MARINE: Man-in-the-middle attack resistant trust model IN connEcted vehicles

Vehicular Ad-hoc NETwork (VANET), a novel technology holds a paramount importance within the transportation domain due to its abilities to increase traffic efficiency and safety. Connected vehicles propagate sensitive information which must be shared with the neighbors in a secure environment. However, VANET may also include dishonest nodes such as Man-in-the-Middle (MiTM) attackers aiming to distribute and share malicious content with the vehicles, thus polluting the network with compromised information. In this regard, establishing trust among connected vehicles can increase security as every participating vehicle will generate and propagate authentic, accurate and trusted content within the network. In this paper, we propose a novel trust model, namely, Man-in-the-middle Attack Resistance trust model IN connEcted vehicles (MARINE), which identifies dishonest nodes performing MiTM attacks in an efficient way as well as revokes their credentials. Every node running MARINE system first establishes trust for the sender by performing multi-dimensional plausibility checks. Once the receiver verifies the trustworthiness of the sender, the received data is then evaluated both directly and indirectly. Extensive simulations are carried out to evaluate the performance and accuracy of MARINE rigorously across three MiTM attacker models and the bench-marked trust model. Simulation results show that for a network containing 35% MiTM attackers, MARINE outperforms the state of the art trust model by 15%, 18%, and 17% improvements in precision, recall and F-score, respectively.N/A

Anonymous Authentication Against Man-In-The-Middle Attack

Evolving enterprise in application and data with flexible and scalable infrastructure in cloud services could improve efficiency and productivity of a business operation. Cloud services also offer resource sharing, data storage and application platform as on-demand services that could reduce the operational expenditure. Nevertheless, increasing usage and accessibility to the cloud services require strong security control to preserve user’s privacy and data integrity due to network communication vulnerabilities. There are many possible attacks that could cause security breach and abuse the user’s identity, leading to illegal access to the server. Man-inthe-middle attack is one of the attacks that can intercept communication between users and collect all users’ information. The attacker can misuse the information and act as a legal user to gain access to the system. It is a big challenge to preserve user’s privacy and provide protection from malicious attack. This paper proposes anonymous authentication scheme to preserve user’s privacy and provide protection to such possible attacks. The proposed scheme also provides secure mutual authentication, anonymity, session key establishment and non-dependency with the third party. The proposed scheme uses password-based authentication as an authentication method with anonymity feature to preserve user’s privacy. Experiment was conducted to test and validate the proposed scheme with man-in-the-middle attack. The result of the experiment shows that the proposed scheme is able to provide the privacy to mitigate and successfully preserve the user’s identity from the attack

Cross-validation based man-in-the-middle attack protection

A thesis submitted to the University of Bedfordshire, in fulfilment of the requirements for the degree of Master of Science by researchIn recent years, computer network has widely used in almost all areas of our social life. It has been profoundly changing the way of our living. However, various network attacks have become an increasingly problem at the same time. In local area networks, Man-in-the-Middle attack, as one kind of ARP attack, is the most common attack. This research implemented a cross-validation based Man-in-the-Middle attack protection method (CVP). This approach enables a host to check whether another host that responds the initialising host with an ARP reply packet is genuine. It then allows the ARP cache table of the initialising hosts to be updated with the MAC address and IP address pairs of the genuine host and to place the MAC address of inauthentic hosts into a blacklist. This research introduced ARP and ICMP firstly, including the structure of ARP and ICMP packets, and their workflows. Secondly, this research discussed the types of ARP attacks and the existing ARP attacks protection methods, including their principles, applicable environment, advantages and disadvantages. Then, this research proposed and implemented a cross-validation based Man-in-the-Middle attack protection method. Simulations and experiments were performed to examine the effect of CVP method. The results show the effectiveness of the proposed cross-validation based method in protecting network from Man-in-the-Middle attack. Compared with the existing Man-in-the-Middle attack protection methods, CVP requires no extra devices and administration, leading to more secure local area networks and low cost. It also has made a “tabu” to attackers. That is, it places the MAC address of attackers into a blacklist. So they will be identified immediately if they try to attack the network again

Detecting and Locating Man-in-the-Middle Attacks in Fixed Wireless Networks

We propose a novel method to detect and locate a Man-in-the-Middle attack in a fixed wireless network by analyzing round-trip time and measured received signal strength from fixed access points. The proposed method was implemented as a client-side application that establishes a baseline for measured round trip time (RTTs) and received signal strength (RSS) under no-threat scenarios and applies statistical measures on the measured RTT and RSS to detect and locate Man-in-the-Middle attacks.We show empirically that the presence of a Man-in-the-Middle attack incurs a significantly longer delay and larger standard deviation in measured RTT compared to that measured without a Man-in-the-Middle attack.We evaluated three machine learning algorithms on the measured RSS dataset to estimate the location of a Man-in-the-Middle attacker.Experimental results show that the proposed method can effectively detect and locate a Man-in-the-Middle attack and achieves a mean location estimation error of 0.8 meters in an indoor densely populated metropolitanenvironment.</p