Weibo Rumor Recognition Based on Communication and Stacking Ensemble Learning by Yu Wu, Yan Zeng, Jie Yang, Zhenni Zhao

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Reversible data hiding scheme based on pixel-value differencing in dual images by Pyung-Han Kim, Kwan-Woo Ryu, Ki-Hyun Jung

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A Torus-Chaotic System and Its Pseudorandom Properties by Jizhao Liu, Xiangzi Zhang, Qingchun Zhao, Jing Lian, Fangjun Huang, Yide Ma

“…Furthermore, the pseudorandom properties of output sequence are well studied and a random number generator algorithm is proposed, which has the potential of being used in several cyber security systems such as the verification code, secure QR code, and some secure communication protocols.…”
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Review of malware detection and classification visualization techniques by Jinwei WANG, Zhengjia CHEN, Xue XIE, Xiangyang LUO, Bin MA

“…With the rapid advancement of technology, network security faces a significant challenge due to the proliferation of malicious software and its variants.These malicious software use various technical tactics to deceive or bypass traditional detection methods, rendering conventional non-visual detection techniques inadequate.In recent years, data visualization has gained considerable attention in the academic community as a powerful approach for detecting and classifying malicious software.By visually representing the key features of malicious software, these methods greatly enhance the accuracy of malware detection and classification, opening up extensive research opportunities in the field of cyber security.An overview of traditional non-visual detection techniques and visualization-based methods were provided in the realm of malicious software detection.Traditional non-visual approaches for malicious software detection, including static analysis, dynamic analysis, and hybrid techniques, were introduced.Subsequently, a comprehensive survey and evaluation of prominent contemporary visualization-based methods for detecting malicious software were undertaken.This primarily encompasses encompassed the integration of visualization with machine learning and visualization combined with deep learning, each of which exhibits distinct advantages and characteristics within the domain of malware detection and classification.Consequently, the holistic consideration of several factors, such as dataset size, computational resources, time constraints, model accuracy, and implementation complexity, is necessary for the selection of detection and classification methods.In conclusion, the challenges currently faced by detection technologies are summarized, and a forward-looking perspective on future research directions in the field is provided.…”
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Survey on explainable knowledge graph reasoning methods by Yi XIA, Mingjng LAN, Xiaohui CHEN, Junyong LUO, Gang ZHOU, Peng HE

“…In recent years, deep learning models have achieved remarkable progress in the prediction and classification tasks of artificial intelligence systems.However, most of the current deep learning models are black box, which means it is not conducive to human cognitive reasoning process.Meanwhile, with the continuous breakthroughs of artificial intelligence in the researches and applications, high-performance complex algorithms, models and systems generally lack the transparency and interpretability of decision making.This makes it difficult to apply the technologies in a wide range of fields requiring strict interpretability, such as national defense, medical care and cyber security.Therefore, the interpretability of artificial intelligence should be integrated into these algorithms and systems in the process of knowledge reasoning.By means of carrying out explicit explainable intelligence reasoning based on discrete symbolic representation and combining technologies in different fields, a behavior explanation mechanism can be formed which is an important way for artificial intelligence to realize data perception to intelligence perception.A comprehensive review of explainable knowledge graph reasoning was given.The concepts of explainable artificial intelligence and knowledge reasoning were introduced briefly.The latest research progress of explainable knowledge graph reasoning methods based on the three paradigms of artificial intelligence was introduced.Specifically, the ideas and improvement process of the algorithms in different scenarios of explainable knowledge graph reasoning were explained in detail.Moreover, the future research direction and the prospect of explainable knowledge graph reasoning were discussed.…”
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A Novel Flip-Filtered Orthagonal Frequency Division Multiplexing-Based Visible Light Communication System: Peak-to-Average-Power Ratio Assessment and System Performance Improvement by Hayder S. R. Hujijo, Muhammad Ilyas

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Enhancing cybersecurity incident response: AI-driven optimization for strengthened advanced persistent threat detection by Gauhar Ali, Sajid Shah, Mohammed ElAffendi

“…The Cyber Security Incident Response Team executes its critical duties in the Centralized Security Operation Centers. …”
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Quantization Effects on Zero-Dynamics Attacks to Closed-Loop Sampled-Data Control Systems by Xile Kang, Hideaki Ishii

“…This paper focuses on cyber-security issues of networked control systems in closed-loop forms from the perspective of quantized sampled-data systems. …”
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Evaluating network test scenarios for network simulators systems by Anis Zarrad, Izzat Alsmadi

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Automotive ethernet controller authentication method based on national cryptographic algorithms by Hui GUO, Yong LUO, Xiaolu GUO

“…With the development of intelligent and connected cars, cyber security of automotive ethernet becomes the focus of automotive industry.Authentication is one of the key technologies for automotive ethernet security.The existing authentication methods have shortcomings to meet the requirements of both safety and electronic controllers’ performance.Besides, national cryptographic algorithms are becoming a trend for automotive network security, while the related research in automotive ethernet area is still in starting phase.In order to balance the limited computing performance of electronic controllers and high security requirements of automotive ethernet, a two-stage trust chain authentication method based on national cryptographic algorithms was proposed.The method can be used in different automotive ethernet topologies.A trust chain was built up based on authentication credentials, which linked the two authentication stages, namely the initial stage and the vehicle using stage.The initial stage was triggered by diagnosis instrument at end of line or controller replacement.It deployed complete authentication based on SM2 algorithm and certificates in the initial stage.The vehicle using stage was triggered by ethernet gateway at vehicle power-on or network wakeup.In this stage, the method deployed fast authentication based on SM4 algorithm and authentication credential trust chain.The authentication credentials for trust chain were generated by credential functions.In order to ensure security, each credential was generated based on last successful authentication parameters dynamically.The test results on automotive ethernet controllers show that the method has lower computing cost and higher security level, and it can guarantee both performance and security.…”
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A Bayesian network model integrating data and expert insights for fishing ship risk assessment by Sang-A Park, Deuk-Jin Park, Jeong-Bin Yim, Hyung-ju Kim

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Explainable Artificial Intelligence (XAI) to Enhance Trust Management in Intrusion Detection Systems Using Decision Tree Model by Basim Mahbooba, Mohan Timilsina, Radhya Sahal, Martin Serrano

“…Despite the growing popularity of machine learning models in the cyber-security applications (e.g., an intrusion detection system (IDS)), most of these models are perceived as a black-box. …”
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Prediction method of 0day attack path based on cyber defense knowledge graph by Cheng SUN, Hao HU, Yingjie YANG, Hongqi ZHANG

“…To solve the difficulty of attack detection caused by the 0day vulnerability, a prediction method of 0day attack path based on cyber defense knowledge graph was proposed.The cyber defense knowledge graph was constructed to refine the discrete security data such as threat, vulnerability and asset into the complete and high-related knowledge format by extracting concepts and entities related to network attack from cyber security ontology research finds and databases.Based on the knowledge integrated by the knowledge graph, assumed and restricted the unknown attributes such as the existence, availability and harmfulness of 0day vulnerabilities, and model the concept of ＆quot;attack＆quot; as a relationship between attacker entities and device entities in the knowledge graph to transform the attack prediction to the link prediction of knowledge graph.According to this, apply path ranking algorithm was applied to mine the potential 0day attack in the target system and construct the 0day attack graph.Predicted the 0day attack path by utilizing the scores output by classifiers as the occurrence probabilities of single step attack and computing the occurrence probabilities of different attack paths.The experimental result shows that with the help of complete knowledge system provided by knowledge graph, the proposed method can reduce the dependence of prediction analysis on expert model and overcome the bad influence of 0day vulnerability to improve the accuracy of 0day attack prediction.And utilizing the characteristic that path ranking algorithm reasons based on the structure of graph can also help to backtrack the reasons of predicting results so as to improve the explainability of predicting.…”
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AI-based model for securing cognitive IoT devices in advance communication systems by Akshat Gaurav, Varsha Arya, Kwok Tai Chui, Brij B. Gupta

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A Resilience-Based Security Assessment Approach for CBTC Systems by Ruiming Lu, Huiyu Dong, Hongwei Wang, Dongliang Cui, Li Zhu, Xi Wang

“…The CBTC system ensures safe and efficient operation of trains, so the quantitative assessment of cyber security is quite necessary. In this paper, a resilience-based assessment method is proposed to analyze the security level of CBTC systems based on indicators of both the cyber domain and the physical domain. …”
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Hybrid Botnet Detection Based on Host and Network Analysis by Suzan Almutairi, Saoucene Mahfoudh, Sultan Almutairi, Jalal S. Alowibdi

“…Botnet is one of the most dangerous cyber-security issues. The botnet infects unprotected machines and keeps track of the communication with the command and control server to send and receive malicious commands. …”
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Quantum algorithms for enhanced educational technologies by Basil Hanafi, Mohammad Ali, Devyaani Singh

“…Also, quantum cryptography provides reliable security and prevention against future attacks on cyber security hence providing safe means of transferring educational data. …”
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INTERNATIONAL CRIMINAL POLICE ORGANISATION STRATEGIES AND THE NATIONAL EFFORT IN THE FIGHT AGAINST CYBERCRIME IN NIGERIA by AUDU MOMOH AMINU, YAHAYA ABDULLAHI ADADU, MUHAMMED BELLO BABAN’UMMA, LUKA RUTH CALEB, MICHAEL MASHAL JEMLAK

“…It therefore recommends increased investment on training and capacity building of Law enforcement agencies and Cyber security stakeholders in Nigeria as well as synergy and increased utilization of Interpol platforms/tools for winning the war against Cybercrime in Nigeria. …”
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Transformation of business under the influence of artificial intelligence by Buha Vesna, Lečić Rada, Berezljev Ljiljana

“…Research results of Forbes Advisor (2023) indicate that the most common use of AI is in Customer Service, Cyber Security, Digital Personal Assistants, Customer Relationship Management (CRM), etc. …”
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Development of the national cybersecurity system of Ukraine as a necessary element of information society development by S. H. Petrov

“…Attention is drawn to the unconditional fulfillment of the tasks of the new Cyber Security Strategy of Ukraine, especially those related to cyber protection of critical infrastructure. …”
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