Automating Network Security Analysis at Packet-level by using Rule-based Engine

Holkovič, Martin; Ryšavý, Ondřej; Dudek, Jindřich

doi:10.1145/3352700.3352714

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…To defend smart devices in IoT environments Classifying different denial-of-service attacks in cloud computing An automating network security analysis at packet-level A rule-based engine for security alert correlation A belief rule-based anomaly detection Nespoli et al 101 Khorshed et al 102 Holkovivc et al 103 Kenaza et al 104 Ul et al 106 Fuzzy Logic-based Approach…”

Section: Rule-based Modeling and Decision-makingmentioning

confidence: 99%

“…Using rule‐based learning, Khorshed et al 103 propose a method for classifying various denial‐of‐service threats in cloud computing. Holkovivc et al 104 offer a rule‐based engine that automates network security analysis at the packet level. Kenaza et al 105 offer a rule‐based alert correlation engine in which ontology provides a full environment to represent information for intrusion detection.…”

Section: Ai‐based Modeling In Cybersecuritymentioning

confidence: 99%

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Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview

Sarker

2023

Security and Privacy

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Due to the rising dependency on digital technology, cybersecurity has emerged as a more prominent field of research and application that typically focuses on securing devices, networks, systems, data and other resources from various cyber-attacks, threats, risks, damages, or unauthorized access. Artificial Intelligence (AI), also referred to as a crucial technology of the current Fourth Industrial Revolution (Industry 4.0 or 4IR), could be the key to intelligently dealing with these cyber issues. Various forms of AI methodologies, such as analytical, functional, interactive, textual as well as visual AI can be employed to get the desired cyber solutions according to their computational capabilities. However, the dynamic nature and complexity of real-world situations and data gathered from various cyber sources make it challenging nowadays to build an effective AI-based security model. Moreover, defending robustly against adversarial attacks is still an open question in the area. In this paper, we provide a comprehensive view on "Cybersecurity Intelligence and Robustness", emphasizing multi-aspects AI-based modeling and adversarial learning that could lead to addressing diverse issues in various cyber applications areas such as detecting malware or intrusions, zero-day attacks, phishing, data breach, cyberbullying and other cybercrimes. Thus the eventual security modeling process could be automated, intelligent, and robust compared to traditional security systems. We also emphasize and draw attention to the future aspects of cybersecurity intelligence and robustness along with the research direction within the context of our study. Overall, our goal is not only to explore AI-based modeling and pertinent methodologies but also to focus on the resulting model's applicability for securing our digital systems and society.

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