CSE

This paper deals with a method able to provide a microprocessor-based system with safety capabilities by modifying the source code of the executed application, only. The method exploits a set of transformations which can automatically be applied, thus greatly reducing the cost of designing a safe system, and increasing the confidence in its correctness. Fault Injection experiments have been performed on a sample application using two different systems based on CISC and RISC processors. Results demonstrate that the method effectiveness is rather independent of the adopted platform

Intrusions are very common problem in the present scenario. To get rid of the intrusions we have the intrusion detection systems but we need to maintain the performance of the intrusion detection systems. For the intrusion detection system to be fast and effective an optimal feature subset should be made by using feature selection approaches. Therefore we proposed the approach of feature selection using random forest to improve the performance of intrusion detection systems. So, we tried to find out an optimal feature subset whose performance is equal to the performance of 41 features or near to it, so that we can make the intrusion detection systems better. We carried out our experiments on kddcup'99 dataset with random forest to select important features.

In today's era detection of security threats that are commonly referred to as intrusion, has become a very important and critical issue in network, data and information security. Highly confidential data of various organizations are present over the network so in order to preserve that data from unauthorized users or attackers a strong security framework is required. Intrusion detection system plays a major role in providing security to computer networks. An Intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside the organization. The Intrusion detection system deals with large amount of data whichcontains various irrelevant and redundant features resulting in increased processing time and low detection rate. Therefore feature selection plays an important role in intrusion detection. There arevarious feature selection methods proposed in literature by different authors. In this paper a comparative analysis of different feature selection methods are presented on KDDCUP'99 benchmark dataset and their performance are evaluated in terms of detection rate, root mean square error and computational time.

The security of information and data is a critical issue in a computer networked environment. In our society computer networks are used to store proprietary information and to provide services for organizations and society. So in order to secure this valuable information from unknown attacks (intrusions) need of intrusion detection system arises. There are many intrusion detection approaches focused on the issues of feature reduction as some of the features are irrelevant or redundant which results in lengthy detection process and degrading the performance of an IDS. So in order to design lightweight IDS we investigate the performance of three feature selection approaches CFS, Information Gain and Gain Ratio. In this paper we propose a fusion model by making use of the three standard algorithms and finally applying genetic algorithm that identify important reduced input features. We apply Naive Bayes classifier on the dataset for evaluating the performance of the proposed method over the standard ones. The reduced attributes shows that proposed algorithm give better performance that is efficient and effective for detecting intrusions.

Nowadays, detection of security threats, commonly referred to as intrusion, has become a very important and critical issue in network, data and information security. Therefore, an intrusion detection system (IDS) has become a very essential component in computer or network security. Prevention of such intrusions entirely depends on detection capability of Intrusion Detection System (IDS). As network speed becomes faster, there is an emerge need for IDS to be lightweight with high detection rates. Therefore, many feature selection approaches/methods are proposed in the literature. There are three broad categories of approaches for selecting good feature subset as filter, wrapper and hybrid approach. The aim of this paper is to present a survey of various feature selection methods for IDS on KDD CUP'99 bench mark dataset based on these three categories and different evaluation criteria.

An intrusion detection (ID) technique classifies the incoming network traffic, represented as a feature vector, into anomalous or normal traffic by a classification
method. In practice, it has been observed that the high dimensionality of the feature vector degrades classification performance. To reduce the dimensionality, without compromising the performance, a new hybrid feature selection method has been introduced and its performance is measured on KDD Cup’99 dataset by the classifiers Naïve Bayes and C4.5. Three sets of experiments have been
conducted using full feature set, reduced sets of features obtained using four well known feature selection methods as Correlation-based Feature Selection (CFS),
Consistency-based Feature Selection (CON), Information Gain (IG), Gain Ratio (GR) and the proposed method on the said dataset and classifiers. In first experiment, classifier Naïve Bayes and C4.5 yielded classification accuracy 97.5% and
99.8% respectively. In second set of experiments, the best performance (accuracy) of these classifiers was achieved as 99.1% and 99.8% by the method IG. In third experiment, 6 features are obtained using proposed method and noted the same as 99.4% and 99.9%. The proposed hybrid feature selection method outperformed earlier mentioned methods on various metrics.