Is it Really Easy to Detect Sybil Attacks in C-ITS Environments: A Position Paper

Hammi, Badis; Idir, Yacine Mohamed; Zeadally, Sherali; Khatoun, Rida; Nebhen, Jamel

doi:10.1109/tits.2022.3165513

Cited by 15 publications

(4 citation statements)

References 67 publications

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“…Meanwhile, their scheme fails to resist the infamous Sybil attack and merely supports the simple average of feedback score ciphertexts. However, the Sybil attack will greatly disturb the normal operations in a reputation management system [29], [30], and as revealed in many recent researches [5], [14], [31], the simple average will provide obviously weaker robustness against malicious feedback providers than the weighted average, in which the reputation values of feedback providers are adopted as important weights.…”

Section: Related Workmentioning

confidence: 99%

PPRU: A Privacy-Preserving Reputation Updating Scheme for Cloud-Assisted Vehicular Networks

Liu,

Wan,

Guo

et al. 2024

IEEE Trans. Veh. Technol.

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Vehicular networks have huge potential to improve road safety and traffic efficiency, especially in the context of large models. Cloud computing can significantly improve the performance of vehicular networks, and the concept of cloud-assisted vehicular networks comes into being. Reputation management plays a crucial role in vehicular networks, since it can help each vehicle evaluate the trustworthiness of the other vehicles and the received messages. Reputation updating is essential in reputation management and it is usually done by the Trusted Authority (TA) regularly after collecting, decrypting, and verifying a large number of reputation feedbacks, which leads to great computation and communication overheads on the TA side and even makes the TA become the bottleneck of reputation management system. In this paper, we propose a novel Privacy-Preserving Reputation Updating (PPRU) scheme for cloud-assisted vehicular networks based on the Elliptic Curve Cryptography (ECC) and Paillier algorithms, in which the reputation feedbacks are collected and preprocessed by the honest-but-curious Cloud Service Provider (CSP) in a privacypreserving manner, and the computation and communication overheads on the TA side can be dramatically reduced by about 88.36% and 83.88% as a result, respectively. Meanwhile, the proposed scheme can provide strong privacy preservation, strong security, and robust reputation management with acceptable computation and communication overheads. Furthermore, the comprehensive theoretical analysis and simulation evaluation are conducted, and the results demonstrate that the proposed scheme is significantly superior to the existing schemes in several aspects.

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Section: Related Workmentioning

confidence: 99%

PPRU: A Privacy-Preserving Reputation Updating Scheme for Cloud-Assisted Vehicular Networks

Liu,

Wan,

Guo

et al. 2024

IEEE Trans. Veh. Technol.

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“…With this majority, they can influence the validation process of new blocks, cancel transactions by creating longer branches of the chain, or double-spend by confirming a transaction on the main chain and then canceling it on a chain they continue to develop in secret [33]. Sybil attack: it focuses on the creation of a large number of false identities by a single attacker in a decentralized network [34]. The attacker uses these identities to gain disproportionate influence over the network by potentially affecting consensus or reputation mechanisms such as manipulating votes for protocol changes, or engaging in behavior that degrades network trust and security, like fraudulently confirming invalid transactions or blocks [35].…”

Section: Overview Of Security Concerns Associated With Blockchain Tec...mentioning

confidence: 99%

Blockchain Technology in the Healthcare Sector: Overview and Security Analysis

Madhoun,

Hammi

2024

2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)

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Today, the healthcare sector is suffering from multiple security vulnerabilities that make it vulnerable to various types of cyberattacks. Therefore, robust security solutions need to be implemented in order to resolve these vulnerabilities. In this context, blockchain technology has emerged as a promising solution in several sectors, including the healthcare sector. It ensures enhanced security and greater transparency. It also boosts patient confidence and optimizes operational efficiency. In this paper, we first present an overview of the main classic healthcare applications: medical records management, traceability of medicines, and research and clinical trials. We then present a detailed analysis of the critical security vulnerabilities threatening these applications. Afterwards, we explain how blockchain technology can help to address these vulnerabilities. Finally, we discuss the various possible methods that aim to prevent traditional blockchain-related attacks in these healthcare applications and how blockchain technology is impacting and transforming the healthcare sector.

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“…Recent surveys on Sybil attack detection in wireless ad hoc networks and wireless sensor networks can be found in Arshad et al [12], Vasudeva and Sood [18], and Singh [19]. There are also several recent survey papers on Sybil attack detection in VANETs, including Shobana and Arockia [20], Zhang et al [21], Velayudhan and Anitha [22], and Hammi et al [23].…”

Section: B Sybil Attack Detection In Ioftmentioning

confidence: 99%

“…Furthermore, the ways in which some of these schemes operate impose various other undesirable constraints. For example, schemes such as Kabbur and Kumar [24] and Yuan et al [25] use RSS indication values obtained through triangulation, requiring at least three monitoring nodes to be used [12] [23]. Other examples include schemes like Lv et al [26], Abbas et al [27] and Angappan et al [28], which require the use of additional localization information such as those obtainable through neighbors of the suspicious nodes [12]; consequently, unlike schemes that purely and directly use intrinsically generated physical layer data, these schemes may be more susceptible to attacks involving information spoofing.…”

Section: B Sybil Attack Detection In Ioftmentioning

confidence: 99%

Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

Chulerttiyawong

Jamalipour

2023

IEEE Internet Things J.

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Sybil attack refers to the situation when a malicious node falsely claims to have numerous identities and is known to be one of the security threats to the Internet of Things (IoT). Due to recent increase usage of unmanned aerial vehicles (UAVs) in various applications, Sybil attack has been identified as a threat to the flying ad hoc network (FANET) paradigm and its integration with the IoT to form the Internet of Flying Things (IoFT). In this paper, we propose an intelligent Sybil attack detection approach for FANETs-based IoFT using physical layer characteristics of the radio signals emitted from the UAVs as detected by two ground nodes. A supervised machine learning approach is employed and experimented with several different classifiers available in the Weka workbench platform. The experiment was carried out based on two features of the radio signals, namely, the received signal strength difference (RSSD) and the time difference of arrival (TDoA). Simulation results revealed that the proposed scheme can achieve a high correct classification accuracy of above 91% on average, even for smart malicious nodes with power control capability operating at power levels not directly trained. In addition to its high performance, the proposed scheme is also less susceptible to various attacks commonly carried out on the upper layers, such as data spoofing, due to the use of only intrinsically generated physical layer data. Furthermore, no additional communications overheads of the UAV nodes are required for the functionality of this scheme.

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Is it Really Easy to Detect Sybil Attacks in C-ITS Environments: A Position Paper

Cited by 15 publications

References 67 publications

PPRU: A Privacy-Preserving Reputation Updating Scheme for Cloud-Assisted Vehicular Networks

PPRU: A Privacy-Preserving Reputation Updating Scheme for Cloud-Assisted Vehicular Networks

Blockchain Technology in the Healthcare Sector: Overview and Security Analysis

Sybil Attack Detection in Internet of Flying Things-IoFT: A Machine Learning Approach

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