Abstract
In the era of modern lifestyle, the internet and networking are essential things for everyone. With the help these facilities everyone can exchange information between intranet and internet-connected people. During the information exchange, so many intermediate devices are involved, so that the security of information or data is primary concern for each and every involved system. Attackers or intruders belong to inside the network or outside of the network. To detect an intruder or attacker an intrusion detection system (IDS) has been proposed for the detection of normal and attack data packets for a network. In this work, KDD-99 dataset is used for the development of IDS. A total set of 32,640 samples are considered, in which 12,440 samples of normal and 20,200 samples of attack class are used. These samples are further bifurcated into training and testing set in balanced manner. Thus, 16,320 samples (normal: 6220 and attack: 10,100) are used for training set and same number of set is used for the testing set. For the supervised learning, SVM and kNN classifiers are used to differentiate between normal data packets and attack data packets with PCA as dimensionality reduction. After the successful completion of experiments, it has been found that PCA-kNN yields maximum accuracy of 90.07% at pc value of 5 using cosine distance.
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Kumar, I., Mohd, N., Bhatt, C., Sharma, S.K. (2020). Development of IDS Using Supervised Machine Learning. In: Pant, M., Kumar Sharma, T., Arya, R., Sahana, B., Zolfagharinia, H. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1154. Springer, Singapore. https://doi.org/10.1007/978-981-15-4032-5_52
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