Intrusion detection in mobile ad hoc networks: techniques, systems, and future challenges

Kumar, Sunil; Dutta, Kamlesh

doi:10.1002/sec.1484

Cited by 72 publications

(27 citation statements)

References 236 publications

(380 reference statements)

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“…Various surveys have attempted to describe machine learning-based IDSs for protection against IoT networks or compromised IoT devices. The surveys cover research work on IDSs for cloud-based IoT systems [16], Wireless sensor networks [17][18][19], cyber-physical systems [20], and mobile ad hoc networks (MANETs) [21][22][23]. However, traditional IDS methods are less effective or insufficient for the security of IoT systems because of their peculiar characteristics mentioned above, in particular, limited energy, ubiquitous, heterogeneity, limited bandwidth capacity and global connectivity.…”

Section: Motivationmentioning

confidence: 99%

“…While the third one can be a hybrid of both using mobile agents. Another study [23] discussed various Intrusion Detection algorithms related to IDS implementation in MANET. According to the authors, these IDS algorithms can be categorized in various categories based on the underlying principle used for the detection of an attack.…”

Section: Current Reviewsmentioning

confidence: 99%

“…These techniques were later classified further as anomaly detection, misuse, signature-based, or hybrid techniques. There were other classification criteria proposed by the authors [23] like real-time/offline, attack types and effectiveness of detection (scalability, reliability, timeliness, etc. ).…”

Section: Current Reviewsmentioning

confidence: 99%

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A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

et al. 2020

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The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

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Section: Motivationmentioning

confidence: 99%

Section: Current Reviewsmentioning

confidence: 99%

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A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

et al. 2020

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“…The guard node is between the sending node and the receiving node, which can be used for normal communication and monitoring. The selection of guard nodes is based on the location and trustworthiness of the nodes [18,33,34]. Because the continuous data flow network in this paper is not a WSN-like mobile ad-hoc network, so when selecting guard nodes, we do not need to consider the location factor, but only need to select according to the trustworthiness of the nodes.…”

Section: Anomalous Nodes Discovery and Confidence Evaluationmentioning

confidence: 99%

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Luo

Qiu

2019

Sensors

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The continuous data-flow application in the IoT integrates the functions of fog, edge, and cloud computing. Its typical paradigm is the E-Health system. Like other IoT applications, the energy consumption optimization of IoT devices in continuous data-flow applications is a challenging problem. Since the anomalous nodes in the network will cause the increase of energy consumption, it is necessary to make continuous data flows bypass these nodes as much as possible. At present, the existing research work related to the performance of continuous data-flow is often optimized from system architecture design and deployment. In this paper, a mathematical programming method is proposed for the first time to optimize the runtime performance of continuous data flow applications. A lightweight anomaly detection method is proposed to evaluate the reliability of nodes. Then the node reliability is input into the optimization algorithm to estimate the task latency. The latency-aware energy consumption optimization for continuous data-flow is modeled as a mixed integer nonlinear programming problem. A block coordinate descend-based max-flow algorithm is proposed to solve this problem. Based on the real-life datasets, the numerical simulation is carried out. The simulation results show that the proposed strategy has better performance than the benchmark strategy.Sensors 2020, 20, 122 2 of 20 aggregation site, etc. [5]. On the other side, the fog computing nodes are not necessarily deployed around these access points. To distinguish these two concepts, in this paper, nodes with sufficient communication and computing resources scattered among IoT devices are referred to as fog nodes. The nano-server clusters deployed at the access points are referred to as MEC nodes, which usually have more resources than fog nodes. The collaborative computing of the IoT-end nodes, the fog nodes, the MEC nodes and the data center are referred to as IoT-Fog-Edge computing in this context.The studies on collaboration of Fog, MEC, and Cloud computing usually focus on finding an optimal solution to allocate the IoT tasks to appropriate virtual machines which are hosted on fog, MEC or cloud nodes. In [10], the authors propose to allocate the workload among local MEC servers, neighborhood MEC servers or cloud servers to minimize the energy consumption of MEC nodes subject to delay constraints. Lyapunov drift-plus-penalty-based dynamic queue evaluation is used for the online allocation algorithm. In [11], an optimal algorithm is put forward to determine that the tasks should be allocated to clouds near the end devices or to the one far from the end devices for the energy efficient big data processing. The delay constraints to tasks in near clouds and far cloud are taken into account. In [12], an optimal algorithm for joint task allocation among mobile devices, the computing access point and the remote cloud is proposed, where computing access point can be treated as an MEC node. The studies introduced above are not correlated with the continuous data-fl...

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“…Several works have been suggested in a MANET to detect and prevent the network from dropping and data integrity intrusions [3]. The intrusion detection systems have been categorized into proactive and reactive mechanisms.…”

Section: Related Workmentioning

confidence: 99%

Proactive Hybrid Intrusion Prevention System for Mobile Adhoc Networks

Yerur¹,

Prakash²,

Rangaswamy³

2017

IJIES

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Abstract:The open nature of Mobile Ad Hoc NETworks (MANETs) provides an opportunity for intrusions. The current intrusion mechanisms are reactive and incapable of preventing the intrusions proactively. This paper proposes the secure routing using Hybrid intrusion prevention systems against dropping and data integrity THreat (SHEATH). This proposal implements self-key and mutual-key reliant prevention and the appearance frequency based behavior certainty measurement on routing paths. The self-key prevention scheme exploits the encrypted value of the sequence number as a normal pattern and the decryption determine whether the route reply is the result of a malicious node or not. The behavior certainty measurement using distributed selection of Squad Head nodes ensures the effective observation of misuse pattern and minimum routing overhead. The data forwarding phase shares a mutual key between the communicating nodes that prevent the data integrity attacks. The simulation results confirm the efficiency of the hybrid preventive scheme against intrusions.

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Intrusion detection in mobile ad hoc networks: techniques, systems, and future challenges

Cited by 72 publications

References 236 publications

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Proactive Hybrid Intrusion Prevention System for Mobile Adhoc Networks

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