Robust distributed privacy-preserving secure aggregation in vehicular communication

Vehicular ad hoc networks (VANETs), formed by computers embedded in vehicles and the traffic infrastructure, are expected to develop in the near future to improve traffic safety and efficiency. To this end, VANETs should be designed to be resistant against various abuses and attacks. In this paper, we first review the existing proposals to provide security, privacy, and data aggregation in vehicle-to-vehicle communication. We then address the fundamental issue of achieving these conflicting properties in a unified solution, having observed that separate efforts cannot fulfill the VANET design objectives. A set of new mechanisms are suggested for efficiently managing identities and securely compressing cryptographic witnesses, which are among the major obstacles to the deployment of strong security mechanisms in VANETs. Finally, we employ the standard threshold cryptographic technology to improve the basic protocol with robustness

Hardware Assisted Solutions for Automobile Security

In the past couple of decades, many in-vehicle features have been invented and deployed in order to make modern vehicles which not only safer and more reliable but also connected, smarter, and intelligent. Meanwhile, vehicular ad-hoc networks (VANETs) are proposed to provide communications between vehicles and road-side stations as the foundation of the intelligent transportation system to provide efficient and safe transportation. To support these updated functions, a large amount of electronic equipment has been integrated into the car system. Although these add-on functions around vehicles offer great help in driving assistance, they inevitably introduced new security vulnerabilities that threaten the safety of the on-board drivers, passengers and pedestrians. This has been demonstrated by many well-documented attacks either on the in-vehicle bus system or on the wireless vehicular network communications. In this dissertation, we design and implement several hardware-oriented solutions to the arousing security issues on vehicles. More specifically, we focus on three important and representative problems: (1) how to secure the in-vehicle Controller Area Network (CAN), (2) how to secure the communication between vehicle and outside, and (3) how to establish trust on VANETs. Current approaches based on cryptographic algorithms to secure CAN bus violate the strict timing and limited resource constraints for CAN communications. We thus emphasize on the alternate solution of intrusion detection system (IDS) in this dissertation. We explore monitoring the changes of CAN message content or the physical delay of its transmission to detect on the CAN bus. We first propose a new entropy-based IDS following the observation that all the known CAN message injection attacks need to alter the CAN identifier bit. Thus, analyzing the entropy changes of such bits can be an effective way to detect those attacks. Next, we develop a delay-based IDS to protect the CAN network by identifying the location of the compromised Electronic Control Unit (ECU) from the transmission delay difference to two terminals connected to the CAN bus. We demonstrate that both approaches can protect the integrity of the messages on CAN bus leading to a further improve the security and safety of autonomous vehicles. In the second part of this dissertation, we consider Plug-and-Secure, an industrial practice on key management for automotive CAN networks. It has been proven to be information theoretically secure. However, we discover side-channel attacks based on the physical properties of the CAN bus that can leak almost the entire secret key bits. We analyze the fundamental characteristics that lead to such attacks and propose techniques to minimize information leakage at the hardware level. Next, we extend our study from in-vehicle secure CAN communication to the communication between vehicle and outside world. We take the example of the popular GPS spoofing attack and show how we can use the rich information from CAN bus to build a cross-validation system to detect such attacks. Our approach is based on the belief that the local driving data from the in-vehicle network can be authenticated and thus trusted by secure CAN networks mechanisms. Such data can be used to cross-validate the GPS signals from the satellite which are vulnerable to spoofing attacks. We conduct driving tests on real roads to show that our proposed approach can defend both GPS spoofing attacks and location-based attacks on the VANETs. Finally, we propose a blockchain based Anonymous Reputation System (BARS) to establish a privacy-preserving trust model for VANETs. The certificate and revocation transparency is implemented efficiently with the proofs of presence and absence based on the extended blockchain technology. To prevent the broadcast of forged messages, a reputation evaluation algorithm is presented relying on both direct historical interactions of that vehicle and indirect opinions from the other vehicles. This dissertation features solutions to vehicle security problems based on hardware or physical characteristics, instead of cryptographic algorithms. We believe that given the critical timing requirement on vehicular systems and their very limited resource (such as the bandwidth on CAN bus), this will be a very promising direction to secure vehicles and vehicular network

Location Privacy In Emerging Network-Based Applications

With the wide spread of computer systems and networks, privacy has become an issue that increasingly attracts attention. In wireless sensor networks, the location of an event source may be subject to unintentional disclosure through traffic analysis by the attacker. In vehicular networks, authentication leaves a trail to tie a driver to a sequence of time and space coordinates. In a cloud-based navigation system, the location information of a sensitive itinerary is disclosed. Those scenarios have shown that privacy protection is a far-reaching problem that could span many different aspects of a computer/network system, especially on a diversified landscape of such systems. To address privacy protection, we propose to look at the issue from three aspects. First, traffic analysis represents one class of problems. This is because in general, encryption can be applied to protect the information being transmitted but the pattern of transmission is harder to hide due to other constraints. To defeat traffic analysis, it is necessary to identify those constraints and decide the trade-off between them and privacy protection. We have shown that the threat to the source location privacy within a sensor network is directly related with the pattern of the routing protocol. Thus to completely remove that pattern, we propose to use a random walk to defeat the threat. Second, authentication is generally required to establish the identities of interacting parties in an electronical communication. But it unnecessarily reveals other private information when it is applied to a vehicular network. We propose to introduce tunable anonymity, through both asymmetric and symmetric encryption primitives, into the authentication process so that it provides $k$-anonymity. We further extend the scenario to mobile scenarios. At last, it is often perceived that private information has to be shared in order to obtain certain services. For example, source location and destination location have to be sent for looking up the shortest path between them. We show that it is possible to apply private information retrieval so that a service provider knows for whom it has provided service for accounting purposes, but not the details of the service. In general, the three aspects represent some basic aspects of privacy issues arising from using computer/network systems. Our approaches, while innovative for the scenarios discussed at hand, are general enough to be applied to similar scenarios

Software Protection and Secure Authentication for Autonomous Vehicular Cloud Computing

Artificial Intelligence (AI) is changing every technology we deal with. Autonomy has been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected coined as Autonomous Vehicles (AVs). Moreover, researchers found a way to make more use of these enormous capabilities and introduced Autonomous Vehicles Cloud Computing (AVCC). In these platforms, vehicles can lend their unused resources and sensory data to join AVCC. In this dissertation, we investigate security and privacy issues in AVCC. As background, we built our vision of a layer-based approach to thoroughly study state-of-the-art literature in the realm of AVs. Particularly, we examined some cyber-attacks and compared their promising mitigation strategies from our perspective. Then, we focused on two security issues involving AVCC: software protection and authentication. For the first problem, our concern is protecting client’s programs executed on remote AVCC resources. Such a usage scenario is susceptible to information leakage and reverse-engineering. Hence, we proposed compiler-based obfuscation techniques. What distinguishes our techniques, is that they are generic and software-based and utilize the intermediate representation, hence, they are platform agnostic, hardware independent and support different high level programming languages. Our results demonstrate that the control-flow of obfuscated code versions are more complicated making it unintelligible for timing side-channels. For the second problem, we focus on protecting AVCC from unauthorized access or intrusions, which may cause misuse or service disruptions. Therefore, we propose a strong privacy-aware authentication technique for users accessing AVCC services or vehicle sharing their resources with the AVCC. Our technique modifies robust function encryption, which protects stakeholder’s confidentiality and withstands linkability and “known-ciphertexts” attacks. Thus, we utilize an authentication server to search and match encrypted data by performing dot product operations. Additionally, we developed another lightweight technique, based on KNN algorithm, to authenticate vehicles at computationally limited charging stations using its owner’s encrypted iris data. Our security and privacy analysis proved that our schemes achieved privacy-preservation goals. Our experimental results showed that our schemes have reasonable computation and communications overheads and efficiently scalable

Deep Learning-Based Intrusion Detection Methods for Computer Networks and Privacy-Preserving Authentication Method for Vehicular Ad Hoc Networks

The incidence of computer network intrusions has significantly increased over the last decade, partially attributed to a thriving underground cyber-crime economy and the widespread availability of advanced tools for launching such attacks. To counter these attacks, researchers in both academia and industry have turned to machine learning (ML) techniques to develop Intrusion Detection Systems (IDSes) for computer networks. However, many of the datasets use to train ML classifiers for detecting intrusions are not balanced, with some classes having fewer samples than others. This can result in ML classifiers producing suboptimal results. In this dissertation, we address this issue and present better ML based solutions for intrusion detection. Our contributions in this direction can be summarized as follows: Balancing Data Using Synthetic Data to detect intrusions in Computer Networks: In the past, researchers addressed the issue of imbalanced data in datasets by using over-sampling and under-sampling techniques. In this study, we go beyond such traditional methods and utilize a synthetic data generation method called Con- ditional Generative Adversarial Network (CTGAN) to balance the datasets and in- vestigate its impact on the performance of widely used ML classifiers. To the best of our knowledge, no one else has used CTGAN to generate synthetic samples for balancing intrusion detection datasets. We use two widely used publicly available datasets and conduct extensive experiments and show that ML classifiers trained on these datasets balanced with synthetic samples generated by CTGAN have higher prediction accuracy and Matthew Correlation Coefficient (MCC) scores than those trained on imbalanced datasets by 8% and 13%, respectively. Deep Learning approach for intrusion detection using focal loss function: To overcome the data imbalance problem for intrusion detection, we leverage the specialized loss function, called focal loss, that automatically down-weighs easy ex- amples and focuses on the hard negatives by facilitating dynamically scaled-gradient updates for training ML models effectively. We implement our approach using two well-known Deep Learning (DL) neural network architectures. Compared to training DL models using cross-entropy loss function, our approach (training DL models using focal loss function) improved accuracy, precision, F1 score, and MCC score by 24%, 39%, 39%, and 60% respectively. Efficient Deep Learning approach to detect Intrusions using Few-shot Learning: To address the issue of imbalance the datasets and develop a highly effective IDS, we utilize the concept of few-shot learning. We present a Few-Shot and Self-Supervised learning framework, called FS3, for detecting intrusions in IoT networks. FS3 works in three phases. Our approach involves first pretraining an encoder on a large-scale external dataset in a selfsupervised manner. We then employ few-shot learning (FSL), which seeks to replicate the encoder’s ability to learn new patterns from only a few training examples. During the encoder training us- ing a small number of samples, we train them contrastively, utilizing the triplet loss function. The third phase introduces a novel K-Nearest neighbor algorithm that sub- samples the majority class instances to further reduce imbalance and improve overall performance. Our proposed framework FS3, utilizing only 20% of labeled data, out- performs fully supervised state-of-the-art models by up to 42.39% and 43.95% with respect to the metrics precision and F1 score, respectively. The rapid evolution of the automotive industry and advancements in wireless com- munication technologies will result in the widespread deployment of Vehicular ad hoc networks (VANETs). However, despite the network’s potential to enable intelligent and autonomous driving, it also introduces various attack vectors that can jeopardize its security. In this dissertation, we present efficient privacy-preserving authenticated message dissemination scheme in VANETs. Conditional Privacy-preserving Authentication and Message Dissemination Scheme using Timestamp based Pseudonyms: To authenticate a message sent by a vehicle using its pseudonym, a certificate of the pseudonym signed by the central authority is generally utilized. If a vehicle is found to be malicious, certificates associated with all the pseudonyms assigned to it must be revoked. Certificate revocation lists (CRLs) should be shared with all entities that will be corresponding with the vehicle. As each vehicle has a large pool of pseudonyms allocated to it, the CRL can quickly grow in size as the number of revoked vehicles increases. This results in high storage overheads for storing the CRL, and significant authentication overheads as the receivers must check their CRL for each message received to verify its pseudonym. To address this issue, we present a timestamp-based pseudonym allocation scheme that reduces the storage overhead and authentication overhead by streamlining the CRL management process

QUALITY-DRIVEN CROSS LAYER DESIGN FOR MULTIMEDIA SECURITY OVER RESOURCE CONSTRAINED WIRELESS SENSOR NETWORKS

The strong need for security guarantee, e.g., integrity and authenticity, as well as privacy and confidentiality in wireless multimedia services has driven the development of an emerging research area in low cost Wireless Multimedia Sensor Networks (WMSNs). Unfortunately, those conventional encryption and authentication techniques cannot be applied directly to WMSNs due to inborn challenges such as extremely limited energy, computing and bandwidth resources. This dissertation provides a quality-driven security design and resource allocation framework for WMSNs. The contribution of this dissertation bridges the inter-disciplinary research gap between high layer multimedia signal processing and low layer computer networking. It formulates the generic problem of quality-driven multimedia resource allocation in WMSNs and proposes a cross layer solution. The fundamental methodologies of multimedia selective encryption and stream authentication, and their application to digital image or video compression standards are presented. New multimedia selective encryption and stream authentication schemes are proposed at application layer, which significantly reduces encryption/authentication complexity. In addition, network resource allocation methodologies at low layers are extensively studied. An unequal error protection-based network resource allocation scheme is proposed to achieve the best effort media quality with integrity and energy efficiency guarantee. Performance evaluation results show that this cross layer framework achieves considerable energy-quality-security gain by jointly designing multimedia selective encryption/multimedia stream authentication and communication resource allocation

Revisiting the Feasibility of Public Key Cryptography in Light of IIoT Communications

Digital certificates are regarded as the most secure and scalable way of implementing authentication services in the Internet today. They are used by most popular security protocols, including Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS). The lifecycle management of digital certificates relies on centralized Certification Authority (CA)-based Public Key Infrastructures (PKIs). However, the implementation of PKIs and certificate lifecycle management procedures in Industrial Internet of Things (IIoT) environments presents some challenges, mainly due to the high resource consumption that they imply and the lack of trust in the centralized CAs. This paper identifies and describes the main challenges to implement certificate-based public key cryptography in IIoT environments and it surveys the alternative approaches proposed so far in the literature to address these challenges. Most proposals rely on the introduction of a Trusted Third Party to aid the IIoT devices in tasks that exceed their capacity. The proposed alternatives are complementary and their application depends on the specific challenge to solve, the application scenario, and the capacities of the involved IIoT devices. This paper revisits all these alternatives in light of industrial communication models, identifying their strengths and weaknesses, and providing an in-depth comparative analysis.This work was financially supported by the European commission through ECSEL-JU 2018 program under the COMP4DRONES project (grant agreement N∘ 826610), with national financing from France, Spain, Italy, Netherlands, Austria, Czech, Belgium and Latvia. It was also partially supported by the Ayudas Cervera para Centros Tecnológicos grant of the Spanish Centre for the Development of Industrial Technology (CDTI) under the project EGIDA (CER-20191012), and in part by the Department of Economic Development and Competitiveness of the Basque Government through the project TRUSTIND—Creating Trust in the Industrial Digital Transformation (KK-2020/00054)

Speed Offset Attack Detection in Vehicular Ad-Hoc Networks (VANETs) Using Machine Learning

An integral component of the Intelligent Transportation System (ITS) is the emerging technology called Vehicular ad-hoc network (VANET). VANET allows Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication wirelessly to improve road safety, traffic congestion, and information dissemination. Communication of vehicles in a VANET network is vulnerable to various attacks. Commonly used cryptographic techniques alone are insufficient to ensure and protect vehicle message integrity and authentication from insider attacks. In such cases, additional measures are necessary to ensure the correctness of the transmitted data. Each vehicle in the network periodically broadcasts a basic safety message (BSM) that contains essential status information about a vehicle, such as its position, speed, and heading to other vehicles and Road Side Units (RSU) to report its status. A speed offset attack is where an attacker (misbehaving vehicle) misleads the network by adding an offset value to its actual speed data in each BSM. Such attacks can result in traffic congestion and road accidents; therefore, it is essential to accurately detect and identify such attackers to ensure safety in the network. This research proposes a novel data-centric approach for detecting speed offset attacks using Machine Learning (ML) and Deep Learning (DL) algorithms. Vehicular Reference Misbehavior (VeReMi) Extension Dataset is used for this research. Preliminary results indicate that the proposed model can detect malicious nodes in the network quickly and accurately