Edge Computing-Based Intrusion Detection System for Smart Cities Development Using IoT in Urban Areas

Vimal, S.; Suresh, A.; Subbulakshmi, P.; Pradeepa, S.; Kaliappan, M.

doi:10.1007/978-3-030-34328-6_14

Cited by 29 publications

(12 citation statements)

References 45 publications

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“…This works in both the cases as pairwise key as well as groupwise key. In the future, we want to extend our scheme for heterogeneous systems and IoT security [39]. In the future, this technique can be extended to support modern networks, which could involve heterogeneous IoT networks [16].…”

Section: Discussionmentioning

confidence: 99%

Scalable and Storage Efficient Dynamic Key Management Scheme for Wireless Sensor Network

Kumar

Malik

Lohani

2021

Wireless Communications and Mobile Computing

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Recently, there have been exploratory growth in the research of wireless sensor network due to wide applications like health monitoring, environment monitoring, and urban traffic management. Sensor network applications have been used in habitat monitoring, border monitoring, health care, and military surveillance. In some applications, the security of these networks is very essential and need robust support. For a network, it is very important that node in the network trust each other and malicious node should be discarded. Cryptography techniques are normally used to secure the networks. Key plays a very important role in network security. Other aspects of security such as integrity, authentication, and confidentiality also depend on keys. In wireless sensor network, it is very difficult to manage the keys as this includes distribution of key, generation of new session key as per requirements, and renewal or revoke the keys in case of attacks. In this paper, we proposed a scalable and storage efficient key management scheme (SSEKMS) for wireless sensor networks that establish the three types of keys for the network: a network key that is shared by all the nodes in the network, a cluster key shared for a cluster, and pairwise key for each pair of nodes. We analysed the resiliency of the scheme (that is the probability of key compromise against the node capture) and compared it with other existing schemes. SSEKMS is a dynamic key management system that also supports the inclusion of the new node and refreshes the keys as per requirements.

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Section: Discussionmentioning

confidence: 99%

Scalable and Storage Efficient Dynamic Key Management Scheme for Wireless Sensor Network

Kumar

Malik

Lohani

2021

Wireless Communications and Mobile Computing

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“…Moreover, the maintenance cost of multicast forwarding topologies is costlier and requires the application of certain multicast protocol [18]. Some other models available in the literature are [19][20][21].…”

Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Optimization for Cluster‐Based Routing Protocol in Information-Centric Wireless Sensor Networks for IoT Based Mobile Edge Computing

Vaiyapuri

Velmurugan

Sivaram

et al. 2021

Wireless Pers Commun

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In present days, the utilization of mobile edge computing (MEC) and Internet of Things (IoT) in mobile networks offers a bottleneck in the evolving technological requirements. Wireless Sensors Network (WSN) become an important component of the IoT and is the major source of big data. In IoT enabled WSN, a massive amount of data collection generated from a resource-limited network is a tedious process, posing several challenging issues. Traditional networking protocols offer unfeasible mechanisms for large-scaled networks and might be applied to IoT platform without any modifications. Information-Centric Networking (ICN) is a revolutionary archetype which that can resolve those big data gathering challenges. Employing the ICN architecture for resource-limited WSN enabled IoT networks may additionally enhance the data access mechanism, reliability challenges in case of a mobility event, and maximum delay under multihop communication. In this view, this paper proposes an IoT enabled cluster based routing (CBR) protocol for information centric wireless sensor networks (ICWSN), named CBR-ICWSN. The proposed model undergoes a black widow optimization (BWO) based clustering technique to select the optimal set of cluster heads (CHs) effectively. Besides, the CBR-ICWSN technique involves an oppositional artificial bee colony (OABC) based routing process for optimal selection of paths. A series of simulations take place to verify the performance of the CBR-ICWSN technique and the results are examined under several aspects. The experimental outcome of the CBR-ICWSN technique has outperformed the compared methods interms of network lifetime and energy efficiency.

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“…It is mainly composed of an agent, an analysis engine, and a response module. Several IDS-based security solutions were developed in the previous year [15][16][17][18][19][20][21][22][23][24][25]. But, these IDS-based security solutions cannot cope with the MEC architecture due to the changing behavior of users and devices.…”

Section: Problem Formulationmentioning

confidence: 99%

An edge based hybrid intrusion detection framework for mobile edge computing

Singh

Chatterjee

Satapathy

2021

Complex Intell. Syst.

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The Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.

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Edge Computing-Based Intrusion Detection System for Smart Cities Development Using IoT in Urban Areas

Cited by 29 publications

References 45 publications

Scalable and Storage Efficient Dynamic Key Management Scheme for Wireless Sensor Network

Scalable and Storage Efficient Dynamic Key Management Scheme for Wireless Sensor Network

A Novel Hybrid Optimization for Cluster‐Based Routing Protocol in Information-Centric Wireless Sensor Networks for IoT Based Mobile Edge Computing

An edge based hybrid intrusion detection framework for mobile edge computing

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