| Title | Cyber Threat Analysis and Trustworthy Artificial Intelligence |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Wang, Shuangbao Paul, Arafin, Md Tanvir, Osuagwu, Onyema, Wandji, Ketchiozo |
| Conference Name | 2022 6th International Conference on Cryptography, Security and Privacy (CSP) |
| Keywords | Cybersecurity Damage Assessment, data analytics, Data Breach, machine learning, machine learning algorithms, privacy, pubcrawl, quantum computing, Quantum mechanics, reinforcement learning, resilience, Resiliency, supervised learning, threat detection, Trustworthy AI, Web servers |
| Abstract | Cyber threats can cause severe damage to computing infrastructure and systems as well as data breaches that make sensitive data vulnerable to attackers and adversaries. It is therefore imperative to discover those threats and stop them before bad actors penetrating into the information systems.Threats hunting algorithms based on machine learning have shown great advantage over classical methods. Reinforcement learning models are getting more accurate for identifying not only signature-based but also behavior-based threats. Quantum mechanics brings a new dimension in improving classification speed with exponential advantage. The accuracy of the AI/ML algorithms could be affected by many factors, from algorithm, data, to prejudicial, or even intentional. As a result, AI/ML applications need to be non-biased and trustworthy.In this research, we developed a machine learning-based cyber threat detection and assessment tool. It uses two-stage (both unsupervised and supervised learning) analyzing method on 822,226 log data recorded from a web server on AWS cloud. The results show the algorithm has the ability to identify the threats with high confidence. |
| DOI | 10.1109/CSP55486.2022.00024 |
| Citation Key | wang_cyber_2022 |
Cyber Threat Analysis and Trustworthy Artificial Intelligence