Towards Secure Data Forwarding with ANFIS and Trust Evaluation in Wireless Sensor Networks

Renjith, P. N.

doi:10.1007/s11277-020-07392-1

Cited by 9 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[7][8][9] These articles used the threshold value of trust to detect the attack. Similar concept is also observed in fuzzy logic-based secure data transmission, 10 Kalman filter, 11 decision tree 12 and reinforcement learning. 13 It has been noticed that every trust or reputation calculation method needs a threshold value to decide the node's dependency for reliable transmission.…”

Section: F I G U R E 1 Nodes' Frequency In the Path Under (A) Benign Case (B) Carousel Attack (C) Stretch Attacksupporting

confidence: 57%

An anomalous co‐operative trust & PG‐DRL based vampire attack detection & routing

Juneja

Dinkar

Gupta

2021

Concurrency and Computation

View full text Add to dashboard Cite

Sensor nodes in WSN play a vital role in communication, IoTs and many other emergencies too. However, the energy consumption of nodes is a major setback to these, which incites various malicious nodes/attacks. This article studies and presents the solution to Vampire attack-one of those kinds of attacks. It depletes the energy by route elongation of data transmission. This article has suggested a novel two-fold mechanism to detect the attack by integrating co-operation trust mechanism and the mitigation of the attack by selecting the secure route by policy gradient-deep reinforcement learning. The designed protocol also ensures the selection of a secure hop even in the presence of the vampire attack. The results are compared with various other existing state-of-the-art schemes and have improved the detection ratio by 20% compared to the forecasting methods applied for detecting the vampire node's behavior. The network lifetime has also improved by 3% than the benchmark dynamic source routing.

show abstract

Section: F I G U R E 1 Nodes' Frequency In the Path Under (A) Benign Case (B) Carousel Attack (C) Stretch Attacksupporting

confidence: 57%

An anomalous co‐operative trust & PG‐DRL based vampire attack detection & routing

Juneja

Dinkar

Gupta

2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…DrivMan can be utilized as a compelling answer to forgive both data provenance and data uprightness to shrewd vehicles in VANETs for their safe and reliable activity. Another recent trust-based methodology for secure data forwarding in VANETs was proposed in [31] using artificial intelligence (AI) and trust evaluation. They used the fuzzy logic technique and neural network for the trust evaluation algorithm.…”

Section: A State Of Artmentioning

confidence: 99%

ARPVP: Attack Resilient Position-Based VANET Protocol Using Ant Colony Optimization

Maranur¹,

Mathapati²

2022

Wireless Pers Commun

View full text Add to dashboard Cite

The position-based routing of Vehicular Ad hoc Network (VANET) vulnerable to various security attacks because of dependency on computing, control, and communication technologies. The Internet of Things (IoT)-enabled VANET application leads to the challenges such as integrity, access control, availability, privacy protection, non-repudiation, and confidentiality. Several security solutions have been introduced for two decades in two categories as cryptography-based and trust-based. Due to the high computation complexity, cryptography-based solutions are outperformed by recent intelligent trust-based mechanisms. The trust-based techniques are lightweight and effective against the well-known security threats in VANET. The objective of this paper has to design a novel position-based routing in which the conduct of vehicles assessed to accomplish reliable VANET communications. Attack Resilient Position-based VANET Protocol (ARPVP) proposed to detect and prevent malicious vehicles in the network using the trust evaluation technique and artificial intelligence (AI). In the first phase of ARPVP, the periodic self-trust assessment algorithm has designed using various trust parameters to detect unreliable vehicles in the network. In the second phase of ARPVP, the position-based route formation algorithm has designed using the AI technique Ant Colony Optimization (ACO). ACO solves the problem of reliable route formation by neglecting the attacker's using a trust-based fitness function. The trust parameters of each vehicle as mobility, buffer occupancy, and link quality parameters had measured in both phases of ARPVP. Simulation outcomes of the proposed model outperformed state-of-art protocols in terms of average throughput, communication delay, overhead, and Packet Delivery Ratio (PDR).

show abstract

“…Identifying the node's presence in the network is not enough; the nodes should also be recognized. Vampire nodes can be identified using trust‐based detection algorithms, such as References 7,8,10, where a threshold trust value is specified. This value is either based on prior information or is not flexible enough to adapt to the ever‐changing WSN environment.…”

Section: Proposed Solutionsmentioning

confidence: 99%

“…[7][8][9] These studies detected the assault based on a trust threshold value. Fuzzy logic safe data transfer, 10 Kalman filter, 11 decision tree, 12 and reinforcement learning 13 are also examples of similar concepts. Any trust or reputation calculation technique requires a threshold value to determine the reliability of a node's transmission.…”

Section: Introductionmentioning

confidence: 99%

A predictive vampire attack detection by social spider optimized Gaussian mixture model clustering

Juneja

Dinkar

2022

Concurrency and Computation

View full text Add to dashboard Cite

A sensor node carries out a specific function in a wireless sensor network. Wireless sensor networks are less bearable but more vital to the industry and the people because of threats. As a result, a node's battery life will be drastically reduced, and the node will be rendered completely inoperable, which is the most extreme form of denial of service attack. A vampire attack, one of the denials of service attacks, may inflict extensive harm to the network, making it harder to detect and using more energy than necessary. This article has proposed a novel vampire attack detection and prevention by the energy consumption prediction in the data path with the least error as low as 1e −3 . In combination with the social spider optimized Gaussian mixture model, the gray prediction model is used to detect and prevent the attack. The energy prediction scheme calculates a cooperative trust score and is categorized by the optimized Gaussian mixture model. The algorithm is validated with recent state-of-the-art schemes and the detection accuracy improvement of up to 35.27% is achieved.

show abstract

Towards Secure Data Forwarding with ANFIS and Trust Evaluation in Wireless Sensor Networks

Cited by 9 publications

References 14 publications

An anomalous co‐operative trust & PG‐DRL based vampire attack detection & routing

An anomalous co‐operative trust & PG‐DRL based vampire attack detection & routing

ARPVP: Attack Resilient Position-Based VANET Protocol Using Ant Colony Optimization

A predictive vampire attack detection by social spider optimized Gaussian mixture model clustering

Contact Info

Product

Resources

About