AI-Based Cyber Defense for More Secure Cyberspace

Bogatinov, Dimitar; Bogdanoski, Mitko; Angelevski, Slavko

doi:10.4018/978-1-4666-8793-6.ch011

Cited by 7 publications

(1 citation statement)

References 18 publications

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“…However, for many developed countries, the risk of cyberattacks has grown due to this greater dependence on integrated information-technology systems. Cyberattacks can target the vital infrastructure components of several countries due to the increasing interconnection and utilization of technology [1].…”

Section: Introductionmentioning

confidence: 99%

Insights into Cybercrime Detection and Response: A Review of Time Factor

Taherdoost

2024

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Amidst an unprecedented period of technological progress, incorporating digital platforms into diverse domains of existence has become indispensable, fundamentally altering the operational processes of governments, businesses, and individuals. Nevertheless, the swift process of digitization has concurrently led to the emergence of cybercrime, which takes advantage of weaknesses in interconnected systems. The growing dependence of society on digital communication, commerce, and information sharing has led to the exploitation of these platforms by malicious actors for hacking, identity theft, ransomware, and phishing attacks. With the growing dependence of organizations, businesses, and individuals on digital platforms for information exchange, commerce, and communication, malicious actors have identified the susceptibilities present in these systems and have begun to exploit them. This study examines 28 research papers focusing on intrusion detection systems (IDS), and phishing detection in particular, and how quickly responses and detections in cybersecurity may be made. We investigate various approaches and quantitative measurements to comprehend the link between reaction time and detection time and emphasize the necessity of minimizing both for improved cybersecurity. The research focuses on reducing detection and reaction times, especially for phishing attempts, to improve cybersecurity. In smart grids and automobile control networks, faster attack detection is important, and machine learning can help. It also stresses the necessity to improve protocols to address increasing cyber risks while maintaining scalability, interoperability, and resilience. Although machine-learning-based techniques have the potential for detection precision and reaction speed, obstacles still need to be addressed to attain real-time capabilities and adjust to constantly changing threats. To create effective defensive mechanisms against cyberattacks, future research topics include investigating innovative methodologies, integrating real-time threat intelligence, and encouraging collaboration.

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Section: Introductionmentioning

confidence: 99%

Insights into Cybercrime Detection and Response: A Review of Time Factor

Taherdoost

2024

Information

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Cyber-Physical Systems Security Based on a Cross-Linked and Correlated Vulnerability Database

Jiang

Atif

Ding

2019

Critical Information Infrastructures Security

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Recent advances in data analytics prompt dynamic datadriven vulnerability assessments whereby data contained from vulnerabilityalert repositories as well as from Cyber-physical System (CPS) layer networks and standardised enumerations. Yet, current vulnerability assessment processes are mostly conducted manually. However, the huge volume of scanned data requires substantial information processing and analytical reasoning, which could not be satisfied considering the imprecision of manual vulnerability analysis. In this paper, we propose to employ a cross-linked and correlated database to collect, extract, filter and visualise vulnerability data across multiple existing repositories, whereby CPS vulnerability information is inferred. Based on our locally-updated database, we provide an in-depth case study on gathered CPS vulnerability data, to explore the trends of CPS vulnerability. In doing so, we aim to support a higher level of automation in vulnerability awareness and back risk-analysis exercises in critical infrastructures (CIs) protection.

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