Application of Machine Learning Approaches in Intrusion Detection System: A Survey
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paper_title: An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection

paper_content:

In the past years, several support vector machines (SVM) novelty detection approaches have been applied on the network intrusion detection field. The main advantage of these approaches is that they can characterize normal traffic even when trained with datasets containing not only normal traffic but also a number of attacks. Unfortunately, these algorithms seem to be accurate only when the normal traffic vastly outnumbers the number of attacks present in the dataset. A situation which can not be always hold. This work presents an approach for autonomous labeling of normal traffic as a way of dealing with situations where class distribution does not present the imbalance required for SVM algorithms. In this case, the autonomous labeling process is made by SNORT, a misuse-based intrusion detection system. Experiments conducted on the 1998 DARPA dataset show that the use of the proposed autonomous labeling approach not only outperforms existing SVM alternatives but also, under some attack distributions, obtains improvements over SNORT itself.
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paper_title: A training algorithm for optimal margin classifiers

paper_content:

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.
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paper_title: Fuzzy set theory

paper_content:

Seit Beginn der neunziger Jahre wird unter dem Schlagwort źFuzzy Logic" von einer Vielzahl von Anwendungen der Theorie unscharfer Mengen vor allem in Japan berichtet. Dies hat in Europa und hier insbesondere in Deutschland zu einem Umdenken bei Wissenschaftlern und Praktikern gefuhrt, die lange Zeit die Potentiale der neuen Technologie verkannten. Nun ist schlagartig eine sehr groβe Nachfrage nach Informationen zu diesem Thema entstanden. Dieser Artikel gibt einen Uberblick uber die wesentlichen Grundlagen der Theorie unscharfer Mengen und zeigt potentielle Anwendungen auf. ::: ::: Since the beginning nineties, entitled with the catchword "Fuzzy Logic" reports have been given on numerous, predominantly Japanese applications of fuzzy set theory. This had brought about a change in thinking for scientists and practitioners in Europe and, in particular, in Germany. Had the potentials of this new technology for a long time been underestimated, there now suddenly springs up a very high demand for information on this topic. This article gives a survey of the fundamentals of fuzzy set theory and describes potential applications.
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Title: Application of Machine Learning Approaches in Intrusion Detection System: A Survey

Section 1: INTRODUCTION
Description 1: Provide an overview of the importance of internet security and the role of intrusion detection systems (IDS). Describe the two primary approaches to IDS: anomaly detection and signature-based detection. Briefly introduce the scope of the paper and its organization.

Section 2: Machine Learning Approach
Description 2: Discuss the concept of machine learning, its importance, and its broad categories (supervised, unsupervised, and reinforcement learning) in the context of intrusion detection systems.

Section 3: Single Classifiers
Description 3: Describe various standalone machine learning algorithms used for intrusion detection, including Decision Tree, Naive Bayes, K-nearest neighbor, Artificial Neural Network, Support Vector Machines, and Fuzzy Logic.

Section 4: Hybrid Classifiers
Description 4: Explain the concept of hybrid classifiers, which combine multiple machine learning algorithms to enhance IDS performance.

Section 5: Ensemble Classifiers
Description 5: Discuss ensemble classifiers, which integrate multiple classifiers to improve overall detection accuracy, and introduce common strategies like majority vote, bagging, and boosting.

Section 6: Distribution of Papers by Year of Publication
Description 6: Analyze the distribution of research papers over the years, categorizing them by single, hybrid, and ensemble classifiers used in intrusion detection.

Section 7: Used Dataset in Researches
Description 7: Review the datasets commonly used in IDS research, including the KDD Cup 1999, DARPA datasets, and the NSL-KDD dataset, and provide a summary of these datasets and their characteristics.

Section 8: Feature Selection
Description 8: Highlight the importance of feature selection in enhancing system performance, and analyze the use of feature selection step in the reviewed papers.

Section 9: Discussion and Conclusion
Description 9: Summarize the key findings from the survey, discuss the limitations of the current study, and suggest future research directions in applying machine learning approaches to IDS.