Intrusion Detection System using KDD Cup 99 Dataset

Intrusion Detection System is a vital feature of protecting network infrastructure from unauthorized users or hackers. Intrusion detection system is used to identify several types of malicious activities that could effect the safety of network and to reduce network traffic. Because of faster growth of Internet, networks are growing rapidly in every area of society. As a result, large amount of data is travelling across many networks which may lead to vulnerability of integrity and confidentiality of data. Many Machine learning models are opened up providing new opportunity to classify traffic in network. In quest to select a good learning model, this paper illustrates performance between J48, Naive Bayes and Random forest classification models. The KDD Cup 99 dataset is used for experimental analysis to identify which classification model improves correctness of data and attains highest accuracy.

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Building attack detection system base on machine learning

Global Journal of Engineering and Technology Advances ◽

10.30574/gjeta.2021.6.2.0010 ◽

2021 ◽

Vol 6 (2) ◽

pp. 018-032

Author(s):

Rasha Thamer Shawe ◽

Kawther Thabt Saleh ◽

Farah Neamah Abbas

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Network Security ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Attack Detection ◽

Support Vector ◽

Data Set ◽

Kdd Cup 99

These days, security threats detection, generally discussed to as intrusion, has befitted actual significant and serious problem in network, information and data security. Thus, an intrusion detection system (IDS) has befitted actual important element in computer or network security. Avoidance of such intrusions wholly bases on detection ability of Intrusion Detection System (IDS) which productions necessary job in network security such it identifies different kinds of attacks in network. Moreover, the data mining has been playing an important job in the different disciplines of technologies and sciences. For computer security, data mining are presented for serving intrusion detection System (IDS) to detect intruders accurately. One of the vital techniques of data mining is characteristic, so we suggest Intrusion Detection System utilizing data mining approach: SVM (Support Vector Machine). In suggest system, the classification will be through by employing SVM and realization concerning the suggested system efficiency will be accomplish by executing a number of experiments employing KDD Cup’99 dataset. SVM (Support Vector Machine) is one of the best distinguished classification techniques in the data mining region. KDD Cup’99 data set is utilized to execute several investigates in our suggested system. The experimental results illustration that we can decrease wide time is taken to construct SVM model by accomplishment suitable data set pre-processing. False Positive Rate (FPR) is decrease and Attack detection rate of SVM is increased .applied with classification algorithm gives the accuracy highest result. Implementation Environment Intrusion detection system is implemented using Mat lab 2015 programming language, and the examinations have been implemented in the environment of Windows-7 operating system mat lab R2015a, the processor: Core i7- Duo CPU 2670, 2.5 GHz, and (8GB) RAM.

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Analysis of NSL KDD Dataset Using Classification Algorithms for Intrusion Detection System

Recent Patents on Engineering ◽

10.2174/1872212112666180402122150 ◽

2019 ◽

Vol 13 (2) ◽

pp. 142-147

Author(s):

Srishti Sharma ◽

Yogita Gigras ◽

Rita Chhikara ◽

Anuradha Dhull

Keyword(s):

Random Forest ◽

Intrusion Detection ◽

Detection System ◽

Random Trees ◽

Attribute Selection ◽

Classification Algorithms ◽

Random Forest Classification ◽

Detection Systems ◽

Forest Classification ◽

Feature Attribute

Background: Intrusion detection systems are responsible for detecting anomalies and network attacks. Building of an effective IDS depends upon the readily available dataset. This dataset is used to train and test intelligent IDS. In this research, NSL KDD dataset (an improvement over original KDD Cup 1999 dataset) is used as KDD’99 contains huge amount of redundant records, which makes it difficult to process the data accurately. Methods: The classification techniques applied on this dataset to analyze the data are decision trees like J48, Random Forest and Random Trees. Results: On comparison of these three classification algorithms, Random Forest was proved to produce the best results and therefore, Random Forest classification method was used to further analyze the data. The results are analyzed and depicted in this paper with the help of feature/attribute selection by applying all the possible combinations. Conclusion: There are total of eight significant attributes selected after applying various attribute selection methods on NSL KDD dataset.

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