660 research outputs found
Data-centric Misbehavior Detection in VANETs
Detecting misbehavior (such as transmissions of false information) in
vehicular ad hoc networks (VANETs) is very important problem with wide range of
implications including safety related and congestion avoidance applications. We
discuss several limitations of existing misbehavior detection schemes (MDS)
designed for VANETs. Most MDS are concerned with detection of malicious nodes.
In most situations, vehicles would send wrong information because of selfish
reasons of their owners, e.g. for gaining access to a particular lane. Because
of this (\emph{rational behavior}), it is more important to detect false
information than to identify misbehaving nodes. We introduce the concept of
data-centric misbehavior detection and propose algorithms which detect false
alert messages and misbehaving nodes by observing their actions after sending
out the alert messages. With the data-centric MDS, each node can independently
decide whether an information received is correct or false. The decision is
based on the consistency of recent messages and new alert with reported and
estimated vehicle positions. No voting or majority decisions is needed, making
our MDS resilient to Sybil attacks. Instead of revoking all the secret
credentials of misbehaving nodes, as done in most schemes, we impose fines on
misbehaving nodes (administered by the certification authority), discouraging
them to act selfishly. This reduces the computation and communication costs
involved in revoking all the secret credentials of misbehaving nodes.Comment: 12 page
The Mason Test: A Defense Against Sybil Attacks in Wireless Networks Without Trusted Authorities
Wireless networks are vulnerable to Sybil attacks, in which a malicious node
poses as many identities in order to gain disproportionate influence. Many
defenses based on spatial variability of wireless channels exist, but depend
either on detailed, multi-tap channel estimation - something not exposed on
commodity 802.11 devices - or valid RSSI observations from multiple trusted
sources, e.g., corporate access points - something not directly available in ad
hoc and delay-tolerant networks with potentially malicious neighbors. We extend
these techniques to be practical for wireless ad hoc networks of commodity
802.11 devices. Specifically, we propose two efficient methods for separating
the valid RSSI observations of behaving nodes from those falsified by malicious
participants. Further, we note that prior signalprint methods are easily
defeated by mobile attackers and develop an appropriate challenge-response
defense. Finally, we present the Mason test, the first implementation of these
techniques for ad hoc and delay-tolerant networks of commodity 802.11 devices.
We illustrate its performance in several real-world scenarios
Detection of Sybil attack in vehicular ad hoc networks by analyzing network performance
Vehicular ad hoc network (VANET) is an emerging technology which can be very helpful for providing safety and security as well as for intelligent transportation services. But due to wireless communication of vehicles and high mobility it has certain security issues which cost the safety and security of people on the road. One of the major security concerns is the Sybil attack in which the attacker creates dummy identities to gain high influence in the network that causes delay in some services and fake voting in the network to misguide others. The early detection of this attack can prevent people from being misguided by the attacker and save them from getting into any kind of trap. In this research paper, Sybil attack is detected by first applying the Poisson distribution algorithm to predict the traffic on the road and in the second approach, analysis of the network performance for packet delivery ratio (PDR) is performed in malign and benign environment. The simulation result shows that PDR decreases in presence of fake vehicles in the network. Our approach is simple and effective as it does not require high computational overhead and also does not violate the privacy issues of people in the network
Analysis of cyber risk and associated concentration of research (ACR)ยฒ in the security of vehicular edge clouds
Intelligent Transportation Systems (ITS) is a rapidly growing research space with many issues and challenges. One of the major concerns is to successfully integrate connected technologies, such as cloud infrastructure and edge cloud, into ITS. Security has been identified as one of the greatest challenges for the ITS, and security measures require consideration from design to implementation. This work focuses on providing an analysis of cyber risk and associated concentration of research (ACR2). The introduction of ACR2 approach can be used to consider research challenges in VEC and open up further investigation into those threats that are important but under-researched. That is, the approach can identify very high or high risk areas that have a low research concentration. In this way, this research can lay the foundations for the development of further work in securing the future of ITS
Security in Wireless Sensor Networks: Issues and Challenges
Wireless Sensor Network (WSN) is an emerging technology that shows great
promise for various futuristic applications both for mass public and military.
The sensing technology combined with processing power and wireless
communication makes it lucrative for being exploited in abundance in future.
The inclusion of wireless communication technology also incurs various types of
security threats. The intent of this paper is to investigate the security
related issues and challenges in wireless sensor networks. We identify the
security threats, review proposed security mechanisms for wireless sensor
networks. We also discuss the holistic view of security for ensuring layered
and robust security in wireless sensor networks.Comment: 6 page
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