Iftikhar Ahmad

Bio: Iftikhar Ahmad is an academic researcher. The author has contributed to research in topics: Intrusion detection system & Feature (computer vision). The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Feature selection using particle swarm optimization in intrusion detection

Abstract: The prevention of intrusion in networks is decisive and an intrusion detection system is extremely desirable with potent intrusion detection mechanism. Excessive work is done on intrusion detection systems but still these are not powerful due to high number of false alarms. One of the leading causes of false alarms is due to the usage of a raw dataset that contains redundancy. To resolve this issue, feature selection is necessary which can improve intrusion detection performance. Latterly, principal component analysis (PCA) has been used for feature reduction and subset selection in which features are primarily projected into a principal space and then features are elected based on their eigenvalues, but the features with the highest eigenvalues may not have the guaranty to provide optimal sensitivity for the classifier. To avoid this problem, an optimization method is required. Evolutionary optimization approach like genetic algorithm (GA) has been used to search the most discriminative subset of transformed features. The particle swarm optimization (PSO) is another optimization approach based on the behavioral study of animals/birds. Therefore, in this paper a feature subset selection based on PSO is proposed which provides better performance as compared to GA.

Filtering Irrelevant Information for Rational Decision Making

Abstract: This chapter deals with the concept of using relevant information as a basis of rational decision making. In this regard, whenever information is irrelevant it needs to be marginalized or eliminated. Making decisions using information which contains irrelevant information often confuses a decision making process. In this chapter we discuss four methods for making rational decisions by either marginalizing irrelevant information or not using irrelevant information. These methods are marginalization of irrationality approach, automatic relevance determination, principal component analysis and independent component analysis. These techniques are applied to condition monitoring, credit scoring, interstate conflict and face recognition.

Face Recognition based on PCA and DCT Combination Technique

Abstract: Face recognition is one of the most successful applications of image analysis and understanding and has gained much attention in recent years. Face Recognition is a computer based application which detects the faces of different persons for authentication and various other purposes. It has separate applications from the fingerprint and iris recognitions. There are various successful techniques are purposed so far as Holistically methods and Discrete Cosine Transform (DCT). In this paper, we purpose a simple and fast face recognition system on the basis of feature extraction using Principal Component Analysis (PCA) is a classical and successful method of dimension reduction and the discrete cosine transform paper (DCT) is a well know compression technique. This paper proposes for improving the recognition rate of the face recognition system. The main advantage of this technique is increase the efficiency and implementation easier, high speed and better recognition rate .