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@article{164121,
        author = {Musali Umesh Kumar Reddy and Shapuram Harsha and Digavabasi Reddy Yeshwanth Reddy and Shaik Tabariz Ahammed and A. Gowtham },
        title = {An Intrusion Detection Framework With Classification Using Support Vector Machine},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {114-119},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164121},
        abstract = {Intrusion detection is a very important component of security technologies which include adaptive security appliances, intrusion and detection systems in other words intrusion prevention systems, and firewalls. There rises an issue with the performance of different intrusion detection algorithms that are deployed. The effectiveness of intrusion detection relies on accuracy, which must be raised in order to reduce false alarms and boost detection rates. Recent work has employed techniques such as multilayer perceptron and' support vector machine (SVM) t' to address performance difficulties. These methods have drawbacks and' be ineffective when applied to' massive data sets, such systems and' network data. Large volumes of traffic data are analyzed by the intrusion detection system; thus, an effective classification method is required to solve the issue. This study examines this matter. Popular machine learning methods are used, including' random forest, extreme learning machine (ELM), an' SVM. These methods' reputation stems from their ability to' classify different data. Utilized by the NSL-knowledge discovery and data mining data set, which are regarded as a standard for ' attacker’ intrusion detection systems. The outcomes show that ELM works better'n alternative strategies!!!!},
        keywords = {NSL–KDD, Logistic Regression, k-nearest neighbor, Random forest, Support vector machine, Intrusion Detection and System.},
        month = {},
        }