AI-Enabled System for Efficient and Effective Cyber Incident Detection and Response in Cloud Environments
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914319794438144 |
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| author | Farzaan, Mohammed Ashfaaq M. Ghanem, Mohamed Chahine El-Hajjar, Ayman Ratnayake, Deepthi N. |
| author_facet | Farzaan, Mohammed Ashfaaq M. Ghanem, Mohamed Chahine El-Hajjar, Ayman Ratnayake, Deepthi N. |
| contents | The escalating sophistication and volume of cyber threats in cloud environments necessitate a paradigm shift in strategies. Recognising the need for an automated and precise response to cyber threats, this research explores the application of AI and ML and proposes an AI-powered cyber incident response system for cloud environments. This system, encompassing Network Traffic Classification, Web Intrusion Detection, and post-incident Malware Analysis (built as a Flask application), achieves seamless integration across platforms like Google Cloud and Microsoft Azure. The findings from this research highlight the effectiveness of the Random Forest model, achieving an accuracy of 90% for the Network Traffic Classifier and 96% for the Malware Analysis Dual Model application. Our research highlights the strengths of AI-powered cyber security. The Random Forest model excels at classifying cyber threats, offering an efficient and robust solution. Deep learning models significantly improve accuracy, and their resource demands can be managed using cloud-based TPUs and GPUs. Cloud environments themselves provide a perfect platform for hosting these AI/ML systems, while container technology ensures both efficiency and scalability. These findings demonstrate the contribution of the AI-led system in guaranteeing a robust and scalable cyber incident response solution in the cloud. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05602 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-Enabled System for Efficient and Effective Cyber Incident Detection and Response in Cloud Environments Farzaan, Mohammed Ashfaaq M. Ghanem, Mohamed Chahine El-Hajjar, Ayman Ratnayake, Deepthi N. Cryptography and Security Emerging Technologies Networking and Internet Architecture The escalating sophistication and volume of cyber threats in cloud environments necessitate a paradigm shift in strategies. Recognising the need for an automated and precise response to cyber threats, this research explores the application of AI and ML and proposes an AI-powered cyber incident response system for cloud environments. This system, encompassing Network Traffic Classification, Web Intrusion Detection, and post-incident Malware Analysis (built as a Flask application), achieves seamless integration across platforms like Google Cloud and Microsoft Azure. The findings from this research highlight the effectiveness of the Random Forest model, achieving an accuracy of 90% for the Network Traffic Classifier and 96% for the Malware Analysis Dual Model application. Our research highlights the strengths of AI-powered cyber security. The Random Forest model excels at classifying cyber threats, offering an efficient and robust solution. Deep learning models significantly improve accuracy, and their resource demands can be managed using cloud-based TPUs and GPUs. Cloud environments themselves provide a perfect platform for hosting these AI/ML systems, while container technology ensures both efficiency and scalability. These findings demonstrate the contribution of the AI-led system in guaranteeing a robust and scalable cyber incident response solution in the cloud. |
| title | AI-Enabled System for Efficient and Effective Cyber Incident Detection and Response in Cloud Environments |
| topic | Cryptography and Security Emerging Technologies Networking and Internet Architecture |
| url | https://arxiv.org/abs/2404.05602 |