An Intrusion Detection System for Wireless Sensor Networks

Ioannou, Christiana; Vassiliou, Vasos; Sergiou, Charalampos

doi:10.1109/ict.2017.7998271

Cited by 48 publications

(28 citation statements)

References 12 publications

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“…For the current work we aim to evaluate BLR as a detection technique for Selective Forward and Blackhole routing layer attacks as these are described in previous works [3]- [5]. Selective Forward and Blackhole attacks aim in exploiting routing vulnerabilities and reroute traffic to the compromised node.…”

Section: A Network Routing Layer Attacksmentioning

confidence: 99%

“…The training phase determines whether the method can be used to identify malicious activity and which independent variables are the most significant. The end result of the training phase is a regression model to be used in the evaluation phase [5]. The evaluation phase tests the model at real-time.…”

Section: Mids Detection Modulesmentioning

confidence: 99%

“…Each x i represents the value of the i th independent variable, α is a constant, β is the regression coefficient. For more information about BLR refer to [5] and references therein. For the training phase, training sets were created using the independent variables gathered by RMT in the network topology were the Sink node is placed in the middle.…”

Section: Mids Detection Modulesmentioning

confidence: 99%

“…We used the energy equation shown in (4) which is also used in [3] and applied the Tmote Sky operating conditions shown in Table VII. To derive the power consumption we used equation (5) taken from [3]. in mJ and the power in mW of our two scenarios.…”

Section: B Mids Overheadmentioning

confidence: 99%

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An Intrusion Detection System for Constrained WSN and IoT Nodes Based on Binary Logistic Regression

Ioannou

Vassiliou

2018

Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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In this paper we evaluate the feasibility of running a lightweight Intrusion Detection System within a constrained sensor or IoT node. We propose mIDS, which monitors and detects attacks using a statistical analysis tool based on Binary Logistic Regression (BLR). mIDS takes as input only local node parameters for both benign and malicious behavior and derives a normal behavior model that detects abnormalities within the constrained node. We offer a proof of correct operation by testing mIDS in a setting where network-layer attacks are present. In such a system, critical data from the routing layer is obtained and used as a basis for profiling sensor behavior. Our results show that, despite the lightweight implementation, the proposed solution achieves attack detection accuracy levels within the range of 96%-100%.

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Section: A Network Routing Layer Attacksmentioning

confidence: 99%

Section: Mids Detection Modulesmentioning

confidence: 99%

Section: Mids Detection Modulesmentioning

confidence: 99%

Section: B Mids Overheadmentioning

confidence: 99%

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An Intrusion Detection System for Constrained WSN and IoT Nodes Based on Binary Logistic Regression

Ioannou

Vassiliou

2018

Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

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“…Therefore, customization of the hardware, software, and firmware to suit the targeted application of this domain were the prime focus of this research. With the evolving advancement and cost reduction in the constituent of the WSN including 1) cost of sensors 2) sensor nodes 3) bandwidth as well as 4) communication protocols, the application of WSN has moved to various fields such as 1) structural health monitoring (SHM), 2) intrusion detection system (IDS), 3) critical climate change monitoring system (CCCM), 4) environmental monitoring, 5) habitat monitoring and many more pervasive and ubiquitous based applications in the field of healthcare, industry, monitoring of micro-grid station and today smart applications using WSN as sub-system of IoT [2][3][4][5][6][7][8][9]. These applications are quite useful for the population exploring the world to meet the vision of industry 4.0 [10], but at the same time, the scalability and the popularity of WSN make it vulnerable, poses its kind of security threats, risk, and challenges.…”

Section: Introductionmentioning

confidence: 99%

Evolution of Cryptographic Algorithm for Resource Constrained Wireless Networks: An Investigation Approach

Tejashwini¹,

R.²,

Satyanarayana³

2018

CAE

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In the era of the internet of things, provisioning suitable security models for small and resource constrained nodes as well innetwork requires consideration of synchronization among heterogeneous operating conditions. This paper provides an investigational study of traditional approaches adopted for resource constrained wireless networks especially Wireless Sensor Network (WSN). The inferring of basic assumptions to propose customized algorithms is discussed to provide an insight into the development of suitable cryptographic and security approaches for future generation WSN that will be a subsystem of Internet-of-Things (IoT). The methodology adopted for investigation includes consideration of recent state of art work published in archival journals including IEEE, Springer, Elsevier, and special edition journals. Initially, approximately 400 plus journals are referred and later articles from various sources since 2015 is considered for an intrinsic explanation of algorithmic consideration, its pros, and cons with a citation to show the potential of its evolution. The outcomes of the paper as statistics, concept, and classification are useful for security specialist researchers and academicians.

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