Interdisciplinary framework for cyber-attacks and anomaly detection in industrial control systems using deep learning by Qawsar Gulzar, Khurram Mustafa

“…In this study, we introduced an interdisciplinary framework that aims to enhance network intrusion detection systems (NIDSs). In this framework, we introduced an IDS via feature selection and feature reduction technique(s) with the attention-driven lightweight deep neural networks: Deep Recurrent Neural Networks (RNN), Deep Long Short-Term Memory (LSTM), and Deep Bi-directional Long Short-Term Memory (Bi-LSTM). …”
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Steganalysis of Adaptive Multi-Rate Speech with Unknown Embedding Rates Using Multi-Scale Transformer and Multi-Task Learning Mechanism by Congcong Sun, Azizol Abdullah, Normalia Samian, Nuur Alifah Roslan

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Boosting Cyberattack Detection Using Binary Metaheuristics With Deep Learning on Cyber-Physical System Environment by Alanoud Al Mazroa, Fahad R. Albogamy, Mohamad Khairi Ishak, Samih M. Mostafa

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UAP: A System Authentication Protocol for UAV Relay Communication by UAV-Assisted by Chunpeng Liu, Tao Huang, Maode Ma

“…Furthermore, we evaluate the resilience of UAP against multiple security vulnerabilities using the Scyther tool. Finally, we compare our protocol with existing authentication methods in terms of application scenarios, security features, and both computational and communication overhead. …”
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Dual-Model Synergy for Fingerprint Spoof Detection Using VGG16 and ResNet50 by Mohamed Cheniti, Zahid Akhtar, Praveen Kumar Chandaliya

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Peculiarities of evidence in the course of investigation of criminal offences under parts 1, 2 of Article 111-1 of the Criminal Code of Ukraine by S. Ye. Ablamskyi, O. V. Kovtun, V. V. Ablamska

“…It is substantiated that in modern conditions, evidence in the course of investigation of criminal offences under Parts 1, 2 of Art. 111-1 of the Criminal Code of Ukraine is inseparable from taking into account the features of modern equipment which can be used for information transmission, analysis of computer information and information from correspondence, channels and groups in social networks containing valuable information, samples of signatures, seals and other details of documents which reflect information about the collaboration activities of individuals and groups. …”
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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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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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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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Intrusion Detection Using Hybrid Pearson Correlation and GS-PSO Optimized Random Forest Technique for RPL-Based IoT by Wei Yang, Xinlong Wang, Zhiming Zhang, Shaolong Chen, Chengqi Hou, Siwei Luo

“…The escalating surge of cyberattacks targeting IoT systems has exposed critical security vulnerabilities in RPL-based networks. Attackers exploit routing spoofing, resource depletion tactics, and topology manipulation strategies, while inherent constraints of resource-limited devices and scalability challenges in mass deployments amplify these risks. …”
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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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Exploration of machine learning approaches for automated crop disease detection by Annu Singla, Ashima Nehra, Kamaldeep Joshi, Ajit Kumar, Narendra Tuteja, Rajeev K. Varshney, Sarvajeet Singh Gill, Ritu Gill

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An Improved Machine Learning-Based Model for Detecting and Classifying PQDs with High Noise Immunity in Renewable-Integrated Microgrids by Irfan Ali Channa, Dazi Li, Mohsin Ali Koondhar, Fida Hussain Dahri, Ibrahim Mahariq

“…Recently, renewable energy sources integrated with microgrid (MG) networks have provided safe, secure, and reliable power supply to both utility and industrial purposes. …”
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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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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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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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Sylph: An Unsupervised APT Detection System Based on the Provenance Graph by Kaida Jiang, Zihan Gao, Siyu Zhang, Futai Zou

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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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Real-Time Corn Variety Recognition Using an Efficient DenXt Architecture with Lightweight Optimizations by Jin Zhao, Chengzhong Liu, Junying Han, Yuqian Zhou, Yongsheng Li, Linzhe Zhang

“…Representative Batch Normalization (RBN) is introduced into the DenseNet-121 model to improve the generalization ability of the model, and the SE module and deep separable convolution are integrated to enhance the feature representation and reduce the computational complexity, and the Dropout regularization is introduced to further improve the generalization ability of the model and reduce the overfitting. …”
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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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