You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Literature Survey and Comprehensive Study of Intrusion Detection 
Chapter Title:  Support vector machine based intrusion detection 
1. Abstract of Cited Paper (BIB001):  This paper proposes an intelligent multi level classification technique for effective intrusion detection in Mobile Ad-hoc Networks. The algorithm uses a combination of a tree classifier which uses a labeled training data and an Enhanced Multiclass SVM algorithm. Moreover, an effective preprocessing technique has been proposed and implemented in this work in order to improve the detection accuracy and to reduce the processing time. From the experiments carried out in this work, it has been observed that significant improvement has been achieved in this model from the view point of both high detection rates as well as low false alarm rates. 
2. Abstract of Cited Paper (BIB002):  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. 
3. Abstract of Cited Paper (BIB003):  Intrusion detection is an important technique in computer and network security. A variety of intrusion detection approaches be present to resolve this severe issue but the main problem is performance. It is important to increase the detection rates and reduce false alarm rates in the area of intrusion detection. Therefore, in this research, an optimized intrusion detection mechanism using soft computing techniques is proposed to overcome performance issues. The KDD-cup dataset is used that is a benchmark for evaluating the security detection mechanisms. The Principal Component Analysis (PCA) is applied to transform the input samples into a new feature space. The selecting of an appropriate number of principal components is a critical problem. So, Genetic Algorithm (GA) is used in the optimum selection of principal components instead of using traditional method. The Support Vector Machine (SVM) is used for classification purpose. The performance of this approach is addresses. Further, a comparative analysis is made with existing approaches. Consequently, this method provides optimal intrusion detection mechanism which is capable to minimize amount of features and maximize the detection rates.

Literature Review:
A brief review of support vector machine classifier related is discussed in this section. In the period 2007-2012, a lot of papers have been presented to represent the Support vector machine based intrusion detection. Some of the papers have been discussed below. A revise for improving the training time of SVM has been presented by Latifur Khan et al. BIB001 , particularly when contracting with large data sets using hierarchical clustering analysis in 2007. For gathering, they utilized the Dynamically Growing Self-Organizing Tree (DGSOT) algorithm since it had verified to triumph over the disadvantages of traditional hierarchical clustering algorithms (e.g., hierarchical agglomerative clustering). Among two classes, clustering analysis assisted discover the boundary points, which were the most capable data points to coach SVM. Using the clustering arrangement created by the DGSOT algorithm, they offered an approach of amalgamation of SVM and DGSOT, which in progress with a first training set and enlarge it slowly. In terms of precision loss and training time gain by means of a single bench-mark real data set they match up to their approach with the Rocchio Bundling technique and casual choice. In advanced to the above approach in the year 2011, Iftikhar Ahmad,Azween et al. have proposed a paper to surmount presentation issues an optimized interference detection mechanism by means of soft computing techniques BIB004 . The KDD-cup dataset was applied that was a benchmark for assessing the safety identification mechanisms. To change the key in models into a feature space the Principal Component Analysis (PCA) was applied. Selecting of a suitable quantity of principal components was an important problem. As an alternative of using conventional method, Genetic Algorithm (GA) was applied in the optimum choice of principal components accordingly. The Support Vector Machine (SVM) was employed for categorization reason. In addition, a proportional study was prepared with presented approaches. Therefore, the technique presented optimal interference detection mechanism was proficient to minimize amount of features and maximize the identification rates. S. Ganapathy et al. BIB002 have proposed an intelligent multi level classification technique for effective intrusion detection in Mobile Ad-hoc Networks in the same year. The algorithm used a combination of a tree classifier which used a labeled training data and an Enhanced Multiclass SVM algorithm. Moreover, an effective preprocessing technique had been proposed and implemented in this work in order to improve the detection accuracy and to reduce the processing time. As a way of dealing with conditions for independent labeling of regular traffic where class distribution does not present the imbalance necessary for SVM algorithms, an approach has been presented by Carlos A. Catania et al. BIB003 in 2012. In such case, the self-governing labeling process was made by SNORT, a misuse-based intrusion detection system. The use of the planned autonomous labeling approach not only outperforms presented SVM alternatives that was shown by the experiments conducted on the 1998 DARPA dataset however attains developments over SNORT itself also under some attack distributions.