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Department of Software Engineering, In the current application development strategies, families of productsare developed with personalized configurations to increase stakeholders’ satisfaction. Product lines have theability to address several requirements due to their reusability and configuration properties. The structuringand prioritizing of configuration requirements facilitate the development processes, whereas it increases theconflicts and inadequacies. This increases human effort, reducing user satisfaction, and failing to accommodatea continuous evolution in configuration requirements. To address these challenges, we propose a framework formanaging the prioritization process considering heterogeneous stakeholders priority semantically. Featuresare analyzed, and mined configuration priority using the data mining method based on frequently accessed andchanged configurations. Firstly, priority is identified based on heterogeneous stakeholder’s perspectives usingthree factors functional, experiential, and expressive values. Secondly, the mined configuration is based on frequentlyaccessed or changed configuration frequency to identify the new priority for reducing failures or errorsamong configuration interaction. We evaluated the performance of the proposed framework with the help ofan experimental study and by comparing it with analytical hierarchical prioritization (AHP) and Clustering.The results indicate a significant increase (more than 90 percent) in the precision and the recall value of theproposed framework, for all selected cases.

Malicious node detection in vehicular ad hoc network (VANET) has always been a research hot spot. An efficient misbehavior detection scheme is needed to avoid and reduce the factor of selfishness and maliciousness especially in the case where the selfish beacons exploit the medium. To disseminate honest data over VANET infrastructure, it is very essential for nodes to collaborate with each other during the process of message forwarding and to ensure the successful delivery of honest data over the network. However, using the fake identities in message forwarding process easily results in the dissemination forged data over the network. Therefore, most of the existing techniques for detection of misbehavior in VANET use collaborative-trust–reputation-based approaches to tackle the issue forged and fake data transmission. The objective of this research is to calculate the trust weightages of each vehicle over the network and to reduce the intensity of the malicious vehicular nodes in VANET. The proposed collaboration-based maliciousness detection mechanism comprises data trust module and reputation calculating module, which guarantees honest data communication and reduces the false positive rate of malicious vehicular nodes. The data trust module uses trust evaluation and reputation calculation model to decide whether the vehicle is trustworthy using vehicular behavior vector in the context of packet transmission. The vehicular trust authority uses collaborative approach to integrate several trust evaluation assessments about a particular vehicular node and formulate a complete trust assessment. The performance evaluation shows that the proposed scheme delivers more priority messages with high true positive rate and low false positive rate. Moreover, the experimental results show that Gaussian kernel function best suits our proposed model in comparison with other rationalities. In addition, proposed models are more vigorous in context of true positive rate than the existing schemes, i.e., Dempster–Shafer theory of evidence, majority voted model and Bayesian inference.

In critical domains such as medical and military, reversible watermarking (RW) has been used. In the medical domain, different modalities are used to store patient information. The current study focuses on the application of RW in echocardiography data. Mostly, RW is applied to protect patient data without affecting the quality of the decoded image. The RW methods are benchmarked as per imperceptibility, robustness, and payload. The survey presents a comparison of state-of-the-art RW techniques. The imperceptibility and payload are balanced through a tradeoff. It has been observed in the literature that most of the RW methods lack robustness, and very small-scale robustness has been achieved in this domain of watermarking. Different types of RW, i.e., fragile, semifragile, and robust methods, are being compared and reviewed. Mostly, fragile methods are developed on the error-expansion techniques built on histogram shifting-based approach. In this study, several RW methods are compared and the results are presented.

Widespread use of numerous hand-held smart devices has opened new avenues in computing. Internet of things (IoT) is the next big thing resulting in the 4th industrial revolution. Coupling IoT with data collection, storage, and processing leads to Internet of everything (IoE). This work outlines the concept of smart device and presents an IoE ecosystem. Characteristics of IoE ecosystem with a review of contemporary research is also presented. A comparison table contains the research finding. To realize IoE, an object-oriented context aware model is presented. This model is based on Unified Modelling Language (UML). A case study of a museum guide system is outlined that discusses how IoE can be implemented. The contribution of this chapter includes review of contemporary IoE systems, a detailed comparison, a context aware IoE model, and a case study to review the concepts.

A major drawback of signature-based intrusion detection systems is the inability to detect novel attacks that do not match the known signatures already stored in the database. Anomaly detection is a kind of intrusion detection in which the activities of a system are monitored and these activities are classified as normal or anomalous based on their expected behavior. Tree-based classifiers have been successfully used to separate the abnormal behavior from the normal one. Tree pruning is a machine learning technique used to minimize the size of a decision tree (DT) in order to reduce the complexity of the classifier and improve its predictive accuracy. In this paper, we attempt to prune a DT using particle swarm optimization (PSO) algorithm and apply it to the network intrusion detection problem. The proposed technique is a hybrid approach in which PSO is used for node pruning and the pruned DT is used for classification of the network intrusions. Both single and multi-objective PSO algorithms are used in the proposed approach. The experiments are carried out on the well-known KDD99Cup dataset. This dataset has been widely used as a benchmark dataset for network intrusion detection problems. The results of the proposed technique are compared to the other state-of-the-art classifiers and it is observed that the proposed technique performs better than the other classifiers in terms of intrusion detection rate, false positive rate, accuracy, and precision.

During the past few years, Internet has become a public platform for communication and exchange of information online. The increase in network usage has increased the chance of network attacks. In order to detect the malicious activities and threats, several kinds of Intrusion Detection Systems (IDSs) have been designed over the past few years. The goal of IDS is to intelligently monitor events occurring in a computer system or a network and analyze them for any sign of violation of the security policy as well as retain the availability, integrity, and confidentiality of a network information system. An IDS may be categorized as anomaly detection system or misuse detection system. Anomaly detection systems usually apply statistical or Artificial Intelligence (AI) techniques to detect attacks; therefore, these systems have the ability to detect novel or unknown attacks. A misuse detection system uses signature-based detection; therefore, these systems are good at identifying already known attacks but cannot detect unknown attacks.

Network security risks grow with increase in the network size. In recent past, the attacks on computer networks have increased tremendously and require efficient network intrusion detection mechanisms. Data mining and machine-learning techniques have been used for network intrusion detection during the past few years and have gained much popularity. In this paper, we propose an intrusion detection mechanism based on binary particle swarm optimization PSO and random forests RF algorithms called PSO-RF and investigate the performance of various dimension reduction techniques along with a set of different classifiers including the proposed approach. Binary PSO is used to find more appropriate set of attributes for classifying network intrusions, and RF is used as a classifier. In the preprocessing step, we reduce the dimensions of the dataset by using different state-of-the-art dimension reduction techniques, and then this reduced dataset is presented to the proposed PSO-RF approach that further optimizes the dimensions of the data and finds an optimal set of features. PSO is an optimization method that has a strong global search capability and is used here for dimension optimization. We perform extensive experimentation to prove the worth of the proposed approach by using different performance metrics. The standard benchmark, that is, KDD99Cup dataset, is used that contains the information about various kinds of network intrusions. The experimental results indicate that the proposed approach performs better than the other approaches for the detection of all kinds of attacks present in the dataset. Copyright © 2012 John Wiley & Sons, Ltd.

Threats to computer networks are numerous and potentially devastating. Intrusion detection techniques provide protection to our data and track unauthorized access. Many algorithms and techniques have been proposed to improve the accuracy and minimize the false positive rate of the intrusion detection system (IDS). Statistical techniques, evolutionary techniques, and data mining techniques have also been used for this purpose. In this paper, we use a centroid-based technique for network intrusion detection in which the centroid is constructed on the basis of diversity. Diversity of a point is the sum of the distances from a point to all other points in a cluster. The point having minimum diversity is chosen as a centroid. The performance of diversity-based centroid shows significant improvement in the classification of intrusions. Experimental results on the KDDCup99 dataset demonstrate that the proposed method shows excellent performance in terms of accuracy, detection rate, and false positive rate.