A survey of local/cooperative-based malicious information detection techniques in VANETs

Abstract

Vehicular ad hoc networks (VANETs) are emerged technology where vehicles and roadside units (RSUs) communicate with each other. VANETs can be categorized as a subbranch of mobile ad hoc networks (MANETs). VANETs help to improve traffic efficiency and safety and provide infotainment facility as well. The dissemination of messages must be relayed through nodes in VANETs. However, it is possible that a node may propagate false information in a network due to its malicious behaviour or selfishness. False information in VANETs can change drivers' behaviour and create disastrous consequences in the network. Therefore, sometimes false safety messages may endanger human life. To avoid any lass, it is more important to detect and avoid false messages. This paper has explained some important algorithms that can detect false messages in VANETs. The categorization of false message detection schemes based on local and cooperative behaviour has been presented in this article. The limitations and consequences of existing schemes as well as future work has been discussed. Keywords: VANETs, MANETs, Misbehaviour, False message detection, Security ReviewThis article analyses Single/local and Cooperative based malicious information detection techniques in VANETs. Single/local based detection schemes are further comprised of plausibility, consistency and single node behavior. However, these techniques performances are not upto the mark, because of single node reliance. The cooperative based detection techniques are more efficient than Single/local based detection schemes. The prerequisites for these scheme need more nodes, unlike Single/local based detection schemes. These techniques are classified based on consistency and behavior with neighbors. Trust-based detection techniques are analysed on previous communication history. However, these scheme needs a honest and sufficient number of nodes for reliable result.

A taxonomy on misbehaving nodes in delay tolerant networks

“…Moreover, this would lead to nodes unavailability, low packet delivery ratio and bogus messages in DTNs. However, the trivial mitigation algorithms in VANETs [65]- [69], Mobile Ad-hoc NETworks (MANETs) [70]- [73], Wireless Sensor Networks (WSNs) [74]- [77], TCP/IP [78]- [82] and UWSNs [83]- [86] are not applicable in DTNs, due to long delay and frequent intermittent connectivity.…”

Securing Vehicle-to-Everything (V2X) Communication Platforms

“…2) Sybil Attacks: This is a well-known harmful attack in wireless vehicular networks where a vehicle pretends to have more than one identify (e.g., multiple certified key-pairs) either at the same time or in succession [64]. Sybil attackers may also launch DoS attacks, waste network bandwidth, destabilize the overall network and pose threats to safety [57], [65]. For instance, if a malicious vehicle changes its identity, it may use multiple pseudonyms to appear as a different, moving vehicle or make it appear that the road is congested (even though it is not) and send incorrect information about the road conditions to neighbouring vehicles/RSUs.…”

Detecting false messages in vehicular ad hoc networks based on a traffic flow model

“…P(y = 0jX ) and P(y = 1jX ) can be calculated by equation (3). For the upstream and downstream segments, two probabilities, P up (yjX ) and P down (yjX ), can be obtained.…”

“…In the detecting process, the PDFs can be used by vehicles to calculate the conditional probability p(X jy = 0) and p(X jy = 1) using their observed density X as inputs. Furthermore, the likelihood of each traffic patterns p(yjX ) can be evaluated using equation (3). In summary, the detailed description of the proposed algorithm is given in Algorithm 1.…”

“…Inside attackers can inject false messages into the network when they are motivated out of selfishness to make a malicious attack. 3 For example, by reporting a bogus traffic congestion, a selfish vehicle may try to create the illusion of traffic congestion to mislead other vehicles to exit the current road in order to reach their destination faster. In addition, hardware faults can also result in incorrect data.…”