A Risk Assessment Analysis to Enhance the Security of OT WAN with SD-WAN by Van Joshua Abergos, Faiza Medjek

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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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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

“…A Feature Pyramid Network (FPN) facilitates the fusion of scale features capturing intricate details as well, as broader context. …”
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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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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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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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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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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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Facial Feature Recognition with Multi-task Learning and Attention-based Enhancements by M. Rohani, H. Farsi, S. Mohamadzadeh

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Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks by Muawia A. Elsadig, Abdelrahman Altigani, Yasir Mohamed, Abdul Hakim Mohamed, Akbar Kannan, Mohamed Bashir, Mousab A. E. Adiel

“…Vehicular ad hoc networks (VANETs) aim to manage traffic, prevent accidents, and regulate various parts of traffic. …”
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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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Bi directional sparse attention recurrent autoencoder based intrusion detection for VANET security with tuna swarm optimization by R. Gopi, V. Thiruppathy Kesavan, Md. Jakir Hossen, Nor Hidayati Binti Abdul Aziz

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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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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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A lightweight verifiable outsourced decryption of attribute-based encryption scheme for blockchain-enabled wireless body area network in fog computing by Rui Guo, Chaoyuan Zhuang, Huixian Shi, Yinghui Zhang, Dong Zheng

“…The proposal provides the verifiability of ciphertext that ensures the user to check the correctness efficiently. (2) The size of the ciphertext is constant that is not increased with the complexity of attribute and access structure. (3) For Internet of Things devices, it introduces the fog computing into our protocol for the purpose of low latency and relation interactions, which has virtually saved the bandwidth. (4) With the help of blockchain technique, we encapsulate the hash value of public parameter, original and transformed ciphertext and transformed key into a block, so that the tamper-resistance is facilitated against an adversary from inside and outside the system. (5) In the standard model, we prove that it is selectively chosen-plaintext attack-secure and verifiable provided that the computational bilinear Diffie–Hellman assumption holds. (6) It implements this protocol and shows the result of performance measurement, which indicates a significant reduction on communication and computation costs burden on every entity in wireless body area network.…”
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Attack resilient IoT security framework using multi head attention based representation learning with improved white shark optimization algorithm by Jawhara Aljabri

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