In present days, the utilization of mobile edge computing (MEC) and Internet of Things (IoT) in mobile networks offers a bottleneck in the evolving technological requirements. Wireless Sensors Network (WSN) become an important component of the IoT and is the major source of big data. In IoT enabled WSN, a massive amount of data collection generated from a resource-limited network is a tedious process, posing several challenging issues. Traditional networking protocols offer unfeasible mechanisms for large-scaled networks and might be applied to IoT platform without any modifications. Information-Centric Networking (ICN) is a revolutionary archetype which that can resolve those big data gathering challenges. Employing the ICN architecture for resource-limited WSN enabled IoT networks may additionally enhance the data access mechanism, reliability challenges in case of a mobility event, and maximum delay under multihop communication. In this view, this paper proposes an IoT enabled cluster based routing (CBR) protocol for information centric wireless sensor networks (ICWSN), named CBR-ICWSN. The proposed model undergoes a black widow optimization (BWO) based clustering technique to select the optimal set of cluster heads (CHs) effectively. Besides, the CBR-ICWSN technique involves an oppositional artificial bee colony (OABC) based routing process for optimal selection of paths. A series of simulations take place to verify the performance of the CBR-ICWSN technique and the results are examined under several aspects. The experimental outcome of the CBR-ICWSN technique has outperformed the compared methods interms of network lifetime and energy efficiency.
In today's interconnected era, intrusion detection system (IDS) has the potential to be the frontier of defense against cyberattacks and plays an essential role in achieving security of networking resources and infrastructures. The performance of IDS depends highly on data features. Selecting the most informative features eliminating the redundant and irrelevant features from network traffic data for IDS is still an open research issue. The key impetus of this paper is to identify and benchmark the potential set of features that can characterize network traffic for intrusion detection. In this correspondence, an ensemble approach is proposed. As a first step, the approach applies four different feature evaluation measures, such as correlation, consistency, information, and distance, to select the more crucial features for intrusion detection. Second, it applies the subset combination strategy to merge the output of the four measures and achieve the potential feature set. Along with this, a new framework that adopts the data analytic lifecycle practices is explored to employ the proposed ensemble for building an effective IDS. The effectiveness of the proposed approach is demonstrated by conducting several experiments on four intrusion detection evaluation datasets, namely KDDCup'99, NSL-KDD, UNSW-NB15, and CICIDS2017. The obtained results prove that the proposed approach contributes more potential features compared to the state-of-the-art approaches, leading to achieve a promising performance gain in the detection rate of 3.2%, the false alarm rate of 38%, and the detection time of 12%. Furthermore, ROC and statistical significance are analyzed for the identified feature subset to strongly conform its acceptability as a future benchmark for building an effective IDS.
The revolutionary advances in network technologies have spearheaded the design of advanced cyberattacks to surpass traditional security defense with dreadful consequences. Recently, Intrusion Detection System (IDS) is considered as a pivotal element in network security infrastructures to achieve solid line of protection against cyberattacks. The prime challenges presented to IDS are curse of high dimensionality and class imbalance that tends to increase the detection time and degrade the efficiency of IDS. As a result, feature selection plays an important role in enabling to identify the most significant features for intrusion detection. Although, several feature evaluation measures are being proposed for feature selection in literature, there is no consensus on which measures are best for intrusion detection. Therein, this work aims at recommending the most appropriate feature evaluation measure for building an efficient IDS. In this direction, four filter-based feature evaluation measures that stem from different theories such as Consistency, Correlation, Information and Distance are investigated for their potential implications in enhancing the detection ability of IDS model for different classes of attacks. Along with this, the influence of the selected features on classification accuracy of an IDS model is analyzed using four different categories of classifiers namely, K-nearest neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM) and Deep Belief Network (DBN). Finally, a two-step statistical significance test is conducted on the experimental results to determine which feature evaluation measure contributes statistically significant difference in IDS performance. All the experimental comparisons are performed on two benchmark intrusion detection datasets, NSL-KDD and UNSW-NB15. In these experiments, consistency measure has best influenced the IDS model in improving the detection ability with regard to detection rate (DR), false alarm rate (FAR), kappa statistics (KS) and identifying the most significant features for intrusion detection. Also, from the analysis results, it is revealed that RF is the ideal classifier to be used in conjunction with any of these four feature evaluation measures to achieve better detection accuracy than others. From the statistical results, we recommend the use of consistency measure for designing an efficient IDS in terms of DR and FAR.
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