SCEER: Secure cluster based efficient energy routing scheme for wireless sensor networks

Gopinath, S.; Kumar, Krishan; Elayaraja, P.; Parameswari, A.; Balakrishnan, Shobana; Thiruppathi, M.

doi:10.1016/j.matpr.2020.12.1096

Cited by 23 publications

(16 citation statements)

References 2 publications

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“…Kumar and Mohan [7] employed a Deep Learning model based on CNN to analyze network traffic patterns and identify the presence of low-rate attacks. Li et al [8] presented a Deep Learning model based on a combination of CNNs and LSTM networks to analyze network traffic data and identify low-rate DDoS attacks. Tab.…”

Section: Related Workmentioning

confidence: 99%

Detecting and Mitigating Low-Rate DoS and DDoS Attacks: Multimodal Fusion of Time-Frequency Analysis and Deep Learning model

2024

Teh. vjesn.

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This paper outlines a method for identifying and counteracting distributed denial of service (DDoS) and low-rate denial of service (DoS) attacks. These impair significant threats to network security and can disrupt the accessibility and efficacy of systems under attack. The proposed method combines Time-Frequency Analysis (TFA) using Short-Time Fourier Transform (STFT) and a Deep Learning model (DLM), namely Recurrent Neural Network (RNN), to enhance network security. By leveraging the strengths of STFT and RNN, the approach achieves improved detection capabilities and enables timely response and effective mitigation. The CICDDoS2019 dataset has been employed to conduct the evaluation, which provides a diverse set of realistic attack traffic scenarios. The results show that the proposed approach is effective, with an impressive accuracy rate of 99.1%. Compared to traditional methods, the integrated achieves higher accuracy and lower false positive rates. This research highlights the potential of Multimodal Fusion method, for addressing the growing need for advanced defense mechanisms in today's evolving threat landscape.

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Section: Related Workmentioning

confidence: 99%

Detecting and Mitigating Low-Rate DoS and DDoS Attacks: Multimodal Fusion of Time-Frequency Analysis and Deep Learning model

2024

Teh. vjesn.

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“…In this work, a BC assisted Smart Contract protocol is used to distribute resource allocation, enforcing the clients to reach an agreement on the wireless channel utilization. BC is a series of pre-determined data types that poses a number of encrypted transactions in a distributed ledger [21]. To receive or send messages that comprise transactions (or other kinds of dataset), a user must be linked to the BC and need to download local copies of BC.…”

Section: Block Chain Based Smart Contractsmentioning

confidence: 99%

“…Since an extension of a cloud at network edge. Applications of the IoT patterns become the inspiration of the FC outline since many applications need short response time and real-time services [7]. The reason behind this was Things were resource limited (that is computation power, energy, and storage,).…”

Section: Introductionmentioning

confidence: 99%

Block Chain Technology Assisted Privacy Preserving Resource Allocation Scheme for Internet of Things Based Cloud Computing

2023

Teh. vjesn.

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Resource scheduling in cloud environments is a complex task, as it involves allocating suitable resources based on Quality of Service (QoS) requirements. Existing resource allocation policies face challenges due to resource dispersion, heterogeneity, and uncertainty. In this research, the authors propose a novel approach called Quasi-Oppositional Artificial Jellyfish Optimization Algorithm (QO-AJFOA) for resource scheduling in cloud computing (CC) environments. The QO-AJFOA model aims to optimize the allocation of computing power and bandwidth resources in servers, with the goal of maximizing long-term utility. The technique combines quasi-oppositional based learning (QOBL) with traditional AJFOA. Additionally, a blockchain-assisted Smart Contract protocol is used to distribute resource allocation, ensuring agreement on wireless channel utilization. Experimental validation of the QO-AJFOA technique demonstrates its promising performance compared to recent models, as tested with varying numbers of tasks and iterations. The proposed approach addresses the challenges of resource scheduling in cloud environments and contributes to the existing literature on resource allocation policies.

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“…Since the sensors are installed without any security, it is susceptible to malicious attacks [15]. It is difficult to increase the sensors' energy, due to naturally unsuitable conditions of the environment, hence it is required to develop a secure and energy aware routing for the network [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Secure cluster-based routing using multi objective-trust centric artificial algae algorithm for wireless sensor network

Balachandra

Gowda

Shivaprasad³

2023

IJECE

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<p>Nowadays, wireless sensor network (WSN) is developed as a key technology to observe and track applications over a wide range. However, energy consumption and security are considered as important issues in the WSN. In this paper, the multi objective-trust centric artificial algae algorithm (M-TCAAA) is proposed to accomplish a secure broadcasting over the WSN. The proposed M-TCAAA is used to choose the secure cluster head (SCH) as well as routing path, based on the distinct fitness measures such as trust, communication cost, residual energy, and node degree. Hence, the M-TCAAA is used to ensure a secure data transmission while decreasing the energy consumed by the nodes. The performance of the M-TCAAA is analyzed by means of energy consumption, packet delivery ratio (PDR), throughput, end to end delay (EED), normalized routing load (NRL), and network lifetime. The existing researches namely energy aware trust and opportunity-based routing with mobile nodes (ETOR-MN), grey wolf updated whale optimization (GUWO), secure cluster-based routing protocol (SCBRP), secure routing protocol based on multi-objective ant-colony-optimization (SRPMA) and multi objective trust aware hybrid optimization (MOTAHO) are considered for evaluating the M-TCAAA. The PDR of the M-TCAAA for 100 nodes is 99.87%, which is larger than the ETOR-MN, GUWO, SRPMA and MOTAHO.</p>

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SCEER: Secure cluster based efficient energy routing scheme for wireless sensor networks

Cited by 23 publications

References 2 publications

Detecting and Mitigating Low-Rate DoS and DDoS Attacks: Multimodal Fusion of Time-Frequency Analysis and Deep Learning model

Detecting and Mitigating Low-Rate DoS and DDoS Attacks: Multimodal Fusion of Time-Frequency Analysis and Deep Learning model

Block Chain Technology Assisted Privacy Preserving Resource Allocation Scheme for Internet of Things Based Cloud Computing

Secure cluster-based routing using multi objective-trust centric artificial algae algorithm for wireless sensor network

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