Gulshan Kumar

The use of artificial intelligence based techniques for intrusion detection: a review

The Internet connects hundreds of millions of computers across the world running on multiple hardware and software platforms providing communication and commercial services. However, this interconnectivity among computers also enables malicious users to misuse resources and mount Internet attacks. The continuously growing Internet attacks pose severe challenges to develop a flexible, adaptive security oriented methods. Intrusion detection system (IDS) is one of most important component being used to detect the Internet attacks. In literature, different techniques from various disciplines have been utilized to develop efficient IDS. Artificial intelligence (AI) based techniques plays prominent role in development of IDS and has many benefits over other techniques. However, there is no comprehensive review of AI based techniques to examine and understand the current status of these techniques to solve the intrusion detection problems. In this paper, various AI based techniques have been reviewed focusing on development of IDS. Related studies have been compared by their source of audit data, processing criteria, technique used, dataset, classifier design, feature reduction technique employed and other experimental environment setup. Benefits and limitations of AI based techniques have been discussed. The paper will help the better understanding of different directions in which research has been done in the field of IDS. The findings of this paper provide useful insights into literature and are beneficial for those who are interested in applications of AI based techniques to IDS and related fields. The review also provides the future directions of the research in this area.

Design of an evolutionary approach for intrusion detection.

A novel evolutionary approach is proposed for effective intrusion detection based on benchmark datasets. The proposed approach can generate a pool of noninferior individual solutions and ensemble solutions thereof. The generated ensembles can be used to detect the intrusions accurately. For intrusion detection problem, the proposed approach could consider conflicting objectives simultaneously like detection rate of each attack class, error rate, accuracy, diversity, and so forth. The proposed approach can generate a pool of noninferior solutions and ensembles thereof having optimized trade-offs values of multiple conflicting objectives. In this paper, a three-phase, approach is proposed to generate solutions to a simple chromosome design in the first phase. In the first phase, a Pareto front of noninferior individual solutions is approximated. In the second phase of the proposed approach, the entire solution set is further refined to determine effective ensemble solutions considering solution interaction. In this phase, another improved Pareto front of ensemble solutions over that of individual solutions is approximated. The ensemble solutions in improved Pareto front reported improved detection results based on benchmark datasets for intrusion detection. In the third phase, a combination method like majority voting method is used to fuse the predictions of individual solutions for determining prediction of ensemble solution. Benchmark datasets, namely, KDD cup 1999 and ISCX 2012 dataset, are used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover individual solutions and ensemble solutions thereof with a good support and a detection rate from benchmark datasets (in comparison with well-known ensemble methods like bagging and boosting). In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives, and a dataset can be represented in the form of labelled instances in terms of its features.

The use of artificial-intelligence-based ensembles for intrusion detection: a review

In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI-) based techniques play prominent role in development of ensemble for intrusion detection (ID) and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular) during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1) architecture & approach followed; (2) different methods utilized in different phases of ensemble learning; (3) other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs).

An information theoretic approach for feature selection

Feature selection methods play a significant role during classification of data having high dimensions of features. The methods select most relevant subset of features that describe data appropriately. Mutual information (MI) based upon information theory is one of the metrics used for measuring relevance of features. This paper analyses various feature selection methods for (1) reduction in number of features; (2) performance of Naive Bayes classification model trained on reduced set of features. Research gaps identified are: (1) computation of MI from the whole sample space instead of unclassified sample subspace; (2) consideration of relevance of features only or tradeoff between relevance and redundancy, but class conditional interaction of features is ignored. In this paper, we propose a general evaluation function using MI for feature selection. The proposed evaluation function is implemented which use dynamically computed MI values from unclassified instances. Effectiveness of the proposed feature selection method is done empirically by comparing classification results using KDD 1999 benchmarked dataset of intrusion detection. The results indicate practicability and effectiveness of the proposed method for applications concerned with high accuracy and stability of predictions. Copyright

Network security – an updated perspective

Network security is a specialized field consisting of the provisions and policies to prevent and monitor unauthorized access, misuse, modification, or denial of a computer network and network-accessible resources as well as ensuring their availability through proper procedures. Many security devices are being developed and deployed to defend against cyber threats and to prevent unintended data breaches. In spite of all these efforts, the ‘golden age’ of cyber crime continues, as organizations around the world continue to suffer data breaches and security attacks. What kinds of threats we are facing today? How these threats are to be dealt with? The goal of this paper is to communicate an updated perspective of network security for organizations, and researchers in the field and present some recommendations to tackle the current situation of security threats.

AI based supervised classifiers: an analysis for intrusion detection

Researchers investigated Artificial Intelligence (AI) based classifiers for intrusion detection to cope the weaknesses of knowledge based systems. AI based classifiers can be utilized in supervised and unsupervised mode. Here, we perform a blind set of experiments to compare & evaluate performance of the supervised classifiers by their categories using variety of metrics. The performance of the classifiers is analyzed using subset of benchmarked KDD cup 1999 dataset as training & Test dataset. This work has significant aspect of using variety of performance metrics to evaluate the supervised classifiers because some classifiers are designed to optimize some specific metric. This empirical analysis is not only a comparison of various classifiers to identify best classifier on the whole and best classifiers for individual attack classes, but also reveals guidelines for researchers to apply AI based classifiers to field of intrusion detection and directions for further research in this field.

A novel evaluation function for feature selection based upon information theory

Feature selection methods play a significance role during classification of data having high dimensions of features. The feature selection methods select most relevant subset of features that describe data appropriately. Mutual Information (MI) based upon information theory is one of metric used for measuring relevance of features. This paper analyzes various feature selection methods based upon MI for (1) Different evaluation function; (2) Consideration of redundancy relevance and class conditional interaction information for measuring net relevance of features. Various research gaps identified are: (1) Computation of MI from the whole sample space instead of unclassified sample subspace. (2) Consideration of relevance of features only or tradeoff between relevance & redundancy, but class conditional interaction of features is ignored. In this paper, we propose a novel generalized evaluation function using MI for feature selection. The proposed evaluation function measures the net relevance of candidate feature as linear combination of relevance, redundancy and class conditional interaction information. The proposed evaluation function is based on the principle of maximal relevance, minimal redundancy and maximal interaction information of features.

Morphological and biodegradability studies of Euphorbia latex modified polyester – Banana fiber composites

The composites of Banana fiber were prepared using polyester resin blended Euphorbia coagulum, morphology and the degree of rate of aerobic biodegradation of the prepared composites were studied. Polyester resin blended Euphorbia coagulum containing Banana fiber, Euphorbia coagulum and polyester resin taken in the ratio 40: 24: 36 was used for the study, which was the optimum composition of the composite reported in a previous study by the authors. In the biodegradability study cellulose has been used as positive reference material. Result shows that Euphorbia coagulum modified polyester – Banana fiber composites exhibited biodegradation to the extent of around 40%. The use of developed green composites may help in reducing the generation of non-biodegradable polymeric wastes.

Cybercrimes: A Proposed Taxonomy and Challenges

Cybersecurity is one of the most important concepts of cyberworld which provides protection to the cyberspace from various types of cybercrimes. This paper provides an updated survey of cybersecurity. We conduct the survey of security of recent prominent researches and categorize the recent incidents in context to various fundamental principles of cybersecurity. We have proposed a new taxonomy of cybercrime which can cover all types of cyberattacks. We have analyzed various cyberattacks as per the updated cybercrime taxonomy to identify the challenges in the field of cybersecurity and highlight various research directions as future work in this field.

A study on effect of ATH on Euphorbia coagulum modified polyester banana fiber composite

Fiber reinforced polymer composites are used for building and structural applications due to their high strength. In conventional composites both the binder and the reinforcing fibers are synthetic or either one of the material is natural. In the present study coagulum of Euphorbia royleana has been used for replacing polyester resinas binder in polyester banana composite. Euphorbia coagulum (driedlatex) is rich in resinous mass (60-80%), which are terpenes and polyisoprene (10-20%). Effect of varying percentage of coagulum content on various physico-mechanical properties of polyester-banana composites has been studied. Since banana fiber is sensitive to water due to presence of polar group, banana composite undergoes delamination and deterioration under humid condition. Alkali treated banana fiber along with coagulum content has improved overall mechanical properties and reduction in water absorption. The best physico-mechanical properties have been achieved on replacing 40% of polyester resin by coagulu...