A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions
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 detect...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10403908/ |
| _version_ | 1797322568114372608 |
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| author | Merve Ozkan-Okay Erdal Akin Omer Aslan Selahattin Kosunalp Teodor Iliev Ivaylo Stoyanov Ivan Beloev |
| author_facet | Merve Ozkan-Okay Erdal Akin Omer Aslan Selahattin Kosunalp Teodor Iliev Ivaylo Stoyanov Ivan Beloev |
| author_sort | Merve Ozkan-Okay |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T05:16:12Z |
| format | Article |
| id | doaj.art-8df1ed1bef5e41a5b155e1273bdad72e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T05:16:12Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8df1ed1bef5e41a5b155e1273bdad72e2024-02-07T00:01:38ZengIEEEIEEE Access2169-35362024-01-0112122291225610.1109/ACCESS.2024.335554710403908A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security SolutionsMerve Ozkan-Okay0https://orcid.org/0000-0002-1071-2541Erdal Akin1https://orcid.org/0000-0002-2223-3927Omer Aslan2https://orcid.org/0000-0003-0737-1966Selahattin Kosunalp3https://orcid.org/0000-0003-2899-4679Teodor Iliev4https://orcid.org/0000-0003-2214-8092Ivaylo Stoyanov5https://orcid.org/0000-0001-9824-1504Ivan Beloev6https://orcid.org/0000-0003-2014-1970Department of Computer Engineering, Ankara University, Gölbaşı, Ankara, TurkeyDepartment of Computer Engineering, Bitlis Eren University, Merkez, Bitlis, TurkeyDepartment of Software Engineering, Bandırma Onyedi Eylül University, Bandırma, Balıkesir, TurkeyDepartment of Computer Technologies, Gönen Vocational School, Bandırma Onyedi Eylül University, Bandırma, Balıkesir, TurkeyDepartment of Telecommunication, University of Ruse, Ruse, BulgariaDepartment of Electrical and Power Engineering, University of Ruse, Ruse, BulgariaDepartment of Transport, University of Ruse, Ruse, BulgariaGiven 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.https://ieeexplore.ieee.org/document/10403908/Cyberattacks and solutionsdeep learningmachine learningreinforcement learningAI tools |
| spellingShingle | Merve Ozkan-Okay Erdal Akin Omer Aslan Selahattin Kosunalp Teodor Iliev Ivaylo Stoyanov Ivan Beloev A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions IEEE Access Cyberattacks and solutions deep learning machine learning reinforcement learning AI tools |
| title | A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions |
| title_full | A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions |
| title_fullStr | A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions |
| title_full_unstemmed | A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions |
| title_short | A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions |
| title_sort | comprehensive survey evaluating the efficiency of artificial intelligence and machine learning techniques on cyber security solutions |
| topic | Cyberattacks and solutions deep learning machine learning reinforcement learning AI tools |
| url | https://ieeexplore.ieee.org/document/10403908/ |
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