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

Enhancing service quality and reliability in intelligent traffic system

Intelligent Traffic Systems (ITS) can manage on-road traffic efficiently based on real-time traffic conditions, reduce delay at the intersections, and maintain the safety of the road users. However, emergency vehicles still struggle to meet their targeted response time, and an ITS is vulnerable to various types of attacks, including cyberattacks. To address these issues, in this dissertation, we introduce three techniques that enhance the service quality and reliability of an ITS. First, an innovative Emergency Vehicle Priority System (EVPS) is presented to assist an Emergency Vehicle (EV) in attending the incident place faster. Our proposed EVPS determines the proper priority codes of EV based on the type of incidents. After priority code generation, EVPS selects the number of traffic signals needed to be turned green considering the impact on other vehicles gathered in the relevant adjacent cells. Second, for improving reliability, an Intrusion Detection System for traffic signals is proposed for the first time, which leverages traffic and signal characteristics such as the flow rate, vehicle speed, and signal phase time. Shannon’s entropy is used to calculate the uncertainty associated with the likelihood of particular evidence and Dempster-Shafer (DS) decision theory is used to fuse the evidential information. Finally, to improve the reliability of a future ITS, we introduce a model that assesses the trust level of four major On-Board Units (OBU) of a self-driving car along with Global Positioning System (GPS) data and safety messages. Both subjective logic (DS theory) and CertainLogic are used to develop the theoretical underpinning for estimating the trust value of a self-driving car by fusing the trust value of four OBU components, GPS data and safety messages. For evaluation and validation purposes, a popular and widely used traffic simulation package, namely Simulation of Urban Mobility (SUMO), is used to develop the simulation platform using a real map of Melbourne CBD. The relevant historical real data taken from the VicRoads website were used to inject the traffic flow and density in the simulation model. We evaluated the performance of our proposed techniques considering different traffic and signal characteristics such as occupancy rate, flow rate, phase time, and vehicle speed under many realistic scenarios. The simulation result shows the potential efficacy of our proposed techniques for all selected scenarios.Doctor of Philosoph

Reputation systems and secure communication in vehicular networks

A thorough review of the state of the art will reveal that most VANET applications rely on Public Key Infrastructure (PKI), which uses user certificates managed by a Certification Authority (CA) to handle security. By doing so, they constrain the ad-hoc nature of the VANET imposing a frequent connection to the CA to retrieve the Certificate Revocation List (CRL) and requiring some degree of roadside infrastructure to achieve that connection. Other solutions propose the usage of group signatures where users organize in groups and elect a group manager. The group manager will need to ensure that group members do not misbehave, i.e., do not spread false information, and if they do punish them, evict them from the group and report them to the CA; thus suffering from the same CRL retrieval problem. In this thesis we present a fourfold contribution to improve security in VANETs. First and foremost, Chains of Trust describes a reputation system where users disseminate Points of Interest (POIs) information over the network while their privacy remains protected. It uses asymmetric cryptography and users are responsible for the generation of their own pair of public and private keys. There is no central entity which stores the information users input into the system; instead, that information is kept distributed among the vehicles that make up the network. On top of that, this system requires no roadside infrastructure. Precisely, our main objective with Chains of Trust was to show that just by relying on people¿s driving habits and the sporadic nature of their encounters with other drivers a successful reputation system could be built. The second contribution of this thesis is the application simulator poiSim. Many¿s the time a new VANET application is presented and its authors back their findings using simulation results from renowned networks simulators like ns-2. The major issue with network simulators is that they were not designed with that purpose in mind and handling simulations with hundreds of nodes requires a massive processing power. As a result, authors run small simulations (between 50 and 100 nodes) with vehicles that move randomly in a squared area instead of using real maps, which rend unrealistic results. We show that by building tailored application simulators we can obtain more realistic results. The application simulator poiSim processes a realistic mobility trace produced by a Multi-agent Microscopic Traffic Simulator developed at ETH Zurich, which accurately describes the mobility patterns of 259,977 vehicles over regional maps of Switzerland for 24 hours. This simulation runs on a desktop PC and lasts approximately 120 minutes. In our third contribution we took Chains of Trust one step further in the protection of user privacy to develop Anonymous Chains of Trust. In this system users can temporarily exchange their identity with other users they trust, thus making it impossible for an attacker to know in all certainty who input a particular piece of information into the system. To the best of our knowledge, this is the first time this technique has been used in a reputation system. Finally, in our last contribution we explore a different form of communication for VANETs. The vast majority of VANET applications rely on the IEEE 802.11p/Wireless Access in Vehicular Environments (WAVE) standard or some other form of radio communication. This poses a security risk if we consider how vulnerable radio transmission is to intentional jamming and natural interferences: an attacker could easily block all radio communication in a certain area if his transmitter is powerful enough. Visual Light Communication (VLC), on the other hand, is resilient to jamming over a wide area because it relies on visible light to transmit information and ,unlike WAVE, it has no scalability problems. In this thesis we show that VLC is a secure and valuable form of communication in VANETs

Data Substantiation in Mobility

The world is embracing the presence of connected autonomous vehicles which are expected to play a major role in the future of intelligent transport systems. Given such connectivity, vehicles in the networks are vulnerable to making incorrect decisions due to anomalous data. No sophisticated attacks are required; just a vehicle reporting anomalous speeds would be enough to disrupt the entire traffic flow. Detection of such anomalies is vital to ensure the security of a vehicular network. This thesis proposes the use of traffic flow theory for anomalous data detection in vehicular networks, by evaluating the consistency of microscopic parameters which are derived by traffic flow theory with macroscopic views of traffic under different traffic conditions. Though little attention has been given to using traffic flow properties to determine anomalous basic safety message data, the fundamental nature of traffic flow properties makes it a robust assessment tool. The aim of this thesis is to develop a robust data substantiation framework for vehicular networks using traffic flow fundamentals. The aim is fulfilled in three objectives; (1) to provide an overview of the context in terms of existing data substantiation methods, vehicular communication, and traffic flow theory, (2) to develop data substantiation models to detect anomalies irrespective of the cause of the anomality, and (3) to assess the applicability of traffic flow theory for data substantiation in vehicular networks. Chapters 1 and 2 are introductions and literature reviews respectively. The first main chapter describes the context of vehicular networks, traffic flow theory, and the intuition of applying traffic flow theory for substantiation in vehicular networks. The next three chapters elaborate, formulate, demonstrate, and evaluate the use of macroscopic views of traffic to substantiate microscopic data in vehicular networks. The first of these discusses the use of steady state conditions in traffic flow theory to substantiate data in vehicular networks, and the second describes the use of shockwave theory in traffic to substantiate data in vehicular networks. The third chapter develops a data substantiation model utilising localised views of traffic to provide an additional resolution to the previous models

An intelligent intrusion detection system for external communications in autonomous vehicles

Advancements in computing, electronics and mechanical systems have resulted in the creation of a new class of vehicles called autonomous vehicles. These vehicles function using sensory input with an on-board computation system. Self-driving vehicles use an ad hoc vehicular network called VANET. The network has ad hoc infrastructure with mobile vehicles that communicate through open wireless channels. This thesis studies the design and implementation of a novel intelligent intrusion detection system which secures the external communication of self-driving vehicles. This thesis makes the following four contributions: It proposes a hybrid intrusion detection system to protect the external communication in self-driving vehicles from potential attacks. This has been achieved using fuzzification and artificial intelligence. The second contribution is the incorporation of the Integrated Circuit Metrics (ICMetrics) for improved security and privacy. By using the ICMetrics, specific device features have been used to create a unique identity for vehicles. Our work is based on using the bias in on board sensory systems to create ICMetrics for self-driving vehicles. The incorporation of fuzzy petri net in autonomous vehicles is the third contribution of the thesis. Simulation results show that the scheme can successfully detect denial-of-service attacks. The design of a clustering based hierarchical detection system has also been presented to detect worm hole and Sybil attacks. The final contribution of this research is an integrated intrusion detection system which detects various attacks by using a central database in BusNet. The proposed schemes have been simulated using the data extracted from trace files. Simulation results have been compared and studied for high levels of detection capability and performance. Analysis shows that the proposed schemes provide high detection rate with a low rate of false alarm. The system can detect various attacks in an optimised way owing to a reduction in the number of features, fuzzification

A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks

Advances in Vehicle-to-Everything (V2X) technology and onboard sensors have significantly accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G has enabled Ultra-Reliable Low Latency Communications (URLLC) to CAVs. However, while communication performance has been enhanced, security and privacy issues have increased. Attacks have become more aggressive, and attackers have become more strategic. Public Key Infrastructure (PKI) proposed by standardization bodies cannot solely defend against these attacks. Thus, in complementary of that, sophisticated systems should be designed to detect such attacks and attackers. Machine Learning (ML) has recently emerged as a key enabler to secure future roads. Various V2X Misbehavior Detection Systems (MDSs) have adopted this paradigm. However, analyzing these systems is a research gap, and developing effective ML-based MDSs is still an open issue. To this end, this paper comprehensively surveys and classifies ML-based MDSs as well as discusses and analyses them from security and ML perspectives. It also provides some learned lessons and recommendations for guiding the development, validation, and deployment of ML-based MDSs. Finally, this paper highlighted open research and standardization issues with some future directions

A Trust Management Framework for Vehicular Ad Hoc Networks

The inception of Vehicular Ad Hoc Networks (VANETs) provides an opportunity for road users and public infrastructure to share information that improves the operation of roads and the driver experience. However, such systems can be vulnerable to malicious external entities and legitimate users. Trust management is used to address attacks from legitimate users in accordance with a user’s trust score. Trust models evaluate messages to assign rewards or punishments. This can be used to influence a driver’s future behaviour or, in extremis, block the driver. With receiver-side schemes, various methods are used to evaluate trust including, reputation computation, neighbour recommendations, and storing historical information. However, they incur overhead and add a delay when deciding whether to accept or reject messages. In this thesis, we propose a novel Tamper-Proof Device (TPD) based trust framework for managing trust of multiple drivers at the sender side vehicle that updates trust, stores, and protects information from malicious tampering. The TPD also regulates, rewards, and punishes each specific driver, as required. Furthermore, the trust score determines the classes of message that a driver can access. Dissemination of feedback is only required when there is an attack (conflicting information). A Road-Side Unit (RSU) rules on a dispute, using either the sum of products of trust and feedback or official vehicle data if available. These “untrue attacks” are resolved by an RSU using collaboration, and then providing a fixed amount of reward and punishment, as appropriate. Repeated attacks are addressed by incremental punishments and potentially driver access-blocking when conditions are met. The lack of sophistication in this fixed RSU assessment scheme is then addressed by a novel fuzzy logic-based RSU approach. This determines a fairer level of reward and punishment based on the severity of incident, driver past behaviour, and RSU confidence. The fuzzy RSU controller assesses judgements in such a way as to encourage drivers to improve their behaviour. Although any driver can lie in any situation, we believe that trustworthy drivers are more likely to remain so, and vice versa. We capture this behaviour in a Markov chain model for the sender and reporter driver behaviours where a driver’s truthfulness is influenced by their trust score and trust state. For each trust state, the driver’s likelihood of lying or honesty is set by a probability distribution which is different for each state. This framework is analysed in Veins using various classes of vehicles under different traffic conditions. Results confirm that the framework operates effectively in the presence of untrue and inconsistent attacks. The correct functioning is confirmed with the system appropriately classifying incidents when clarifier vehicles send truthful feedback. The framework is also evaluated against a centralized reputation scheme and the results demonstrate that it outperforms the reputation approach in terms of reduced communication overhead and shorter response time. Next, we perform a set of experiments to evaluate the performance of the fuzzy assessment in Veins. The fuzzy and fixed RSU assessment schemes are compared, and the results show that the fuzzy scheme provides better overall driver behaviour. The Markov chain driver behaviour model is also examined when changing the initial trust score of all drivers