A sequence number based bait detection scheme to thwart grayhole attack in mobile ad hoc networks

Jhaveri, Rutvij H.; Patel, Neelam

doi:10.1007/s11276-015-0945-9

Cited by 41 publications

(18 citation statements)

References 32 publications

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“…In IDS, several approaches are proposed such as packet payload analysis [23], pattern propagation [24] and bro language [25]. In addition, various schemes for ad-hoc networks are proposed to detect attack patterns and to provide a defense mechanism in the network [26][27][28][29][30]. IDS which are passive by nature have a key issue their inability to mount targeted, reliable and adaptive response [31].…”

Section: Intrusion Detection and Preventionmentioning

confidence: 99%

Applications in Security and Evasions in Machine Learning: A Survey

2020

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In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications’ perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers’ knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks.

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Section: Intrusion Detection and Preventionmentioning

confidence: 99%

Applications in Security and Evasions in Machine Learning: A Survey

2020

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“…Jhaveri et al proposed an approach which is based on the fabricated highest sequence number by a malicious node in order to detect the attacker node [33]. The sequence number based bait detection scheme (SNBDS) includes two slight modification in the routing table of all nodes, i.e., "Node Status" which is used to record the behavior of the node and "Last Reply time" that is the updated sequence number for the desired destination node in the last RREP of any node.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Scheme for Detection and Prevention of Black Hole Attacks in AODV-Based MANETs

Pathan¹,

He²,

Zhu³

et al. 2019

ijacsa

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Mobile ad hoc network (MANET) is a set of independent mobile nodes, which connect to each other over a wireless channel without any centralized infrastructure, nor integrated security. MANET is a weak target to many Denial of Service (DOS) attacks, which seriously harms its functionality and connectivity. A black hole attack is a type of DOS attack, where the malevolent node tries to get all the data packets from a source node by sending fabricated fake route reply (RREP) packet, falsely pretending that it possesses the shortest path towards the destination node, and then drops all the packets it receives. In this paper, the AODV (Ad-hoc on-demand distance vector) routing protocol is improved by incorporating an efficient and simple mechanism to mitigate black hole attacks. Mechanism to detect black hole attacks from MANET (MDBM) uses fake route request (RREQ) packets with an unreal destination address in order to detect black hole nodes prior to the actual routing process. Simulation experiment conducted has verified the performance of the proposed detection and prevention scheme. The results demonstrated that the proposed mechanism performed well in terms of Packet Delivery Ratio, End-to-End Delay and Throughput under black hole attack.

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“…In this adversary model (a type of gray-hole attack described as Attack1 in [22]), a malicious node continuously monitors the field value of received as well as overheard control packets, in order to keep track of the highest recorded value of the destination sequence number. During the routing process, the malicious node replies to an Route Request (RREQ) with the lowest hop count (which is 1) and the highest possible value of the destination sequence number.…”

Section: The Adversary Modelmentioning

confidence: 99%

An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs

Pathan

Zhu

et al. 2018

Future Internet

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Due to the dynamism of topology, sharing of bandwidth and constraint of resources in wireless nodes, the provision of quality of service (QoS) for routing in mobile ad hoc networks (MANETs) presents a great challenge. Security is another crucial aspect of providing QoS since the existence of malicious nodes present all kinds of threats to MANETs. Although a number of mechanisms have been proposed for protecting MANETs, most of the solutions are only effective for a particular kind of attacks or provide security at the cost of sacrificing QoS. In this paper, we propose a trust-based secure QoS routing scheme by combining social and QoS trust. The primary approach of the proposed scheme relies on mitigating nodes that exhibit various packet forwarding misbehavior and on discovering the path that ensures reliable communication through the trust mechanism. The scheme would select the best forwarding node based on packet forwarding behavior as well as capability in terms of QoS parameters, such as residual energy, channel quality, link quality, etc. We will present an adversary model for packet dropping attack against which we evaluate the proposed scheme. Simulation experiment using Network Simulator-2 (NS2) and under various network conditions show that mixing social and QoS trust parameters can greatly improve security and quality of service routing in terms of overhead, packet delivery ratio and energy consumption.

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A sequence number based bait detection scheme to thwart grayhole attack in mobile ad hoc networks

Cited by 41 publications

References 32 publications

Applications in Security and Evasions in Machine Learning: A Survey

Applications in Security and Evasions in Machine Learning: A Survey

An Efficient Scheme for Detection and Prevention of Black Hole Attacks in AODV-Based MANETs

An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs

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