Predicting correlation relationships of entities between attack patterns and techniques based on word embedding and graph convolutional network by Weicheng QIU, Xiuzhen CHEN, Yinghua MA, Jin MA, Zhihong ZHOU

“…Threat analysis relies on knowledge bases that contain a large number of security entities.The scope and impact of security threats and risks are evaluated by modeling threat sources, attack capabilities, attack motivations, and threat paths, taking into consideration the vulnerability of assets in the system and the security measures implemented.However, the lack of entity relations between these knowledge bases hinders the security event tracking and attack path generation.To complement entity relations between CAPEC and ATT＆amp;CK techniques and enrich threat paths, an entity correlation prediction method called WGS was proposed, in which entity descriptions were analyzed based on word embedding and a graph convolution network.A Word2Vec model was trained in the proposed method for security domain to extract domain-specific semantic features and a GCN model to capture the co-occurrence between words and sentences in entity descriptions.The relationship between entities was predicted by a Siamese network that combines these two features.The inclusion of external semantic information helped address the few-shot learning problem caused by limited entity relations in the existing knowledge base.Additionally, dynamic negative sampling and regularization was applied in model training.Experiments conducted on CAPEC and ATT＆amp;CK database provided by MITRE demonstrate that WGS effectively separates related entity pairs from irrelevant ones in the sample space and accurately predicts new entity relations.The proposed method achieves higher prediction accuracy in few-shot learning and requires shorter training time and less computing resources compared to the Bert-based text similarity prediction models.It proves that word embedding and graph convolutional network based entity relation prediction method can extract new entity correlation relationships between attack patterns and techniques.This helps to abstract attack techniques and tactics from low-level vulnerabilities and weaknesses in security threat analysis.…”
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Augmenting cybersecurity through attention based stacked autoencoder with optimization algorithm for detection and mitigation of attacks on IoT assisted networks by Kashi Sai Prasad, E. Laxmi Lydia, M. V. Rajesh, K. Radhika, Janjhyam Venkata Naga Ramesh, N. Neelima, Srinivasa Rao Pokuri

“…The IoT network’s security can progressively become a critical concern as IoT technology obtains extensive use. …”
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Zero-day exploits detection with adaptive WavePCA-Autoencoder (AWPA) adaptive hybrid exploit detection network (AHEDNet) by Ahmed A. Mohamed, Abdullah Al-Saleh, Sunil Kumar Sharma, Ghanshyam G. Tejani

“…Additionally, a novel “Meta-Attention Transformer Autoencoder (MATA)” for enhancing feature extraction which address the subtlety issue, and improves the model’s ability and flexibility to detect new security threats, and a novel “Genetic Mongoose-Chameleon Optimization (GMCO)” was introduced for effective feature selection in the case of addressing the efficiency challenges. …”
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A novel integration of multi-stocked gated variant recurrent units and Kolmogorov-Arnold tuned deep training networks for anchoring the intrusion detection against computer attacks by M. Saritha, Saidireddy Malgireddy

“…Abstract With the explosive expansion of smart computers, there is a rapid intrusion of network attacks in the user’s personal life that intensifies the privacy and security breaches. …”
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A Novel Self-Attention-Enabled Weighted Ensemble-Based Convolutional Neural Network Framework for Distributed Denial of Service Attack Classification by Shravan Venkatraman, S. Kanthimathi, K. S. Jayasankar, T. Pranay Jiljith, R. Jashwanth

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USING SUPPORT VECTORS TO BUILD A RULE-BASED SYSTEM FOR DETECTING MALICIOUS PROCESSES IN AN ORGANISATION'S NETWORK TRAFFIC by Halyna Haidur, Sergii Gakhov, Dmytro Hamza

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Pseudo-Random Identification and Efficient Privacy-Preserving V2X Communication for IoV Networks by Sujash Naskar, Gerhard Hancke, Tingting Zhang, Mikael Gidlund

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Information Security and Artificial Intelligence–Assisted Diagnosis in an Internet of Medical Thing System (IoMTS) by Pi-Yun Chen, Yu-Cheng Cheng, Zi-Heng Zhong, Feng-Zhou Zhang, Neng-Sheng Pai, Chien-Ming Li, Chia-Hung Lin

“…The internet of medical thing system (IoMTS) comprises the fifth-generation (5G) networking technology that collects and shares digital data from signal- or image-capturing devices through computer and wireless communication networks. …”
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A Consistency-Aware Hybrid Static–Dynamic Multivariate Network for Forecasting Industrial Key Performance Indicators by Jiahui Long, Xiang Jia, Bingyi Li, Lin Zhu, Miao Wang

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A Multi-Class Intrusion Detection System for DDoS Attacks in IoT Networks Using Deep Learning and Transformers by Sheikh Abdul Wahab, Saira Sultana, Noshina Tariq, Maleeha Mujahid, Javed Ali Khan, Alexios Mylonas

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Post-disaster building damage assessment based on gated adaptive multi-scale spatial-frequency fusion network by Bo Yu, Yao Sun, Jiansong Hu, Fang Chen, Lei Wang

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Federated Deep Learning for Scalable and Privacy-Preserving Distributed Denial-of-Service Attack Detection in Internet of Things Networks by Abdulrahman A. Alshdadi, Abdulwahab Ali Almazroi, Nasir Ayub, Miltiadis D. Lytras, Eesa Alsolami, Faisal S. Alsubaei, Riad Alharbey

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A Scalable Hybrid Autoencoder–Extreme Learning Machine Framework for Adaptive Intrusion Detection in High-Dimensional Networks by Anubhav Kumar, Rajamani Radhakrishnan, Mani Sumithra, Prabu Kaliyaperumal, Balamurugan Balusamy, Francesco Benedetto

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Smart Grid Security: Proactive Prediction of Advanced Persistent Threats by Motahareh Dehghan, Erfan Khosravain

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Making a Real-Time IoT Network Intrusion-Detection System (INIDS) Using a Realistic BoT–IoT Dataset with Multiple Machine-Learning Classifiers by Jawad Ashraf, Ghulam Musa Raza, Byung-Seo Kim, Abdul Wahid, Hye-Young Kim

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Evaluating the Performance of Machine Learning Classifiers for Network Intrusion Detection: A Comparative Study Using the UNSW-NB15 Dataset by Iwan Handoyo Putro

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A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection by Hessa Alfraihi, Muhammad Swaileh A. Alzaidi, Hamed Alqahtani, Abdulbasit A. Darem, Ali M. Al-Sharafi, Ahmad A. Alzahrani, Menwa Alshammeri, Abdulwhab Alkharashi

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A speech-based convolutional neural network for human body posture classification by Abdulmohsin Husam Ali

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Legal aspects of functional security standardisation of the Internet of Things by P. S. Klimushyn, V. Ye. Roh, T. P. Kolisnyk

“…The single phenomenon of IoT security has been identified as a complex concept that includes functional security and information security with their interconnection, contradictions, challenges and risks. …”
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FD-IDS: Federated Learning with Knowledge Distillation for Intrusion Detection in Non-IID IoT Environments by Haonan Peng, Chunming Wu, Yanfeng Xiao

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