Host-based Intrusion Detection System (HIDS) 
AmanYadav1, Abhishek Srivastav2, Abhinandan Tiwari3, Krishna Vir Singh4
1AmanYadav*, CSE Department, ABESEC Ghaziabad AKTU Lucknow , India.
2Abhishek Srivastav, CSE Department, ABESEC Ghaziabad AKTU Lucknow , India.
3Abhinandan Tiwari, CSE Department, ABESEC Ghaziabad AKTU Lucknow, India.
4Krishna Vir Singh, CSE Department, ABESEC Ghaziabad AKTU Lucknow , India.
Manuscript received on June 01, 2020. | Revised Manuscript received on June 08, 2020. | Manuscript published on June 30, 2020. | PP: 1043-1049 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9903069520/2020©BEIESP | DOI: 10.35940/ijeat.E9903.069520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents the data analysis and feature extraction of KDD dataset of 1999. This is used to detect signature based and anomaly attacks on a system. The process is supported by data extraction as well as data cleaning of the above mentioned data set. The dataset consists of 42 parameters and 58 services. These parameters are further filtered to extract useful attributes. Every attack in the dataset is labeled either with “normal” or into four different attack types i.e. denial-of-service, network probe, remote-to-local or user-to-root. Using different machine learning algorithms, the work tries to compare the individual accuracy, True Positive and False positive rate of every algorithm with every other algorithm. The work focuses its attention to increase security through detection of static as well as dynamic attack.
Keywords: Host based intrusion detection, Data cleaning, Data analysis, Machine learning, KDD cupp’99, Attack, anomaly.