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Title |
FLORA: Fuzzy Logic - Objective Risk Analysis for Intrusion Detection and Prevention |
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Author |
Alwi M Bamhdi |
| Citation |
Vol. 23 No. 5 pp. 179-192 |
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Abstract |
The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions. |
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Keywords |
Anomaly detection, cybersecurity, cyber-attacks, fuzzy logic, information & data security, intrusion detection & prevention, risk analysis & assessment. |
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