Embedding Trust in the Media Access Control Protocol for Wireless Networks by Chaminda Alocious, Hannan Xiao, Bruce Christianson, Joseph Spring

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SA3C-ID: a novel network intrusion detection model using feature selection and adversarial training by Wanwei Huang, Haobin Tian, Lei Wang, Sunan Wang, Kun Wang, Songze Li

“…With the continuous proliferation of emerging technologies such as cloud computing, 5G networks, and the Internet of Things, the field of cybersecurity is facing an increasing number of complex challenges. …”
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XAI-XGBoost: an innovative explainable intrusion detection approach for securing internet of medical things systems by Yousif Hosain, Muhammet Çakmak

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A Hybrid Deep Learning Framework for Deepfake Detection Using Temporal and Spatial Features by Fazeel Zafar, Talha Ahmed Khan, Salas Akbar, Muhammad Talha Ubaid, Sameena Javaid, Kushsairy Abdul Kadir

“…The rise of deep-fake technology has sparked concerns as it blurs the distinction between fake media by harnessing Generative Adversarial Networks (GANs). This has raised issues surrounding privacy and security in the realm. …”
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LLM-Based Cyberattack Detection Using Network Flow Statistics by Leopoldo Gutiérrez-Galeano, Juan-José Domínguez-Jiménez, Jörg Schäfer, Inmaculada Medina-Bulo

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Explainable AI for zero-day attack detection in IoT networks using attention fusion model by Deepa Krishnan, Swapnil Singh, Vijayan Sugumaran

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Optimizing Cervical Cancer Diagnosis with Feature Selection and Deep Learning by Łukasz Jeleń, Izabela Stankiewicz-Antosz, Maria Chosia, Michał Jeleń

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An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal Features by Ziheng Wang, Miao Ye, Jin Cheng, Cheng Zhu, Yong Wang

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Wireless Security Threats by Umair Jilani, Muhammad Umar Khan, Adnan Afroz, Khawaja Masood Ahmed

“…These entire devices store large amount of data and their wireless connection to network spectrum exhibit them as important source of computing. …”
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A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN by Van Joshua Abergos, Faiza Medjek

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Combining Long-Term Recurrent Convolutional and Graph Convolutional Networks to Detect Phishing Sites Using URL and HTML by Subhash Ariyadasa, Shantha Fernando, Subha Fernando

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Approach of detecting low-rate DoS attack based on combined features by Zhi-jun WU, Jing-an ZHANG, Meng YUE, Cai-feng ZHANG

“…LDoS (low-rate denial of service) attack is a kind of RoQ (reduction of quality) attack which has the characteristics of low average rate and strong concealment.These characteristics pose great threats to the security of cloud computing platform and big data center.Based on network traffic analysis,three intrinsic characteristics of LDoS attack flow were extracted to be a set of input to BP neural network,which is a classifier for LDoS attack detection.Hence,an approach of detecting LDoS attacks was proposed based on novel combined feature value.The proposed approach can speedily and accurately model the LDoS attack flows by the efficient self-organizing learning process of BP neural network,in which a proper decision-making indicator is set to detect LDoS attack in accuracy at the end of output.The proposed detection approach was tested in NS2 platform and verified in test-bed network environment by using the Linux TCP-kernel source code,which is a widely accepted LDoS attack generation tool.The detection probability derived from hypothesis testing is 96.68%.Compared with available researches,analysis results show that the performance of combined features detection is better than that of single feature,and has high computational efficiency.…”
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On‐Chip Metamaterial‐Enhanced Mid‐Infrared Photodetectors with Built‐In Encryption Features by Shicong Hou, Li Han, Shi Zhang, Libo Zhang, Kaixuan Zhang, Kening Xiao, Yao Yang, Yunduo Zhang, Yuanfeng Wen, Wenqi Mo, Yiran Tan, Yifan Yao, Jiale He, Weiwei Tang, Xuguang Guo, Yiming Zhu, Xiaoshuang Chen

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Enhanced anomaly traffic detection framework using BiGAN and contrastive learning by Haoran Yu, Wenchuan Yang, Baojiang Cui, Runqi Sui, Xuedong Wu

“…Abstract Abnormal traffic detection is a crucial topic in the field of network security. However, existing methods face many challenges when processing complex high-dimensional traffic data. …”
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Securing the 6G–IoT Environment: A Framework for Enhancing Transparency in Artificial Intelligence Decision-Making Through Explainable Artificial Intelligence by Navneet Kaur, Lav Gupta

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A secure and energy-efficient routing using coupled ensemble selection approach and optimal type-2 fuzzy logic in WSN by S. Ambareesh, Pundalik Chavan, S. Supreeth, Rajesh Nandalike, P. Dayananda, S. Rohith

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Smart framework for industrial IoT and cloud computing network intrusion detection using a ConvLSTM-based deep learning model by Ala' Abdulmajid Eshmawi, Asma Aldrees, Raed Alharthi

“…In the rapidly evolving landscape of the Industrial Internet of Things (IIoT) and cloud computing, ensuring robust network security has become a major challenge for the Internet of Everything (IoE). …”
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Spatial attention-guided pre-trained networks for accurate identification of crop diseases by Satrughan Kumar, S. S. Aravinth, Sreedhar Kollem, Munish Kumar, Quadri Noorulhasan Naveed, Azath Mubarakali, Abhishek Bhattacherjee, Addisu Frinjo Emma

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A Cross-Mamba Interaction Network for UAV-to-Satallite Geolocalization by Lingyun Tian, Qiang Shen, Yang Gao, Simiao Wang, Yunan Liu, Zilong Deng

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Optimizing Intrusion Detection in IoMT Networks Through Interpretable and Cost-Aware Machine Learning by Abdelatif Hafid, Mohamed Rahouti, Mohammed Aledhari

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