Enhancing Network Security with AI/ML Algorithms Along with Cloud Deployment

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Apurba Chatterjee

Enhancing Network Security with AI/ML Algorithms Along with Cloud Deployment

Authors: Apurba Chatterjee

Unique Paper ID: 170585
Volume: 11
Issue: 7
PageNo: 843-845

Keywords: Machine Learning malware algorithms XGBoost LSTM CNN cloud.

Abstract:
Early adopters of machine learning-powered network security solutions can gain a competitive edge by demonstrating a proactive and robust security posture. Machine learning can detect never-before-seen attacks (zero-day exploits) by recognizing unusual behavior, significantly enhancing a company's security posture. The KDD99 dataset is used as a base for the research. Network intrusion detection systems analyze network traffic to identify malicious activities. The activity for detection includes denial-of-service attacks, port scans, malware distribution, and unauthorized access attempts. Some of the algorithms that has been used while writing the python code are: XGBoost, LSTM and CNN. This will be an effective approach to any network-based systems. We can enact the algorithm deployment as a part of our daily cloud deployment.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 7
PageNo: 843-845

Enhancing Network Security with AI/ML Algorithms Along with Cloud Deployment

Available:https://ijirt.org/article?manuscript=170585

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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