Study of trust‐based mechanism and its component model in MANET: Current research state, issues, and future recommendation

Khanna, Nitin; Sachdeva, Monika

doi:10.1002/dac.4012

Cited by 25 publications

(10 citation statements)

References 45 publications

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“…Past interaction history (PIH) has (Table 5) quality factors such as source node location, receiver node location, time taken to transmit packets, minimum route path, and trust values. This PIH is exposed below table 6 by the terms like network id, node pattern, trust value, node lifetime, efficiency, and bandwidth [58]. The network lifetime is determined by the ratio of confirmed acknowledgment to the whole number of possible node transmissions on reaching the prescribed Trust Values (TV).…”

Section: Past Interaction History With Transmission Datamentioning

confidence: 99%

Intelligent route discovery towards rushing attacks in ad hoc wireless networks

Allimuthu

Mahalakshmi

2021

J Ambient Intell Human Comput

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MANET (Mobile Ad-hoc Networks) are distributed or delegated away from a central server, authoritative location of wireless networks that communicate without pre-existing structure. Ad-hoc networks are compromising the many types of attacks and routing. In MANET, the routing plays a vital role in terms of packets interaction and data transmission. Due to decentralized control, the MANET data transmission becomes insecure because of dispersed routing on the mobile ad-hoc nodes. Since the efficient route on MANET only controls the packets and does not simplify the route between the source to the destination, the maintenance of route interaction becomes a crucial process. Maintain effective data transactions over the MANET network, and it is essential to improve the route and locate the attacker. Nevertheless, MANET allows for route interaction against security threads. In this research article, four processing schemes are suggested to preserve the security measures against routing protocols. Especially in node communication, the rushing attacker has a significant impact on packet-based data transmission in MANET. Also, for this research, an Attacker detection automation of the Bees Colony Optimization (ADABCP) method is used, as a result of which the desired result is brought about in the effective attacker detection on the routing process. Moreover, the proposed Hybrid Random Late Detection (HRLD) routing protocol manages the MANET routing and overcomes the MANET congestion communication. The Swift Implicit Response Round Trip Time (SIRT) mechanism is generated by the Route Finding Manipulation (RFM) to enhance the performance. This RFM scheme helps to find the optimal routing in a secured manner. The proposed (SIRT-ADABCP-HRLD) approach was compared to the existing ESCT, ZRDM-LFPM, and ENM-LAC approaches, found to have improved by routing and data transmission. Compared to the conventional method, the method mentioned above achieves a better ratio for the end-to-end delay, communication overhead, packet delivery ratio, network lifetime, and energy consumption.

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Section: Past Interaction History With Transmission Datamentioning

confidence: 99%

Intelligent route discovery towards rushing attacks in ad hoc wireless networks

Allimuthu

Mahalakshmi

2021

J Ambient Intell Human Comput

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“…Although these algorithms improve the rating prediction accuracy of recommendation systems, they are at the cost of time or low coverage. Therefore, they fail to work well in cold start cases, and data sparsity remains a stubborn challenge [37]. Inspired by the problem, our paper attempts to alleviate this problem by introducing the implicit trust recommendation approach.…”

Section: Related Workmentioning

confidence: 99%

A Novel Implicit Trust Recommendation Approach for Rating Prediction

Liu

Ren

et al. 2020

IEEE Access

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Rating predictions, as an application that is widely used in recommender systems, have gradually become a valuable way which can help user narrow down their choices quickly and make wise decisions from the vast amount of information. However, most existing collaborative recommendation models suffer from poor accuracy due to data sparsity and cold start problems that recommender systems contain only a few explicit data. To solve this problem, a new implicit trust recommendation approach (ITRA) is proposed to generate item rating prediction by mining and utilizing user implicit information in recommender systems. Specifically, user trust neighbor set that has similar preference and taste with a target user is first obtained by trust expansion strategy via user trust diffusion features in a trust network. Then, the trust ratings mined from user trust neighbors are used to compute trust similarity among users based on user collaborative filtering model. Finally, using the above filtered trust ratings and user trust similarity, the prediction results are generated by a trust weighting method. In addition, the empirical experiments are conducted on three real-world datasets, and the results demonstrate that our rating prediction model has obvious advantages over the state-of-the-art comparison methods in terms of the accuracy of recommendations.

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“…However, the railway cyber infrastructure and networks currently only adopt rudimentary defenses (e.g., cryptography), which provide protection against the most basic threats, forfeiting effective ways of detecting advanced cyber-attacks [26]. While initially designed as closed systems, current infrastructure networks are vulnerable to various network layer attacks, like blackhole, badmouthing, and jamming attacks [27].…”

Section: Introductionmentioning

confidence: 99%

SPD-Safe: Secure Administration of Railway Intelligent Transportation Systems

Hatzivasilis

Fysarakis²,

Ioannidis

et al. 2021

Electronics

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The railway transport system is critical infrastructure that is exposed to numerous man-made and natural threats, thus protecting this physical asset is imperative. Cyber security, privacy, and dependability (SPD) are also important, as the railway operation relies on cyber-physical systems (CPS) systems. This work presents SPD-Safe—an administration framework for railway CPS, leveraging artificial intelligence for monitoring and managing the system in real-time. The network layer protections integrated provide the core security properties of confidentiality, integrity, and authentication, along with energy-aware secure routing and authorization. The effectiveness in mitigating attacks and the efficiency under normal operation are assessed through simulations with the average delay in real equipment being 0.2–0.6 s. SPD metrics are incorporated together with safety semantics for the application environment. Considering an intelligent transportation scenario, SPD-Safe is deployed on railway critical infrastructure, safeguarding one outdoor setting on the railway’s tracks and one in-carriage setting on a freight train that contains dangerous cargo. As demonstrated, SPD-Safe provides higher security and scalability, while enhancing safety response procedures. Nonetheless, emergence response operations require a seamless interoperation of the railway system with emergency authorities’ equipment (e.g., drones). Therefore, a secure integration with external systems is considered as future work.

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Study of trust‐based mechanism and its component model in MANET: Current research state, issues, and future recommendation

Cited by 25 publications

References 45 publications

Intelligent route discovery towards rushing attacks in ad hoc wireless networks

Intelligent route discovery towards rushing attacks in ad hoc wireless networks

A Novel Implicit Trust Recommendation Approach for Rating Prediction

SPD-Safe: Secure Administration of Railway Intelligent Transportation Systems

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