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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}, }
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