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A Survey of Intrusion Detection in Wireless Network Applications
"... Information systems are becoming more integrated into our lives. As this integration deepens, the importance of securing these systems increases. Because of lower installation and maintenance costs, many of these systems are largely networked by wireless means. In order to identify gaps and propose ..."
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Information systems are becoming more integrated into our lives. As this integration deepens, the importance of securing these systems increases. Because of lower installation and maintenance costs, many of these systems are largely networked by wireless means. In order to identify gaps and propose research directions in wireless network intrusion detection research, we survey the literature of this area. Our approach is to classify existing contemporary wireless intrusion detection system (IDS) techniques based on target wireless network, detection technique, collection process, trust model and analysis technique. We summarize pros and cons of the same or different types of concerns and considerations for wireless intrusion detection with respect to specific attributes of target wireless networks including wireless local area networks (WLANs), wireless personal area networks (WPANs), wireless sensor networks (WSNs), ad hoc networks, mobile telephony, wireless mesh networks (WMNs) and cyber physical systems (CPSs). Next, we summarize the most and least studied wireless IDS techniques in the literature, identify research gaps, and analyze the rationale for the degree of their treatment. Finally, we identify worthy but little explored topics and provide suggestions for ways to conduct research.
Theoretical Formulation and Analysis of the Deterministic Dendritic Cell Algorithm
"... As one of the emerging algorithms in the field of Artificial Immune Systems (AIS), the Dendritic Cell Algorithm (DCA) has been successfully applied to a number of challenging realworld problems. However, one criticism is the lack of a formal definition, which could result in ambiguity for understan ..."
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As one of the emerging algorithms in the field of Artificial Immune Systems (AIS), the Dendritic Cell Algorithm (DCA) has been successfully applied to a number of challenging realworld problems. However, one criticism is the lack of a formal definition, which could result in ambiguity for understanding the algorithm. Moreover, previous investigations have mainly focused on its empirical aspects. Therefore, it is necessary to provide a formal definition of the algorithm, as well as to perform runtime analyses to reveal its theoretical aspects. In this paper, we define the deterministic version of the DCA, named the dDCA, using set theory and mathematical functions. Runtime analyses of the standard algorithm and the one with additional segmentation are performed. Our analysis suggests that the standard dDCA has a runtime complexity of O(n2) for the worstcase scenario, where n is the number of input data instances. The introduction of segmentation changes the algorithm’s worst case runtime complexity to O(max(nN, nz)), for DC population size N with size of each segment z. Finally, two runtime variables of the algorithm are formulated based on the input data, to understand its runtime behaviour as guidelines for further development.
Quiet in Class: Classification, Noise and the Dendritic Cell Algorithm
"... Abstract. Theoretical analyses of the Dendritic Cell Algorithm (DCA) have yielded several criticisms about its underlying structure and operation. As a result, several alterations and fixes have been suggested in the literature to correct for these findings. A contribution of this work is to investi ..."
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Abstract. Theoretical analyses of the Dendritic Cell Algorithm (DCA) have yielded several criticisms about its underlying structure and operation. As a result, several alterations and fixes have been suggested in the literature to correct for these findings. A contribution of this work is to investigate the effects of replacing the classification stage of the DCA (which is known to be flawed) with a traditional machine learning technique. This work goes on to question the merits of those unique properties of the DCA that are yet to be thoroughly analysed. If none of these properties can be found to have a benefit over traditional approaches, then “fixing ” the DCA is arguably less efficient than simply creating a new algorithm. This work examines the dynamic filtering property of the DCA and questions the utility of this unique feature for the anomaly detection problem. It is found that this feature, while advantageous for noisy, timeordered classification, is not as useful as a traditional static filter for processing a synthetic dataset. It is concluded that there are still unique features of the DCA left to investigate. Areas that may be of benefit to the Artificial Immune Systems community are suggested. 1
Costimulation and Priming: Can it Help Protect Ad Hoc Wireless Networks?
"... Abstract—We review two key mechanisms of the Biological immune system (BIS): costimulation and priming. Then we explain the relevance of these two mechanisms for misbehavior detection in ad hoc wireless networks. We argue that costimulation and priming can not only increase the reliability and robus ..."
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Abstract—We review two key mechanisms of the Biological immune system (BIS): costimulation and priming. Then we explain the relevance of these two mechanisms for misbehavior detection in ad hoc wireless networks. We argue that costimulation and priming can not only increase the reliability and robustness of misbehavior detection but also help increase the energy efficiency. We conclude by giving an outlook on future research related to the design of security protocols inspired by the efficiency of the BIS. I.
Theoretical Formulation and Analysis of the Deterministic Dendritic Cell Algorithm
, 2013
"... As one of the emerging algorithms in the field of Artificial Immune Systems (AIS), the Dendritic Cell Algorithm (DCA) has been successfully applied to a number of challenging realworld problems. However, one criticism is the lack of a formal definition, which could result in ambiguity for understan ..."
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As one of the emerging algorithms in the field of Artificial Immune Systems (AIS), the Dendritic Cell Algorithm (DCA) has been successfully applied to a number of challenging realworld problems. However, one criticism is the lack of a formal definition, which could result in ambiguity for understanding the algorithm. Moreover, previous investigations have mainly focused on its empirical aspects. Therefore, it is necessary to provide a formal definition of the algorithm, as well as to perform runtime analyses to reveal its theoretical aspects. In this paper, we define the deterministic version of the DCA, named the dDCA, using set theory and mathematical functions. Runtime analyses of the standard algorithm and the one with additional segmentation are performed. Our analysis suggests that the standard dDCA has a runtime complexity of O(n2) for the worstcase scenario, where n is the number of input data instances. The introduction of segmentation changes the algorithm’s worst case runtime complexity to O(max(nN, nz)), for DC population size N with size of each segment z. Finally, two runtime variables of the algorithm are formulated based on the input data, to understand its runtime behaviour as guidelines for further development.