An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks

Aruchamy, Prasanth; Gnanaselvi, Sabeena; Sowndarya, Devi; Naveenkumar, Pushpalatha

doi:10.1002/cpe.7818

Cited by 34 publications

(5 citation statements)

References 34 publications

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“…AED is the predominant metric for assessing the energy efficiency of IWSN. It exhibits how much battery energy is depleted by the sensor nodes while participating in the fault diagnosis 36 . Typically, the AED of the network is increased by varying the number of nodes from 100 to 1000.…”

Section: Resultsmentioning

confidence: 99%

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An energy‐aware software fault detection system based on hierarchical rule approach for enhancing quality of service in internet of things‐enabled wireless sensor network

Balraj,

Prasanth

2024

Trans Emerging Tel Tech

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Of late, the Internet of Things (IoT) has progressed in its pervasiveness across the globe for diverse applications. Wireless Sensor Network (WSN) is one of the prominent technologies employed in IoT environments where multiple tiny sensor nodes are distributed to sense real‐time observations about unforeseeable areas for control and managerial purposes. Owing to the presence of sensors in inaccessible regions and their battery restrictions, different types of software faults occur in IoT‐enabled WSNs (IWSNs). These faults create uncertainty in data reading which causes serious damage to the sensor network. Hence, the IWSN necessitates an effective fault‐detection methodology to continue optimal activity despite the existence of software faults. This work proposes a novel Energy‐Aware Hierarchical Rule‐based Software Fault Detection (HRSFD) model to identify various software faults with minimum energy depletion in the IWSN environment. Primarily, the proposed model extracts antecedent attributes from the characteristics of the sensed data. Its abnormal values can be identified based on the obtained antecedent attributes. Subsequently, the category of the software fault is determined by applying a hierarchical rule strategy. Finally, from the simulation results, it is apparent that the fault detection accuracy rate of the proposed HRSFD model attains 99.12% for dense networks. The lifetime of the network is also prolonged by 18% as compared to the existing state‐of‐the‐art models.

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Section: Resultsmentioning

confidence: 99%

“…It exhibits how much battery energy is depleted by the sensor nodes while participating in the fault diagnosis. 36 Typically, the AED of the network is increased by varying the number of nodes from 100 to 1000. The AED can be computed as,…”

Section: Impact Of Aedmentioning

confidence: 99%

An energy‐aware software fault detection system based on hierarchical rule approach for enhancing quality of service in internet of things‐enabled wireless sensor network

Balraj,

Prasanth

2024

Trans Emerging Tel Tech

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“…The analysis of the proposed HCA under three different datasets and two network layouts in terms of Accuracy [28][29][30][31] and mean square error (MSE).…”

Section: Performance Analysis Of the Proposed Hca Modelmentioning

confidence: 99%

An effective cryptanalysis of DES for secure communication using hybrid cryptanalysis and deep neural network

F¹,

Karuppasamy²

2023

Concurrency and Computation

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SummaryIn today's world, information security plays a key role in data storage and communication owing to the modern evolution of digitized data interchange in electronic mode. Cryptography is a widely preferred technique for securing transmitted information by transforming the original text into cipher text. A few cryptographic techniques have the inability to protect the data which are vulnerable to a distinct class of attacks. Therefore, a reliable cryptographic technique is necessitated for enlarging information security. In this paper, a new hybrid cryptanalysis (HCA) model has been proposed to acquire optimal cryptanalysis of data encryption standard (DES). It combines linear cryptanalysis (LCA) and neural cryptanalysis (NCA) for enhancing the performance of the cryptographic system with minimal time complexity. Primarily, the HCA model employs LCA to break cryptographic codes by analyzing linear approximations of the cryptographic algorithm. NCA is then applied with the aid of a deep neural network for identifying patterns in larger datasets. These patterns can be further utilized to break encryption. The efficacy of the proposed HCA model will be assessed through the assessment of three different datasets. The results manifest that the accuracy of the proposed model is increased up to 97.66% and the time needed to break encryption can be reduced.

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“…WSN plays a vital role in the development of the IoT [1], which contains lowcost and low-computing resources. As a result, it is limited by low resource utilization and high energy consumption issues [2], [3]. To overcome these issues, a software-defined network is integrated with the WSN environment, which is known as a software defined wireless sensor network (SD-WSN).…”

Section: Introductionmentioning

confidence: 99%

“…where z P1 i,j represents the current position of the candidate solution in the first process of DTO, O P1 Fi represents the value of objective function, Rand is a random number with the interval of [0, 1], Rand 1 is a random number with the interval of [1,2], I D is the driving instructor matrix, and k i is a number which is randomly selected from {1, 2, . .…”

mentioning

confidence: 99%

Artificial Intelligence-Based Intrusion Detection and Prevention in Edge-Assisted SDWSN With Modified Honeycomb Structure

Kipongo,

Swart,

Esenogho

2024

IEEE Access

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The software-defined wireless sensor network (SDWSN) has the potential to improve flexibility, scalability, and network performance, but security and quality of service (QoS) are major challenges due to attackers, poor network management, and inefficient route selection. Several existing works for intrusion detection had drawbacks like poor security, inefficient network management, higher energy consumption and latency, and lesser throughput. A modified honeycomb structure-based intrusion detection system for SDWSN is proposed to address these challenges, which includes secure authentication using the 3D cube algorithm, modified honeycomb-based network partitioning, clustering, reinforcement learning-based intelligent routing with a transfer learning-based deep Q network (TLDQN), and a hybrid intrusion detection system. The latter detects malicious nodes using a driver training-based optimization (DTO) algorithm and intrusions with a bidirectional generative adversarial network (Bi-GAN). The results show that the proposed system outperforms existing solutions in terms of security, network performance, and efficiency. The simulation of this research is conducted by NS-3.26 Network Simulator, and the performances are evaluated based on various performance metrics (with respect to the total number of nodes) like energy consumption, latency, throughput, packet delivery ratio, network lifetime, computation overhead, detection accuracy, packet drop ratio, and control overhead, which proved that the proposed work achieves superior performance compared to existing works. The evaluation also includes a total simulation period during which the system's real-time performance was conducted. Time-based metrics such as precision, recall, and F1score, as well as confusion matrices, are utilized to analyze the system's effectiveness in real-time in response to dynamic network threats.

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An artificial intelligence approach for energy‐aware intrusion detection and secure routing in internet of things‐enabled wireless sensor networks

Cited by 34 publications

References 34 publications

An energy‐aware software fault detection system based on hierarchical rule approach for enhancing quality of service in internet of things‐enabled wireless sensor network

An energy‐aware software fault detection system based on hierarchical rule approach for enhancing quality of service in internet of things‐enabled wireless sensor network

An effective cryptanalysis of DES for secure communication using hybrid cryptanalysis and deep neural network

Artificial Intelligence-Based Intrusion Detection and Prevention in Edge-Assisted SDWSN With Modified Honeycomb Structure

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