Research on LEO Satellite Network Routing Security by Wenhao XUE, Tian PAN, Chengcheng LU, Fan YANG, Tao HUANG, Yunjie LIU

“…The design of secure mechanisms and failure recovery mechanisms for inter-satellite routing has become pivotal in maintaining dependable communication within the LEO satellite network.To address the potential security threats faced by satellite networks, the impact of different routing attack behaviors on a typical inter-satellite routing protocol was analyzed and differentiated packet security authentication mechanisms and link failure recovery mechanisms were designed.Additionally, a satellite network emulation platform based on virtualization technology was constructed, enabled the verification of the effectiveness of the designed inter-satellite routing security mechanisms through the emulation of various routing attack scenarios.Furthermore, performance metrics such as CPU utilization and packet processing time were also evaluated before and after the introduction of security mechanisms.Experimental results demonstrated that the proposed inter-satellite routing security mechanism effectively mitigated multiple security threats in the space network environment while reduced communication latency caused by sudden link failures, thereby ensured secure and reliable communication within the LEO satellite network.…”
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Cyber attack detection in IOT-WSN devices with threat intelligence using hidden and connected layer based architectures by S. Nandhini, A. Rajeswari, N. R. Shanker

“…Abstract In this paper, cyber-attacks in IOT-WSN are detected through proposed optimized-Neural Network algorithms such as (i) Equilibrium Optimizer Neural Network (EO-NN), (ii) Particle Swarm Optimization (PSO-NN), (iii) Single Candidate Optimizer Neural Network (SCO-NN) and (iv) Single Candidate Optimizer Long Short-Term Memory (SCO-LSTM) with different connecting, hidden neural network layers and threat intelligence data. …”
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A single flow detection enabled method for DDoS attacks in IoT based on traffic feature reconstruction and mapping by Lixia XIE, Bingdi YUAN, Hongyu YANG, Ze HU, Xiang CHENG, Liang ZHANG

“…To address the slow response time of existing detection modules to Internet of things (IoT) distributed denial of service (DDoS) attacks, their low feature differentiation, and poor detection performance, a single flow detection enabled method based on traffic feature reconstruction and mapping (SFDTFRM) was proposed.Firstly, SFDTFRM employed a queue to store previously arrived flow based on the first in, first out rule.Secondly, to address the issue of similarity between normal communication traffic of IoT devices and DDoS attack traffic, a multidimensional reconstruction neural network model more lightweight compared to the baseline model and a function mapping method were proposed.The modified model loss function was utilized to reconstruct the quantitative feature matrix of the queue according to the corresponding index, and transformed into a mapping feature matrix through the function mapping method, enhancing the differences between different types of traffic, including normal communication traffic of IoT devices and DDoS attack traffic.Finally, the frequency information was extracted using a text convolutional network and information entropy calculation and the machine learning classifier was employed for DDoS attack traffic detection.The experimental results on two benchmark datasets show that SFDTFRM can effectively detect different DDoS attacks, and the average metrics value of SFDTFRM is a maximum of 12.01% higher than other existing methods.…”
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Gradual poisoning of a chest x-ray convolutional neural network with an adversarial attack and AI explainability methods by Stephen B. Lee

“…To improve generalizability, we create a generic ResNet pneumonia classification model and then use it as an example by subjecting it to BadNets adversarial attacks. The study uses various poisoned datasets of different compositions (2%, 16.7% and 100% ratios of poisoned data) and two different test sets (a normal set of test data and one that contained poisoned images) to study the effects of BadNets. …”
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On the use of information theory metrics for detecting DDoS attacks and flash events: an empirical analysis, comparison, and future directions by Jagdeep Singh, Navjot Jyoti, Sunny Behal

“…It has observed that the generalized divergence based metrics produce more accuracy in detecting different types of attack flows in contrast to entropy metrics.…”
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Practical Preimage Attacks on 3-Round Keccak-256 and 4-Round Keccak[r=640, c=160] by Xiaoen Lin, Le He, Hongbo Yu

“… Recently, linear structures and algebraic attacks have been widely used in preimage attacks on round-reduced Keccak. …”
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Synthetic EMG Based on Adversarial Style Transfer Can Effectively Attack Biometric-Based Personal Identification Models by Peiqi Kang, Shuo Jiang, Peter B. Shull

“…Therefore, it is necessary to extract the style with the leaked personal signals and generate the attack signals with different contents. With our proposed method and tiny leaked personal EMG fragments, numerous EMG signals with different content can be generated in that person’s style. …”
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Pilot Contamination Attack Detection Methods—An Exhaustive Performance Evaluation Through Probability Metrics and Statistical Classification Parameters by Dimitriya Mihaylova, Georgi Iliev, Zlatka Valkova-Jarvis, Viktor Stoynov

“…Depending on the particular application of the system in which the PCA detection methods are incorporated and the distribution of attack initiation among all samples, different classification parameters are of varying significance in the efficiency assessment. …”
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Design of Cyberwar Laboratory Exercises to Implement Common Security Attacks against IEEE 802.11 Wireless Networks by Mina Malekzadeh, Abdul Azim Abdul Ghani, Shamala Subramaniam

“…This aspect makes the wireless networks vulnerable to various types of attacks. A true understanding of these attacks provides better ability to defend the network against the attacks, thus eliminating potential threats from the wireless systems. …”
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Optical Coherence Tomography-Based Fingerprint Presentation Attack Detection via Multi-Tissue Features Contribution Analysis by Zhanqing Li, Jie Ji, Jiajin Qi, Haonan Fang, Yipeng Liu

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Renewable Energy Interconnected Power System Load Frequency Control Under Network Attacks and Communication Time Delay by MI Yang, YANG Zishuai, XU Mingming, ZHOU Jie, HAN Yunhao

“…Second， an improved periodic event-triggering mechanism was proposed based on the traditional sliding mode control， which determined the sampling state based on the divided time interval to reduce the amount of data exchange in the network and solve periodic DoS attacks. Finally， simulation experiments were conducted under different scenarios using MATLAB/Simulink to demonstrate that the proposed strategy can effectively overcome the problem of increased frequency deviations caused by network attacks and communication delays. …”
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Attack-Defense Game between Malicious Programs and Energy-Harvesting Wireless Sensor Networks Based on Epidemic Modeling by Guiyun Liu, Baihao Peng, Xiaojing Zhong, Lefeng Cheng, Zhifu Li

“…As energy-harvesting wireless sensor networks (EHWSNs) are increasingly integrated with all walks of life, their security problems have gradually become hot issues. As an attack means, malicious programs often attack sensor nodes in critical locations in the networks to cause paralysis and information leakage of the networks, resulting in security risks. …”
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Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification by Dharani Kanta Roy, Hemanta Kumar Kalita

“…The proposed work introduces an intelligent semi-supervised intrusion detection system based on different algorithms to classify the network attacks accurately. …”
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DeepTransIDS: Transformer-Based Deep learning Model for Detecting DDoS Attacks on 5G NIDD by Kumar Harshdeep, Konatham Sumalatha, Rohit Mathur

“…The confusion matrix analysis also confirms the model's ability to accurately identify intricate attack patterns even in the class of imbalance conditions. …”
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A multi-factor integration-based semi-supervised learning for address resolution protocol attack detection in SDIIoT by Zhong Li, Huimin Zhuang

“…MIS considers the address resolution protocol attack features from different aspects to help the model make correct judgment. …”
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A multilayer deep autoencoder approach for cross layer IoT attack detection using deep learning algorithms by K. Saranya, A. Valarmathi

“…A new paradigm for cross-layer based IoT attack detection is presented in this paper, which provides a flexible and robust solution for complete cybersecurity across different IoT domains and thereby improves the field of cyber threat identification. …”
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Development of 3D satisfaction surface for concrete durability design under chloride attack considering climate change by Yeongmo Yeon, Jang-Ho Jay Kim

“…The service life of the target structure against chloride attack was evaluated using the proposed 3D PBE. The result show that the durability life of the structure decreased as the relative humidity decreased and the temperature increased during curing. …”
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An Efficient Mutual Authentication and Fractional Lyrebird Optimization With Deep Learning–Based SIP-Based DRDoS Attack Detection by V. Sreenivasulu, C. V. Ravikumar

“…In this article, a deep learning model fractional lyrebird optimization algorithm–deep stack autoencoder (FLOA-DSA) is developed for the detection of SIP-based distributed reflection denial-of-service (DRDoS) attacks in SIP network. Here, the authentication process is carried out under different phases based on various mathematical model functionalities. …”
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TOP: A Combined Logical and Physical Obfuscation Method for Securing Networks-on-Chip Against Reverse Engineering Attacks by Mona Hashemi, Siamak Mohammadi, Trevor E. Carlson

“…Second, our distributed approach specifically addresses timing-based attacks by introducing timing violations and triggering malfunctions upon applying wrong key. …”
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DDoS Attack Detection in IoT: A Comparative Resource and Performance Analysis of Deep Learning and Machine Learning Models by Amer Abualhassan, Irfan Rashid, Farid Binbeshr, Muhammad Imam

“…The proliferation of Internet of Things (IoT) devices has increased susceptibility to Distributed Denial of Service (DDoS) attacks, exposing the limitations of traditional security mechanisms such as firewalls and rule-based intrusion detection systems (IDS), which often fail due to static rules and limited adaptability in resource-constrained environments. …”
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