Gray and Black Hole Attack Identification Using Control Packets in MANETs

Nandal, Amita; Dhaka, Raghuveer Singh

doi:10.1016/j.procs.2015.06.010

Cited by 42 publications

(20 citation statements)

References 5 publications

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“…Multiple attackers cannot be handled with this technique. Dhaka et al [8] used two control packets namely code sequence packet and response sequence packet to detect grayhole and blackhole attack. Code sequence packet contains the sender details with sender sequence ID and the response sequence packet contains receiver details with destination sequence ID.…”

Section: Sequence Number Based Detectionmentioning

confidence: 99%

“…Most of the above discussed methodologies relay only on one parameter namely sequence number [8,15,16], packet acknowledgment [17,18,19,20], buffer memory [14] to detect malicious node. This increases false positive and false negative.…”

Section: Acknowledgment Based Detectionmentioning

confidence: 99%

“…From the literature, it is understood that malicious node attracts more packets & drops more packets, and that its network participation is very limited such as it initiates/forwards very less number of control packets [7,8]. Based on this fact, we have proposed behavior based routing misbehavior detection (BRMD) against packet dropping attacks.…”

Section: Introductionmentioning

confidence: 99%

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Behavior based Routing Misbehavior Detection in Wireless Sensor Networks

Terence¹,

Geethanjali²

2019

KSII TIIS

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Sensor networks are deployed in unheeded environment to monitor the situation. In view of the unheeded environment and by the nature of their communication channel sensor nodes are vulnerable to various attacks most commonly malicious packet dropping attacks namely blackhole, grayhole attack and sinkhole attack. In each of these attacks, the attackers capture the sensor nodes to inject fake details, to deceive other sensor nodes and to interrupt the network traffic by packet dropping. In all such attacks, the compromised node advertises itself with fake routing facts to draw its neighbor traffic and to plunge the data packets. False routing advertisement play vital role in deceiving genuine node in network. In this paper, behavior based routing misbehavior detection (BRMD) is designed in wireless sensor networks to detect false advertiser node in the network. Herein the sensor nodes are monitored by its neighbor. The node which attracts more neighbor traffic by fake routing advertisement and involves the malicious activities such as packet dropping, selective packet dropping and tampering data are detected by its various behaviors and isolated from the network. To estimate the effectiveness of the proposed technique, Network Simulator 2.34 is used. In addition packet delivery ratio, throughput and end-to-end delay of BRMD are compared with other existing routing protocols and as a consequence it is shown that BRMD performs better. The outcome also demonstrates that BRMD yields lesser false positive (less than 6%) and false negative (less than 4%) encountered in various attack detection.

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Section: Sequence Number Based Detectionmentioning

confidence: 99%

Section: Acknowledgment Based Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Behavior based Routing Misbehavior Detection in Wireless Sensor Networks

Terence¹,

Geethanjali²

2019

KSII TIIS

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“…The basic idea of iTrust is introducing a regularly available Trusted Authority (TA) to judge the node's behavior based on the routing evidences collected and probabilistically checking. Dhaka et al [13] proposed a scheme in which we are sending a control sequence to the neighbor nodes and we are expecting the nodes response. Based on the node response we can identify the malicious node.…”

Section: Related Workmentioning

confidence: 99%

“…The implementation of an algorithm is done in well known network simulator NS-2.34 [13]. The simulation environment is setup to simulate the algorithm in which we take an area of 900x900 to transmit the packet TCP/FTP, UDP/CBR protocol AODV is used and the channel wireless operation mode 802.11, mobility model random waypoint with least frequency 50 Hz is used for the simulation time period of 200 sec.…”

Section: Scenario Setupmentioning

confidence: 99%

Exposure and Mitigation of the Gray Hole Attack from AODV in Mobile Ad hoc Network: An Approach

Tiwari¹,

Jain²

2017

IJCA

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Wireless ad hoc network is a collection of mobile nodes and all nodes behave as router or host. Due to its dynamic nature and lack of central authority security is challenging task. The nodes of network may compromise from the various security threats and can leak the personal information. Numerous security threats such as Denial of Service, black hole attack, Gray-hole attack, Worm hole, Sybil attack and jamming may be used by attacker to damage the network security. Gray hole attack is one the security threat which selectively drop the packets. In this paper, proposes an probabilistic approach with IDS (Intrusion Detection System) which detect and mitigate the gray hole attack effectively. The simulation of the propose approach is done in NS2.34 network simulation and comparative analysis is perform among the performance metrics such as Throughput, Packet Delivery Ratio and Routing load etc.

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A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

Bhattacharya

Roy

Chattopadhyay

et al. 2022

Security and Privacy

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Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)‐based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML‐based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road‐map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

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Gray and Black Hole Attack Identification Using Control Packets in MANETs

Cited by 42 publications

References 5 publications

Behavior based Routing Misbehavior Detection in Wireless Sensor Networks

Behavior based Routing Misbehavior Detection in Wireless Sensor Networks

Exposure and Mitigation of the Gray Hole Attack from AODV in Mobile Ad hoc Network: An Approach

A comprehensive survey on online social networks security and privacy issues: Threats, machine learning‐based solutions, and open challenges

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