Rapid growth in the Internet usage and diverse military applications have led researchers to think of intelligent systems that can assist the users and applications in getting the services by delivering required quality of service in networks. Some kinds of intelligent techniques are appropriate for providing security in communication pertaining to distributed environments such as mobile computing, e-commerce, telecommunication, and network management. In this paper, a survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence has been proposed. These techniques have been useful for effectively identifying and preventing network intrusions in order to provide security to the Internet and to enhance the quality of service. In addition to the survey on existing intelligent techniques for intrusion detection systems, two new algorithms namely intelligent rule-based attribute selection algorithm for effective feature selection and intelligent rule-based enhanced multiclass support vector machine have been proposed in this paper.Keywords: Survey; Intrusion detection system; Neural networks; Fuzzy systems; Swarm intelligence; Particle swarm intelligence Review Intrusion detection systemsRecently, Internet has become a part and parcel of daily life. The current internet-based information processing systems are prone to different kinds of threats which lead to various types of damages resulting in significant losses. Therefore, the importance of information security is evolving quickly. The most basic goal of information security is to develop defensive information systems which are secure from unauthorized access, use, disclosure, disruption, modification, or destruction. Moreover, information security minimizes the risks related to the three main security goals namely confidentiality, integrity, and availability.Various systems have been designed in the past to identify and block the Internet-based attacks. The most important systems among them are intrusion detection systems (IDS) since they resist external attacks effectively. Moreover, IDSs provide a wall of defense which overcomes the attack of computer systems on the Internet. IDS could be used to detect different types of attacks on network communications and computer system usage where the traditional firewall cannot perform well. Intrusion detection is based on an assumption that the behavior of intruders differ from a legal user [1]. Generally, IDSs are broadly classified into two categories namely anomaly and misuse detection systems based on their detection approaches [2,3]. Anomaly intrusion detection determines whether deviation from the established normal usage patterns can be flagged as intrusions. On the other hand, misuse detection systems detect the violations of permissions effectively. Intrusion detection systems can be built by u...
Purpose Previous studies that have attempted to link TQM and employees’ satisfaction are either theoretical without empirical evidence or had limited outcome in scope as they link only few elements of TQM with employees’ job satisfaction and commitment. This study is warranted due to the paucity of insights into the impact of soft strategies on determining job satisfaction and commitment. Despite the considerable body of TQM literature that has evolved to examine the relationship between TQM and employees’ job satisfaction in various countries as well as industries there is no existing literature that recognizes the soft aspects of TQM within the context of the Indian manufacturing industry. The paper aims to discuss these issues. Design/methodology/approach On the basis of the proposed hypotheses a conceptual model was proposed and tested. A questionnaire survey was employed for data collection. The participants were 450 shop floor employees of three Indian manufacturing organizations. Findings The results have shown that six out of the nine soft aspects of TQM played a role in determining job satisfaction and commitment. The results have also shown that the predictors of both job satisfaction and commitment were the same except for the strength of prediction. The proposed model showed an acceptable fit. Originality/value This is the first study to examine the impact of soft aspects of TQM in determining job satisfaction and commitment in the Indian manufacturing organizations.
Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set.
In this paper, we propose a new pattern classification system by combining Temporal features with Fuzzy Min-Max (TFMM) neural network based classifier for effective decision support in medical diagnosis. Moreover, a Particle Swarm Optimization (PSO) algorithm based rule extractor is also proposed in this work for improving the detection accuracy. Intelligent fuzzy rules are extracted from the temporal features with Fuzzy Min-Max neural network based classifier, and then PSO rule extractor is used to minimize the number of features in the extracted rules. We empirically evaluated the effectiveness of the proposed TFMM-PSO system using the UCI Machine Learning Repository Data Set. The results are analysed and compared with other published results. In addition, the detection accuracy is validated by using the tenfold cross validation.
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