Sybil attack in IOT: Modelling and defenses

Rajan, Anjana; Jithish, J.; Sankaran, Sriram

doi:10.1109/icacci.2017.8126193

Cited by 48 publications

(23 citation statements)

References 10 publications

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“…Modifying the routing information, so called modifying attack, is the easiest form of routing attacks. Besides these attacks, many attacks have been found in literature, the most common of which are Sybil [139], Gray Hole [140], Wormhole, Hello flood, and Selective forwarding. Next, We briefly illustrate them.…”

Section: Attacks and Threats Against Communication Levelmentioning

confidence: 99%

A Comprehensive Study of Security and Privacy Guidelines, Threats, and Countermeasures: An IoT Perspective

Abdulghani

Konstantas

2019

JSAN

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As Internet of Things (IoT) involvement increases in our daily lives, several security and privacy concerns like linkability, unauthorized conversations, and side-channel attacks are raised. If they are left untouched, such issues may threaten the existence of IoT. They derive from two main reasons. One is that IoT objects are equipped with limited capabilities in terms of computation power, memory, and bandwidth which hamper the direct implementation of traditional Internet security techniques. The other reason is the absence of widely-accepted IoT security and privacy guidelines and their appropriate implementation techniques. Such guidelines and techniques would greatly assist IoT stakeholders like developers and manufacturers, paving the road for building secure IoT systems from the start and, thus, reinforcing IoT security and privacy by design. In order to contribute to such objective, we first briefly discuss the primary IoT security goals and recognize IoT stakeholders. Second, we propose a comprehensive list of IoT security and privacy guidelines for the edge nodes and communication levels of IoT reference architecture. Furthermore, we point out the IoT stakeholders such as customers and manufacturers who will benefit most from these guidelines. Moreover, we identify a set of implementation techniques by which such guidelines can be accomplished, and possible attacks against previously-mentioned levels can be alleviated. Third, we discuss the challenges of IoT security and privacy guidelines, and we briefly discuss digital rights management in IoT. Finally, through this survey, we suggest several open issues that require further investigation in the future. To the best of the authors’ knowledge, this work is the first survey that covers the above-mentioned objectives.

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Section: Attacks and Threats Against Communication Levelmentioning

confidence: 99%

A Comprehensive Study of Security and Privacy Guidelines, Threats, and Countermeasures: An IoT Perspective

Abdulghani

Konstantas

2019

JSAN

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“…An adversary

scriptA

can physically seize some IoT sensors as the sensors are deployed in an “unattended (hostile) environment” whereas 24 × 7 monitoring is not possible, and hence, it allows

scriptA

to extract the useful information (for example, session keys established with other IoT sensor) from their memory using the power analysis attack scriptA can install malicious program in the memory of sensing devices to perform other attacks (ie, routing attacks) scriptA can also deploy some attacker nodes to capture the exchanging confidential data of IoT sensors.…”

Section: Introductionmentioning

confidence: 99%

“…If these attacker nodes are present, the information may not reach to the destination within a specific time. Moreover, information may be lost or modified, which may also create more energy disbursement …”

Section: Introductionmentioning

confidence: 99%

“…10,11  can install malicious program in the memory of sensing devices to perform other attacks (ie, routing attacks). [12][13][14][15]  can also deploy some attacker nodes to capture the exchanging confidential data of IoT sensors. There is a crucial need of an intrusion detection mechanism for an IoT environment to detect malicious attacker nodes.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, information may be lost or modified, which may also create more energy disbursement. [12][13][14][15][18][19][20][21][22][23]…”

Section: Introductionmentioning

confidence: 99%

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RAD‐EI: A routing attack detection scheme for edge‐based Internet of Things environment

Wazid

Dsouza

Das

et al. 2019

Int J Communication

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Summary Internet of Things (IoT) offers various types of application services in different domains, such as “smart infrastructure, health‐care, critical infrastructure, and intelligent transportation system.” The name edge computing signifies a corner or edge in a network at which traffic enters or exits from the network. In edge computing, the data analysis task happens very close to the IoT smart sensors and devices. Edge computing can also speed up the analysis process, which allows decision makers to take action within a short duration of time. However, edge‐based IoT environment has several security and privacy issues similar to those for the cloud‐based IoT environment. Various types of attacks, such as “replay, man‐in‐the middle, impersonation, password guessing, routing attack, and other denial of service attacks” may be possible in edge‐based IoT environment. The routing attacker nodes have the capability to deviate and disrupt the normal flow of traffic. These malicious nodes do not send packets (messages) to the edge node and only send packets to its neighbor collaborator attacker nodes. Therefore, in the presence of such kind of routing attack, edge node does not get the information or sometimes it gets the partial information. This further affects the overall performance of communication of edge‐based IoT environment. In the presence of such an attack, the “throughput of the network” decreases, “end‐to‐end delay” increases, “packet delivery ratio” decreases, and other parameters also get affected. Consequently, it is important to provide solution for such kind of attack. In this paper, we design an intrusion detection scheme for the detection of routing attack in edge‐based IoT environment called as RAD‐EI. We simulate RAD‐EI using the widely used “NS2 simulator” to measure different network parameters. Furthermore, we provide the security analysis of RAD‐EI to prove its resilience against routing attacks. RAD‐EI accomplishes around 95.0% “detection rate” and 1.23% “false positive rate” that are notably better than other related existing schemes. In addition, RAD‐EI is efficient in terms of computation and communication costs. As a result, RAD‐EI is a good match for some critical and sensitive applications, such as smart security and surveillance system.

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Taxonomy of IoT Vulnerabilities

Bou‐Harb

Neshenko

2020

Cyber Threat Intelligence for the Internet of Things

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Sybil attack in IOT: Modelling and defenses

Cited by 48 publications

References 10 publications

A Comprehensive Study of Security and Privacy Guidelines, Threats, and Countermeasures: An IoT Perspective

A Comprehensive Study of Security and Privacy Guidelines, Threats, and Countermeasures: An IoT Perspective

RAD‐EI: A routing attack detection scheme for edge‐based Internet of Things environment

Taxonomy of IoT Vulnerabilities

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