Using Deep Learning Algorithm in Security Informatics


Authors : Rachid Tahril; Abdellatif Lasbahani; Abdessamad Jarrar; Youssef Balouki

Volume/Issue : Volume 9 - 2024, Issue 4 - April

Google Scholar : https://tinyurl.com/2v3hyszz

Scribd : https://tinyurl.com/3a583jan

DOI : https://doi.org/10.38124/ijisrt/IJISRT24APR2271

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Abstract : The utilization of deep learning algorithms in security informatics has revolutionized cybersecurity, offering advanced solutions for threat detection and mitigation. This paper presents findings from research exploring the efficacy of deep learning in various security domains, including anomaly detection, malware detection, phishing detection, and threat intelligence analysis. Results demonstrate high detection rates and accuracy, with anomaly detection achieving a remarkable 98.5% detection rate and malware detection showcasing a classification accuracy of 99.2%. Phishing detection also yielded promising results with a detection accuracy of 95.8%. These findings underscore the potential of deep learning in enhancing security defenses. However, challenges such as interpretability and robustness remain, necessitating further research and development. By addressing these challenges and prioritizing robust security measures, organizations can leverage deep learning to create more effective and trustworthy security solutions, thereby mitigating cyber threats and safeguarding digital assets.

Keywords : Deep Learning, Security Informatics, Anomaly Detection, Malware Detection, Phishing Detection, Threat Intelligence Analysis, Cybersecurity, Interpretation, Robustness, Mitigation.

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The utilization of deep learning algorithms in security informatics has revolutionized cybersecurity, offering advanced solutions for threat detection and mitigation. This paper presents findings from research exploring the efficacy of deep learning in various security domains, including anomaly detection, malware detection, phishing detection, and threat intelligence analysis. Results demonstrate high detection rates and accuracy, with anomaly detection achieving a remarkable 98.5% detection rate and malware detection showcasing a classification accuracy of 99.2%. Phishing detection also yielded promising results with a detection accuracy of 95.8%. These findings underscore the potential of deep learning in enhancing security defenses. However, challenges such as interpretability and robustness remain, necessitating further research and development. By addressing these challenges and prioritizing robust security measures, organizations can leverage deep learning to create more effective and trustworthy security solutions, thereby mitigating cyber threats and safeguarding digital assets.

Keywords : Deep Learning, Security Informatics, Anomaly Detection, Malware Detection, Phishing Detection, Threat Intelligence Analysis, Cybersecurity, Interpretation, Robustness, Mitigation.

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