The popularity of using Internet contains some risks of network attacks. Intrusion detection is one major research problem in network security, whose aim is to identify unusual access or attacks to secure internal networks. In literature, intrusion detection systems have been approached by various machine learning techniques. However, there is no a review paper to examine and understand the current status of using machine learning techniques to solve the intrusion detection problems. This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers. Related studies are compared by their classifier design, datasets used, and other experimental setups. Current achievements and limitations in developing intrusion detection systems by machine learning are present and discussed. A number of future research directions are also provided.

Review: Intrusion detection by machine learning: A review

The aim of an intrusion detection systems (IDS) is to detect various types of malicious network traffic and computer usage, which cannot be detected by a conventional firewall. Many IDS have been developed based on machine learning techniques. Specifically, advanced detection approaches created by combining or integrating multiple learning techniques have shown better detection performance than general single learning techniques. The feature representation method is an important pattern classifier that facilitates correct classifications, however, there have been very few related studies focusing how to extract more representative features for normal connections and effective detection of attacks. This paper proposes a novel feature representation approach, namely the cluster center and nearest neighbor (CANN) approach. In this approach, two distances are measured and summed, the first one based on the distance between each data sample and its cluster center, and the second distance is between the data and its nearest neighbor in the same cluster. Then, this new and one-dimensional distance based feature is used to represent each data sample for intrusion detection by a k-Nearest Neighbor (k-NN) classifier. The experimental results based on the KDD-Cup 99 dataset show that the CANN classifier not only performs better than or similar to k-NN and support vector machines trained and tested by the original feature representation in terms of classification accuracy, detection rates, and false alarms. I also provides high computational efficiency for the time of classifier training and testing (i.e., detection).

CANN: An intrusion detection system based on combining cluster centers and nearest neighbors

Intrusion detection is an important area of research. Traditionally, the approach taken to find attacks is to inspect the contents of every packet. However, packet inspection cannot easily be performed at high-speeds. Therefore, researchers and operators started investigating alternative approaches, such as flow-based intrusion detection. In that approach the flow of data through the network is analyzed, instead of the contents of each individual packet. The goal of this paper is to provide a survey of current research in the area of flow-based intrusion detection. The survey starts with a motivation why flow-based intrusion detection is needed. The concept of flows is explained, and relevant standards are identified. The paper provides a classification of attacks and defense techniques and shows how flow-based techniques can be used to detect scans, worms, Botnets and (DoS) attacks.

/pdf/an-overview-of-ip-flow-based-intrusion-detection-v0mpuqd4o1.pdf

An Overview of IP Flow-Based Intrusion Detection

https://romisatriawahono.net/lecture/rm/survey/network%20security/Patel%20-%20Intrution%20Detection%20System%20in%20Cloud%20-%202013.pdf

Review: An intrusion detection and prevention system in cloud computing: A systematic review

This work presents a survey of the current state of the art of visualization techniques for parallel coordinates. It covers geometric models for constructing parallel coordinates and reviews methods for creating and understanding visual representations of parallel coordinates. The classification of these methods is based on a taxonomy that was established from the literature and is aimed at guiding researchers to find existing techniques and identifying white spots that require further research. The techniques covered in this survey are further related to an established taxonomy of knowledge-discovery tasks to support users of parallel coordinates in choosing a technique for their problem at hand. Finally, we discuss the challenges in constructing and understanding parallel-coordinates plots and provide some examples from different application domains.

State of the Art of Parallel Coordinates

Due to the increase in unauthorized access and stealing of internet resources, internet security has become a very significant issue. Network anomalies in particular can cause many potential problems, but it is difficult to discern these from normal traffic. In this paper, we focus on a Support Vector Machine (SVM) and a genetic algorithm to detect network anomalous attacks. We first use a genetic algorithm (GA) for choosing proper fields of traffic packets for analysis. Only the selected fields are used, and a time delay processing is applied to SVM for considering temporal relationships among packets. In order to verify our approach, we tested our proposal with the datasets of MIT Lincoln Lab, and then analyzed its performance. Our SVM approach with selected fields showed excellent performance.

SVM approach with a genetic algorithm for network intrusion detection

With the growth of critical worm threats, many researchers have studied worm-related topics and internet anomalies. The researches are mainly concentrated on worm propagation and detection more than the fundamental characteristics of worms. It is very important to know worms' characteristics because the characteristics provide basic resource for worm prevention. Unfortunately, this kind of research cases are very few until now. Moreover the existing researches only focus on understanding the function structure of the worm propagation or the taxonomy of the worm according to a sequence of worm attacks. Thus, in this paper, we try to confirm the formalized pattern of the worm action from the existing researches and analyze the report of the anti-virus companies. Finally, we define the formalized actions based on temporal behaviors and worm packet flows, and we apply our proposed method for the new worm classification.

An approach for classifying internet worms based on temporal behaviors and packet flows

The expansion of computer networks has lead to the increase of online network attacks. Therefore, an efficient method to detect and analyze network attacks is inevitable. As a result much research has been done on network visualization. This paper proposes a method which efficiently visualizes and analyzes network attacks using parallel coordinates. A brief review on the limitations on previous visualization methods and a structure which can analyze network attacks through visualization will be presented. Moreover, experimental results on visualization of scanning attacks, denial of service attacks and spoofing attacks using multi parallel coordinates will be shown.

Visualization of network components for attack analysis

Network data modeling is a essential research for the evaluation for intrusion detection systems performance, network modeling and methods for analyzing network data. In network data modeling, real data from the network must be analyzed and the modeled data must be efficiently composed to reflect a sufficient amount of the original data. In this parer the useful elements of real network data were quantified from packets captured from a huge network. Futhermore, a statistical analysis method was used to find the most effective element for efficiently classifying the modeled data.

/pdf/effective-feature-selection-model-for-network-data-modeling-gkpeiarg2o.pdf

Effective Feature Selection Model for Network Data Modeling

This paper suggests a method to check whether the real network dataset and modeling dataset for real network has statistically similar characteristics. The method we adopt in this paper is a Generalized Additive Model. By using this method, we show how similar the MIT/LL Dataset and the KDD CUP 99' Dataset are regarding their characteristics. It provided reasonable outcome for us to confirm that MIT/LL Dataset and KDD Cup Dataset are not statistically similar.

A Regression Method to Compare Network Data and Modeling Data Using Generalized Additive Model

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