AI-DRIVEN INTRUSION DETECTION SYSTEMS FOR MICROSERVICES IN B2B SALES PLATFORMS
Keywords:
Intrusion Detection System, B2B, Microservice, AI, LIMEAbstract
Due to the increase in cyberattacks, there is a need to enhance security measures, especially for B2B sales platforms that integrate microservices. Traditional IDS are ineffective in this environment, and the incorporation of artificial intelligence is implemented to improve the detection and prevention of threats. Machine learning-based IDS enhance the ability of the IDS to discover novelties, identify new forms of threats, and learn with them. This paper aims to uncover the possibility of the future application of AI in changing IDS for microservices in B2B sales platforms. It looks at the advantages of an IDS that is propelled by AI, where accuracy is enhanced, new threats are detected in real-time, and few false positives are experienced. The paper also considers specific examples of its functioning, in particular the identification of man-in-the-middle and injection attacks, combating DoS attacks and data leakage. It also highlights the need for xAI to establish trust and be able to understand how these AI systems make their decisions. The paper concludes, especially by emphasizing the importance of the AI-based IDS in protecting B2B sales platforms and the security of sensitive business transactions.
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