The Role of Machine Learning and Artificial Intelligence in Detecting the Malicious Use of Cyber Space

Panem, Charanarur; Gundu, Srinivasa Rao; Vijaylaxmi, J.

doi:10.1002/9781394166954.ch2

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Artificial Intelligence (AI) and Machine Learning (ML) technologies are being leveraged both by security professionals and malicious actors. Threats include adversarial attacks that manipulate AI models, automated spear-phishing campaigns, and the use of AI-based tools for reconnaissance and attack automation [13,49,50].…”

Section: Figure 1 Classifications Of Cyber-attacks [44]mentioning

confidence: 99%

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

Ugochukwu Ikechukwu Okoli,

Ogugua Chimezie Obi,

Adebunmi Okechukwu Adewusi

et al. 2024

World J. Adv. Res. Rev.

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The cybersecurity concerns get increasingly intricate as the digital world progresses. In light of the increasing complexity of cyber threats, it is imperative to develop and implement advanced and flexible security strategies. Machine Learning (ML) has become a potent tool in strengthening cybersecurity, providing the capacity to scrutinise extensive information, recognise trends, and improve threat detection and defence methods. This paper examines the significance of ML in the field of cybersecurity, with a special emphasis on the identification of threats and the implementation of protective measures. By incorporating ML algorithms into cybersecurity frameworks, organisations may automate decision-making processes, facilitating prompt responses to ever-changing threats. The initial segment explores the terrain of cyber threats, highlighting the necessity for dynamic and aggressive security methods. Conventional solutions that rely on signatures are frequently inadequate when it comes to handling sophisticated, shape-shifting attacks. ML algorithms, in contrast, have exceptional proficiency in identifying nuanced patterns and irregularities within extensive datasets, therefore offering a more efficient method of detecting potential threats. The second section delves into several ML methodologies utilised in cybersecurity, including supervised and unsupervised learning, deep learning, and reinforcement learning. Every approach is assessed based on its suitability for threat detection, demonstrating its advantages and constraints. Furthermore, the relevance of feature engineering and data pretreatment in improving machine learning models for cybersecurity applications. The versatility of ML algorithms allows them to grow with emerging threats, making them a useful tool in the ever-changing arena of cyber warfare. The final segment focuses on real-world applications of machine learning in cybersecurity, presenting successful use cases across sectors. From anomaly detection to behavior analysis, ML algorithms contribute to the discovery of dangerous activity, lowering false positives and strengthening the overall security posture. Lastly, the paper covers the obstacles and ethical issues related to the adoption of ML in cybersecurity. Issues like as adversarial assaults, skewed datasets, and the interpretability of ML models are examined, highlighting the necessity for a holistic strategy that integrates modern technology with ethical considerations. The fusion of human expertise and machine intelligence offers a formidable defense against evolving cyber threats, paving the way for a more resilient and secure digital future.

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Section: Figure 1 Classifications Of Cyber-attacks [44]mentioning

confidence: 99%

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

Ugochukwu Ikechukwu Okoli,

Ogugua Chimezie Obi,

Adebunmi Okechukwu Adewusi

et al. 2024

World J. Adv. Res. Rev.

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“…Crawler (Khan et al, 2023) for Ontology Extracting DarkWeb Marketplaces (OEDWM), which deals with the context of details connected to a domain for cyber terrorism (Yadav et al,2023) is the foundation of the semantic web. Crawling and extracting information (Panem et al, 2023) is a crucial source of data for knowledge-based systems. However, creating Crawler is a labour-intensive process that strongly depends on the developer's skill.…”

Section: Ontology Extracting Darkweb Marketplaces (Oedwm)mentioning

confidence: 99%

Perceptual Operating Systems for the Trade Associations of Cyber Criminals to Scrutinize Hazardous Content

Rawat,

Rajavat

2024

International Journal of Cyber Warfare and Terrorism

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The limits of user visibility have been exceeded by the internet. The “Dark Web” or “Dark Net” refers to certain unknown portions of the internet that cannot be found using standard search methods. A number of computerised techniques are being explored to extract or crawl the concealed data. All users can freely interact on the surface web. Identity identities may be found on the deep web, and the dark web (DW), a hub for anonymous data, is a haven for terrorists and cybercriminals to promote their ideologies and illegal activities. Officials in clandestine surveillance and cyberpolicing are always trying to track down offenders' trails or hints. The search for DW offenders might take five to ten years.The proposed study provides data from a DW mining and online marketplaces situation from a few domains, as well as an overview for investigators to build an automated engine for scraping all dangerous information from related sites.

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Using Machine Learning to Achieve Cyber security Requirements: A Comprehensive

Alsalamony

2024

Preprint

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The Role of Machine Learning and Artificial Intelligence in Detecting the Malicious Use of Cyber Space

Cited by 3 publications

References 14 publications

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

Perceptual Operating Systems for the Trade Associations of Cyber Criminals to Scrutinize Hazardous Content

Using Machine Learning to Achieve Cyber security Requirements: A Comprehensive

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