Detection and Prevention of Routing Attacks in Internet of Things

Choudhary, Sarika; Kesswani, Nishtha

doi:10.1109/trustcom/bigdatase.2018.00219

Cited by 29 publications

(6 citation statements)

References 8 publications

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“…Relied on the estimation of mutual information gain, a significant performance was achieved. Key-based and clustering-based algorithms were introduced to recognize the routing attacks under IoT networks [17]. The deployment of a cluster based approach [18] has significantly improved the detection performance on similar attacks.…”

Section: Machine Learning Oriented Idfsmentioning

confidence: 99%

Intelligent Intrusion Detection Framework for Multi-Clouds – Iot Environment Using Swarm-Based Deep Learning Classifier

Nizamudeen¹

2023

Preprint

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In current era, a tremendous volume of data has been generated by the use of web technologies. The association between different devices and services have also been explored to wisely and widely use recent technologies. Due to the restriction in the available resources, the chance of security violation is increasing highly on the constrained devices. IoT backend with the multi-cloud infrastructure to extend the public services in terms of better scalability and reliability. Several users might access the multi-cloud resources that lead to data threats while handling user requests for IoT services. It poses a new challenge in proposing new functional elements and security schemes. In this paper, an intelligent Intrusion Detection Framework (IDF) is introduced to detect network and application-based attacks. The proposed framework has three phases: data pre-processing, feature selection and classification. Initially, the collected datasets are pre-processed using Integer- Grading Normalization (I-GN) technique that ensures a fair-scaled data transformation process. Secondly, Opposition-based Learning- Rat Inspired Optimizer (OBL-RIO) is designed for the feature selection phase. The progressive nature of rats chooses the significant features. The fittest value ensures the stability of the features from OBL-RIO. Finally, a 2D-Array-based Convolutional Neural Network (2D-ACNN) is proposed as the binary class classifier. The input features are preserved in a 2D-array model to perform on the convoluted set of layers. It detects the normal (or) abnormal traffic. The proposed framework is trained and tested on the Netflow-based datasets. The proposed framework yields 95.20% accuracy, 2.5% false positive rate and 97.24% detection rate.

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Section: Machine Learning Oriented Idfsmentioning

confidence: 99%

Intelligent Intrusion Detection Framework for Multi-Clouds – Iot Environment Using Swarm-Based Deep Learning Classifier

Nizamudeen¹

2023

Preprint

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“…In a replay attack, a malicious node sends a signed packet to the destination numerous times to keep the network busy [17]. Routing information attacks, in which an attacker creates a route or continues to transmit error messages by altering the routing information [18], Table 1 summarize part of physical and network attacks.…”

Section: Related Workmentioning

confidence: 99%

A Cooperative Smart Jamming Attack in Internet of Things Networks

Sharah

Owida

Edwan

et al. 2022

JICCE

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The emerging scope of the Internet-of-Things (IoT) has piqued the interest of industry and academia in recent times. Therefore, security becomes the main issue to prevent the possibility of cyberattacks. Jamming attacks are threads that can affect performance and cause significant problems for IoT device. This study explores a smart jamming attack (coalition attack) in which the attackers were previously a part of the legitimate network and are now back to attack it based on the gained knowledge. These attackers regroup into a coalition and begin exchanging information about the legitimate network to launch attacks based on the gained knowledge. Our system enables jammer nodes to select the optimal transmission rates for attacks based on the attack probability table, which contains the most probable link transmission rate between nodes in the legitimate network. The table is updated constantly throughout the life cycle of the coalition. The simulation results show that a coalition of jammers can cause highly successful attacks.

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“…Various approaches were developed earlier to ensure a secured communication between nodes of IoT. In the starting of research over security in IoT, the authors focused on cryptography based algorithms such as key matching and hashing [6]. However, the main problem was a huge computational complexity due to a number of mathematic computations.…”

Section: Literature Surveymentioning

confidence: 99%

LWTSM-IoT: Light Weight Trust Sensing Mechanism for Internet of Things

Prasad¹,

Reddy²

2021

IJIES

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In Internet of Things (IoT), secure communication is a prime concern since the open internet source and vast heterogeneity offers several challenges to the network. To achieve an enhanced security, an effective trust evaluation model is required through which the abnormal nodes can be detected and isolated. Towards this objective we have proposed a Light Weight Trust Sensing (LWTS) mechanism for IoT routing. Several factors like Packet Forwarding Factor, Packet Consistency Factor and Packet Repetition Factor are employed to analyze the behaviour of IoT nodes. Along with these factors, the proposed model also checks for energy efficiency to achieve an improved network lifetime. Trust Calculation process is accomplished in two phases; they are direct and indirect fashion. Finally based on obtained total trust, each neighbour node are categorized as No Trust, Average Trust, Fair Trust and Good Trust and the node with good trust is selected as next-hop forwarding node. For the proposed approach extensive simulations are carried out and the performance is measured through Packet Delivery Ratio, Malicious Detection Rate and Average Energy Consumption. The obtained results prove the effectiveness when compared to existing approaches.

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Detection and Prevention of Routing Attacks in Internet of Things

Cited by 29 publications

References 8 publications

Intelligent Intrusion Detection Framework for Multi-Clouds – Iot Environment Using Swarm-Based Deep Learning Classifier

Intelligent Intrusion Detection Framework for Multi-Clouds – Iot Environment Using Swarm-Based Deep Learning Classifier

A Cooperative Smart Jamming Attack in Internet of Things Networks

LWTSM-IoT: Light Weight Trust Sensing Mechanism for Internet of Things

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