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@article{170585,
        author = {Apurba Chatterjee},
        title = {Enhancing Network Security with AI/ML Algorithms Along with Cloud Deployment},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {843-845},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170585},
        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.},
        keywords = {Machine Learning, malware, algorithms, XGBoost, LSTM, CNN, cloud.},
        month = {December},
        }