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@article{149783,
        author = {Naimesh Kame and Sambhav Rakhe and Gitesh Chaudhari and Akash Ajnadkar and Shraddha Khonde},
        title = {Collaborative Attack Generation And Detection Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {1},
        pages = {586-589},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=149783},
        abstract = {Intrusions on systems are attempts to gain access to unauthorized data with malicious intent. Intrusion Detection Systems (IDS) is a system that can detect and report such attacks. Intruders always try to evade IDS taking advantage of its impotence to detect novel attacks, combined attacks or collaborative attacks. Combined attacks are attacks on a system consisting of two or more attacks done iteratively in a loop that hide the signature of a single attack. Collaborative attacks are more sophisticated, intelligent and powerful attacks that possess the ability to merge different attacks in a single packet. These attacks can depict the behaviour of various attacks but a signature of none. Detection of such attacks is only possible with a novel IDS dataset. KDD-99 is the most common IDS dataset; we use the attacks and features available in this dataset to make our collaborative IDS dataset. We also present a host-based machine learning IDS for detecting the same.},
        keywords = {KDD99, Collaborative, Novel dataset, Intrusion Detection System},
        month = {},
        }