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

Enhanced Obfuscation for Software Protection in Autonomous Vehicular Cloud Computing Platforms

Nowadays, sensors, communications connections, and more powerful computing capabilities are added to automobiles, making them more intelligent. The primary goal was to eliminate the need for human control, making them Autonomous Vehicles (AVs). Consequently, researchers thought to put all that newly added computational power to use for other endeavors. Hence, Autonomous Vehicular Cloud Computing (AVCC) models were introduced. Nevertheless, this goal is not an easy undertaking, the dynamic nature of autonomous vehicles introduces a critical challenge in the development of such a distributed computing platform. Furthermore, it presents far complicated issues as far as security and protection of services associated with this framework. In this paper, we center around securing programs running on AVCC. Here, we focus on timing side-channel attacks which aim to leak information about running code, which can be utilized to reverse engineer the program itself. We propose to mitigate these attacks via obfuscated compilation. In particular, we change the control flow of an input program at the compiler level, thereby changing the program’s apparent behavior and accompanying physical manifestations to hinder these attacks. We improve our previous ARM-based implementation to address its limitations and provide more comprehensive coverage for different programs. Our solution is software-based and generically portable - fitting different hardware platforms and numerous input program languages at the source level. Our findings prove a considerable improvement over our previous technique, which may provide more defense against timing side-channels

A Proposed Software Protection Mechanism for Autonomous Vehicular Cloud Computing

Cars are becoming smarter every day. They are being equipped with sensors, communications interfaces, and more powerful processing capabilities. The primary purpose was to enable these cars to drive themselves without any human interventions and become so-called Autonomous Vehicles (AVs). But why stop there, why not harness all that computing power for a greater collective purpose. That\u27s how the idea of Autonomous Vehicular Cloud Computing (AVCC) was born. Nonetheless, this is not a trivial task, the mobile and dynamic nature of vehicles poses a significant challenge in the formation and management of this cloud computing model and yet a more substantial challenge in terms of security and privacy of all the parties involved in this system. In this paper, we focus on protecting software running on AVCC. We use dynamic obfuscated compilation to complicate programs\u27 execution paths and hinder information leakage via side channels attacks. Relying on compilers offers advantages, such as the independence of architecture and support for a variety high-level programming languages and application simplicity with minimal set-up cost. Here, we introduce our system in the realm of ARM processor, which power AVCC. Then, we present execution statistics for simple standard programs. The results show tangible timing variations in diversified code versions for the same program, which may disrupt side-channel attacks

OJIT: A Novel Obfuscation Approach Using Standard Just-In-Time Compiler Transformations

International audienceWith the adoption of cloud computing, securing remote program execution becomes an important issue. Relying on standard data encryption is not enough, since code execution happens on remote servers, possibly allowing for eavesdropping from potential adversaries; thus the full execution process requires protection from such threats. In this paper, we introduce OJIT system as a novel approach for obfuscating programs, making it difficult for adversaries to reverse-engineer. The system exploits the JIT compilation technology to dynamically transform the code, making it constantly changing, thereby complicating the execution state. This paper quantitatively studies the effect of this approach by considering a set of obfuscation metrics borrowed from the software engineering field. The paper constructs a testbed system using the LLVM compilation framework that frequently applies random sequences of standard compiler optimizations on the currently running program. Results on using selected benchmarks from the SPEC CPU 2006 suite show a significant sustainable increase in obfuscation for a large number of standard optimizations over the run-time course of the programs

OJIT: A Novel Secure Remote Execution Technology By Obfuscated Just-In-Time Compilation

The Third European LLVM Conferenc

Security and Privacy Issues in Autonomous Vehicles: A Layer-Based Survey

Artificial Intelligence (AI) is changing every technology we are used to deal with. Autonomy has long been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Big auto manufacturers as well are investing billions of dollars to produce Autonomous Vehicles (AVs). This new technology has the potential to provide more safety for passengers, less crowded roads, congestion alleviation, optimized traffic, fuel-saving, less pollution as well as enhanced travel experience among other benefits. But this new paradigm shift comes with newly introduced privacy issues and security concerns. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected. They collect huge troves of information, which needs to be protected from breaches. In this work, we investigate security challenges and privacy concerns in AVs. We examine different attacks launched in a layer-based approach. We conceptualize the architecture of AVs in a four-layered model. Then, we survey security and privacy attacks and some of the most promising countermeasures to tackle them. Our goal is to shed light on the open research challenges in the area of AVs as well as offer directions for future research