Innovative survey of defense machinery against Sybil attacks over wireless ad hoc network on IoT

Abstract

Integrating IoT with Wireless Ad hoc Network (WANET) capabilities can solve several problems. However, because they both rely on identity nodes to communicate with each other, they are both vulnerable to Sybil attacks. Sybil attackers illegally change into several different identities (attackers) to carry out various malicious activities such as damaging data aggregation, voting, and disrupting routing. Several defense machineries have been proposed for Sybil attacks on WANET, which are mostly based on cryptography, location or position, network behavior, resource testing, and trust. However, the drawbacks are that not all machinery are suitable for use in networks with limited resources. This paper presents a survey, classification, and comparison of various defense machineries that have been proposed for non-IoT WANETs. The author emphasizes the issue of the advantages and disadvantages of this defense mechanism when applied to the IoT infrastructure and how each method can effectively recognize properties of Sybil attacks.

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

“…There are quite a number of published articles that outline different Sybil attack detection methods that are applicable to slightly different IoT domains; several recent survey papers summarize these into their associated categories. 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].…”

“…In the range-free methods, high accuracy location is calculated based on data supplied through external means, such as Global Positioning System (GPS), radar or other localization schemes. The range-based methods, however, generally can work simply by using data obtainable from the physical layer characteristics of the radio signals being sent and received [19]. There are several reasons why methods that use intrinsically generated physical layer data to detect Sybil attack might be more preferable than others.…”

“…With the use of only two monitoring nodes at fixed locations while still able to achieve a high detection accuracy of above 91% on average, it supports our hypothesis that such a minimal number of nodes may already be sufficient when assisted by a machine learning mechanism. To provide further illustration, TABLE I summarizes the contribution of our proposed scheme compared with the existing Sybil attack detection approaches described by Singh [19].…”