Publikationer från Malmö universitet
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A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions
Ankara Univ, Dept Comp Engn, TR-06100 Golbasi, Ankara, Turkiye..
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP). Bitlis Eren Univ, Dept Comp Engn, TR-13100 Merkez, Bitlis, Turkiye..ORCID-id: 0000-0002-2223-3927
Bandirma Onyedi Eylul Univ, Dept Software Engn, TR-10250 Bandirma, Balikesir, Turkiye..ORCID-id: 0000-0003-0737-1966
Bandirma Onyedi Eylul Univ, Gonen Vocat Sch, Dept Comp Technol, TR-10250 Bandirma, Balikesir, Turkiye..
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2024 (engelsk)Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 12, s. 12229-12256Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL), has become essential in the realm of cybersecurity. These techniques have proven to be effective in detecting and mitigating cyberattacks, which can cause significant harm to individuals, organizations, and even countries. Machine learning algorithms use statistical methods to identify patterns and anomalies in large datasets, enabling security analysts to detect previously unknown threats. Deep learning, a subfield of ML, has shown great potential in improving the accuracy and efficiency of cybersecurity systems, particularly in image and speech recognition. On the other hand, RL is again a subfield of machine learning that trains algorithms to learn through trial and error, making it particularly effective in dynamic environments. We also evaluated the usage of ChatGPT-like AI tools in cyber-related problem domains on both sides, positive and negative. This article provides an overview of how ML, DL, and RL are applied in cybersecurity, including their usage in malware detection, intrusion detection, vulnerability assessment, and other areas. The paper also specifies several research questions to provide a more comprehensive framework to investigate the efficiency of AI and ML models in the cybersecurity domain. The state-of-the-art studies using ML, DL, and RL models are evaluated in each Section based on the main idea, techniques, and important findings. It also discusses these techniques' challenges and limitations, including data quality, interpretability, and adversarial attacks. Overall, the use of ML, DL, and RL in cybersecurity holds great promise for improving the effectiveness of security systems and enhancing our ability to protect against cyberattacks. Therefore, it is essential to continue developing and refining these techniques to address the ever-evolving nature of cyber threats. Besides, some promising solutions that rely on machine learning, deep learning, and reinforcement learning are susceptible to adversarial attacks, underscoring the importance of factoring in this vulnerability when devising countermeasures against sophisticated cyber threats. We also concluded that ChatGPT can be a valuable tool for cybersecurity, but it should be noted that ChatGPT-like tools can also be manipulated to threaten the integrity, confidentiality, and availability of data.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2024. Vol. 12, s. 12229-12256
Emneord [en]
Cyberattacks and solutions, deep learning, machine learning, reinforcement learning, AI tools
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Identifikatorer
URN: urn:nbn:se:mau:diva-66155DOI: 10.1109/ACCESS.2024.3355547ISI: 001152597400001Scopus ID: 2-s2.0-85182941248OAI: oai:DiVA.org:mau-66155DiVA, id: diva2:1841062
Tilgjengelig fra: 2024-02-27 Laget: 2024-02-27 Sist oppdatert: 2024-02-27bibliografisk kontrollert

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