RETRACTED ARTICLE: Design and implement a cross layer verification framework (CLVF) for detecting and preventing blackhole and wormhole attack in wireless ad-hoc networks for cloud environment

Jagadeesan, S.; Parthasarathy, V.

doi:10.1007/s10586-018-1825-8

Cited by 15 publications

(24 citation statements)

References 15 publications

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“…From the simulation values provided above, it proves that throughput estimation without considering optimal sensing and robust classification results offers a false result of performance. Evaluating the throughput rate of state‐of‐the‐art methods, [1, 2, 5 16], the throughput is improved in OS‐LNCC method compared to [1, 2, 5 16] as OS‐LNCC method makes sure that a primary channel is sensed by only one SU and not by the MU by effective classification based on the LNCC. With this, the throughput rate using OS‐LNCC method is found to be better by 21% compared to [1], 38% compared to [2], 51% compared to [5] and 68% compared to [16].…”

Section: Methodsmentioning

confidence: 99%

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Ganesh

Kumar

Kumar³

2020

IET Communications

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Cognitive radio networks (CRN) make use of dynamic spectrum access to communicate opportunistically. Unlicensed users severely affect the spectrum sensing outcomes in CRN. Primary user emulation attack (PUEA) and spectrum sensing data falsification (SSDF) have become a paramount concern in CRNs. It is especially challenging when both masquerading (in the physical layer) and falsification (in the data link layer) occur by providing false spectrum reports. Existing methods to detect such attacks cannot be utilised in scenarios with multi-hop CRN. In this study, to mitigate attack against PUEA and SSDF, a method called optimised sensing and Levenshtein nearest centroid classification (OS-LNCC) for multi-hop CRN is presented. First, a network model for multi-hop CRN is designed. Next, a probable density optimal logical sensing model is designed to alleviate the problems related to falsification of spectrum reports. Here, the falsification of spectrum reports is overcome by exploiting dual factors, that is, probability for false alarm and probability for detection according to the departure rate of primary user (PU). With these dual factors, optimal logical sensing is made, therefore improving the throughput with minimum delay. Finally, each cognitive radio (CR) user evaluates its current sensing information to existing sensing classes through the Levenshtein distance function. Based on quantitative variables, the prediction function of each sensing class is measured using nearest centroid (NC) classifier and the sensing report is classified into either presence or absence of PU. These predictive classes are then integrated at the fusion centre so that robust mitigation against PUEA and SSDF is made. Computer simulation outputs show that OS-LNCC method performs better than the conventional methods using metrics such as sensing delay by 47%, percentage of error in prediction by 46% and throughput by 45%.

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Section: Methodsmentioning

confidence: 99%

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Ganesh

Kumar

Kumar³

2020

IET Communications

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“…Hua [146] Covert in-band channel creation. Bhosale and Sonavane [41] Intrusion detection system Jagadeesan and Parthasarathy [147] Cross-layer verification framework.…”

Section: Defenses Against Routing- Black Hole- Gray Hole- and Worm Ho...mentioning

confidence: 99%

“…Bhosale and Sonavane [41] designed and implemented an intrusion detection system to detect wormhole attacks using Contiki OS and Cooja Simulator. Jagadeesan and Parthasarathy [147] proposed a crosslayer verification framework that can detect and counter black hole and worm hole attacks in wireless ad-hoc networks.…”

Section: Defenses Against Routing- Black Hole- Gray Hole- and Worm Ho...mentioning

confidence: 99%

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

Gupta¹,

Maple²

2023

Preprint

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<p>Edge Computing-based Connected Autonomous Vehicles (EC-CAVs) can fulfill the emerging demand for smart transportation on a global scale. Such innovations to transportation ecosystems can bring manyfold benefits from fully autonomous driving services to proactive vehicle monitoring and traffic management. Given the complexity involved in the deployment of EC-CAVs, zero-tolerance safety and security measures must be incorporated to avert vehicle immobilization, road accidents, disclosure of sensitive data, or any potential threats. This paper presents a reference architecture of EC-CAVs to investigate existing and emerging cyber-threats for deriving a common attack taxonomy for the EC-CAVs ecosystem. Subsequently, a comprehensive survey of security mechanisms is presented that can ensure safe and secure transportation of passengers from one destination to another using an EC-CAVs ecosystem.</p>

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“…Therefore, designing an appropriate security measure in these networks seems essential. [1][2][3][4][5] In general, the operations in mobile networks can be categorized into key management, secure routing, authentication, denial of service attack prevention, and misbehavior and intrusion detection. Various routing protocols are used in MANETs.…”

Section: Introductionmentioning

confidence: 99%

A reliable and secure method for network‐layer attack discovery and elimination in mobile ad‐hoc networks based on a probabilistic threshold

Ebazadeh

Fotohi

2021

Security and Privacy

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The wireless nature of mobile networks makes it vulnerable to all kinds of attacks. Therefore, designing a secure method for detecting intrusive nodes in this type of network seems necessary. Among the types of attacks on mobile ad-hoc networks, the Grayhole attack is one of the most dangerous attacks. In this type of attack, a malicious node absorbs network traffic and then selectively deletes a number of received packets. In this paper, a method called RSA-GRAY HOLE is used to detect and isolate the Grayhole attack. The proposed method not only detects the malicious node, but also disconnects it from the network and does not allow it to participate in the next routing process. The proposed method detects a malicious node in two steps; in the first step, it examines the validity of the nodes of a route to select a valid route. In the second step, the destination node detects the presence of malicious nodes in the route and after detecting the malicious nodes, disconnects them from the network. To demonstrate the efficiency of the proposed RSA-GRAY HOLE method, the NS-2 simulator was used and this method was compared with the DSR protocol under Grayhole attack. The simulation results show the superior performance of the proposed RSA-GRAY HOLE method compared to the Grayhole DSR method in terms of throughput (23%), package delivery rate (26%), average delay (19%) and number of lost packets (20%).

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RETRACTED ARTICLE: Design and implement a cross layer verification framework (CLVF) for detecting and preventing blackhole and wormhole attack in wireless ad-hoc networks for cloud environment

Cited by 15 publications

References 15 publications

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

Optimised Levenshtein centroid cross‐layer defence for multi‐hop cognitive radio networks

An investigation of cyber-attacks and security mechanisms for connected and autonomous vehicles

A reliable and secure method for network‐layer attack discovery and elimination in mobile ad‐hoc networks based on a probabilistic threshold

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