li, Haoze Li, Hongwei Liao, Zhufeng Suo, Xuesong Chen, Jiameng Han

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Developing and Implementing an Artificial Intelligence (AI)-Driven System For Electricity Theft Detection by Nwamaka Georgenia Ezeji, Kingsley Ifeanyi Chibueze, Nnenna Harmony Nwobodo-Nzeribe

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CRYPTO-RESISTANT METHODS AND RANDOM NUMBER GENERATORS IN INTERNET OF THINGS (IOT) DEVICES by Petro Klimushyn, Tetiana Solianyk, Oleksandr Mozhaiev, Yurii Gnusov, Oleksandr Manzhai, Vitaliy Svitlychny

“…The analysis of technologies and circuit solutions allowed to draw the following conclusions: protection of IoT solutions includes: security of IoT network nodes and their connection to the cloud using secure protocols, ensuring confidentiality, authenticity and integrity of IoT data by cryptographic methods, attack analysis and network cryptographic stability; the initial basis for the protection of IoT solutions is the true randomness of the formed RNG sequences and used in algorithms for cryptographic transformation of information to protect it; feature of IoT devices is their heterogeneity and geographical distribution, limited computing resources and power supply, small size; The most effective (reduce power consumption and increase the generation rate) for use in IoT devices are RNG exclusively on a digital basis, which implements a three-stage process: the initial digital circuit, normalizer and random number flow generator; Autonomous Boolean networks (ABN) allow to create RNG with unique characteristics: the received numbers are really random, high speed – the number can be received in one measure, the minimum power consumption, miniature, high (up to 3 GHz) throughput of Boolean chaos; a promising area of ABN development is the use of optical logic valves for the construction of optical ABN with a bandwidth of up to 14 GHz; the classification of known classes of RNG attacks includes: direct cryptanalytic attacks, attacks based on input data, attacks based on the disclosure of the internal state of RNG, correlation attacks and special attacks; statistical test packages to evaluate RNG sequences have some limitations or shortcomings and do not replace cryptanalysis; Comparison of cryptoaccelerators with cryptographic transformation software shows their significant advantages: for AES block encryption algorithm, speeds increase by 10-20 times in 8/16-bit cryptoaccelerators and 150 times in 32-bit, growth hashing of SHA-256 in 32-bit cryptoaccelerators more than 100 times, and for the NMAS algorithm - up to 500 times. …”
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YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation by Uddin A Hasib, Raihan Md Abu, Jing Yang, Uzair Aslam Bhatti, Chin Soon Ku, Lip Yee Por

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A lightweight Xray-YOLO-Mamba model for prohibited item detection in X-ray images using selective state space models by Kai Zhao, Shufan Peng, Yujin Li, Tianliang Lu

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Advancing Corn Yield Mapping in Kenya Through Transfer Learning by Ahaan Bohra, Sophie Nottmeyer, Chenchen Ren, Shuo Chen, Yuchi Ma

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