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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Any Bluetooth device can be hacked. Know how? by Prathibha Muraleedhara, Mary Subaja Christo, Jaya J, Yuvasini  D

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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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Multistage Convolutional Neural Network With Deformable Attention for Word-Level Offline Text-Independent Writer Identification by Manabu Okawa

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Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks by Prabu Selvam, P. Karthikeyan, S. Manochitra, A. V. L. N. Sujith, T. Ganesan, Rajaram Ayyasamy, Mohammed Shuaib, Shadab Alam, A. Rajendran

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EfficientTransformer: A Dynamic Anomaly Detection Model for Industrial Control Networks by Jinyang Liu, Guogang Wang, Xuejun Zong, Bowei Ning, Kan He

“…As industrial control network threats become increasingly complex, traditional intrusion detection systems (IDS) struggle to capture implicit relationships due to feature redundancy and intricate feature interactions. …”
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Security Evaluation of Provably Secure ECC-Based Anonymous Authentication and Key Agreement Scheme for IoT by Kisung Park, Myeonghyun Kim, Youngho Park

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Graph neural networks embedded with domain knowledge for cyber threat intelligence entity and relationship mining by Gan Liu, Kai Lu, Saiqi Pi

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Controller Area Network (CAN) Bus Transceiver with Authentication Support and Enhanced Rail Converters by Can Hong, Weizhong Chen, Xianshan Wen, Theodore W. Manikas, Ping Gui, Mitchell A. Thornton

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An improved synergistic dual-layer feature selection algorithm with two type classifier for efficient intrusion detection in IoT environment by G Logeswari, K Thangaramya, M Selvi, J. Deepika Roselind

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Ergodic secrecy rate and outage probability in NOMA IRS massive MIMO networks with jamming by Min Zhu, Hamid Gholizadeh Fard, Bahar Hazrati, Behrooz Mosallaei, Alhussein G. Alkhayer, Kai Jin, Jingyu Zhang

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Combining Generative Adversarial Networks (GANs) With Gaussian Noise for Anomaly Detection in Internet of Things (IoT) Traffic by Roya Morshedi, S. Mojtaba Matinkhah

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Knowledge Improved Hybrid DNN–KAN Framework for Intrusion Detection in Wireless Sensor Networks by M. Sriraghavendra, Muna Elsadig, Ines Hilali Jaghdam, S. Abdel-Khalek, B. Galeebathullah, Salem Alkhalaf

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CAN-GraphiT: A Graph-Based IDS for CAN Networks Using Transformer by Md Mahbub Hasan, Sanchita Ghose, Krishna Chandra Roy

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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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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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Intrusion Detection Framework for Internet of Things with Rule Induction for Model Explanation by Kayode S. Adewole, Andreas Jacobsson, Paul Davidsson

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A deep dive into artificial intelligence with enhanced optimization-based security breach detection in internet of health things enabled smart city environment by S Jayanthi, Sodagudi Suhasini, N. Sharmili, E. Laxmi Lydia, V. Shwetha, Bibhuti Bhusan Dash, Mrinal Bachute

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Intrusion detection system based on machine learning using least square support vector machine by Pratik Waghmode, Manideep Kanumuri, Hosam El-Ocla, Tanner Boyle

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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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