Adversarial Attack on Radar-based Environment Perception Systems

Due to their robustness to degraded capturing conditions, radars are widely used for environment perception, which is a critical task in applications like autonomous vehicles. More specifically, Ultra-Wide Band (UWB) radars are particularly efficient for short range settings as they carry rich information on the environment. Recent UWB-based systems rely on Machine Learning (ML) to exploit the rich signature of these sensors. However, ML classifiers are susceptible to adversarial examples, which are created from raw data to fool the classifier such that it assigns the input to the wrong class. These attacks represent a serious threat to systems integrity, especially for safety-critical applications. In this work, we present a new adversarial attack on UWB radars in which an adversary injects adversarial radio noise in the wireless channel to cause an obstacle recognition failure. First, based on signals collected in real-life environment, we show that conventional attacks fail to generate robust noise under realistic conditions. We propose a-RNA, i.e., Adversarial Radio Noise Attack to overcome these issues. Specifically, a-RNA generates an adversarial noise that is efficient without synchronization between the input signal and the noise. Moreover, a-RNA generated noise is, by-design, robust against pre-processing countermeasures such as filtering-based defenses. Moreover, in addition to the undetectability objective by limiting the noise magnitude budget, a-RNA is also efficient in the presence of sophisticated defenses in the spectral domain by introducing a frequency budget. We believe this work should alert about potentially critical implementations of adversarial attacks on radar systems that should be taken seriously

Overview of Sensing Attacks on Autonomous Vehicle Technologies and Impact on Traffic Flow

While perception systems in Connected and Autonomous Vehicles (CAVs), which encompass both communication technologies and advanced sensors, promise to significantly reduce human driving errors, they also expose CAVs to various cyberattacks. These include both communication and sensing attacks, which potentially jeopardize not only individual vehicles but also overall traffic safety and efficiency. While much research has focused on communication attacks, sensing attacks, which are equally critical, have garnered less attention. To address this gap, this study offers a comprehensive review of potential sensing attacks and their impact on target vehicles, focusing on commonly deployed sensors in CAVs such as cameras, LiDAR, Radar, ultrasonic sensors, and GPS. Based on this review, we discuss the feasibility of integrating hardware-in-the-loop experiments with microscopic traffic simulations. We also design baseline scenarios to analyze the macro-level impact of sensing attacks on traffic flow. This study aims to bridge the research gap between individual vehicle sensing attacks and broader macroscopic impacts, thereby laying the foundation for future systemic understanding and mitigation

XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars

Compute In-Memory platforms such as memristive crossbars are gaining focus as they facilitate acceleration of Deep Neural Networks (DNNs) with high area and compute-efficiencies. However, the intrinsic non-idealities associated with the analog nature of computing in crossbars limits the performance of the deployed DNNs. Furthermore, DNNs are shown to be vulnerable to adversarial attacks leading to severe security threats in their large-scale deployment. Thus, finding adversarially robust DNN architectures for non-ideal crossbars is critical to the safe and secure deployment of DNNs on the edge. This work proposes a two-phase algorithm-hardware co-optimization approach called XploreNAS that searches for hardware-efficient & adversarially robust neural architectures for non-ideal crossbar platforms. We use the one-shot Neural Architecture Search (NAS) approach to train a large Supernet with crossbar-awareness and sample adversarially robust Subnets therefrom, maintaining competitive hardware-efficiency. Our experiments on crossbars with benchmark datasets (SVHN, CIFAR10 & CIFAR100) show upto ~8-16% improvement in the adversarial robustness of the searched Subnets against a baseline ResNet-18 model subjected to crossbar-aware adversarial training. We benchmark our robust Subnets for Energy-Delay-Area-Products (EDAPs) using the Neurosim tool and find that with additional hardware-efficiency driven optimizations, the Subnets attain ~1.5-1.6x lower EDAPs than ResNet-18 baseline.Comment: 16 pages, 8 figures, 2 table

Generative Adversarial Networks for Multi-Objective Synthetic Data Generation

Synthetic data has become increasingly accessible due to remarkable advancements in machine learning. This data is extremely useful to researchers due to its wide range of applications. Synthetic data may be used to robust populations that are under-sampled, or to create permutations of some existing data, generating combinations not seen in the original data. Synthetic data may also be used in place of the original data completely when sensitive aspects limit the distribution.Previously, research in synthetic data generation has been primarily focused on generating data that is maximally realistic. Significantly less attention has been paid to assurances of other components of the data, such as privacy concerns or data diversity. This has left a gap in the field of synthetic data generation. We address this through the investigation of multi-agent synthetic data generation.In this dissertation, we expand the scope of data generation by introducing agents that optimize various facets of synthetic data, such as privacy, class diversity, and training utility. We propose a novel, multi-objective synthetic generation framework to allow all of these objectives to be optimized. We finally demonstrate this framework can generate high quality data across multiple domains for an arbitrary number of objectives

Neural networks in the pursuit of invincible counterdrone systems

The growing range of possibilities provided by the proliferation of commercial unmanned aerial vehicles, or drones, raises alarming safety and security threats. The efficient mitigation of these threats depends on authorities having defense systems to counter both accidentally trespassing and maliciously operated drones. To effectively counter such vehicles, defense systems must be able to detect a new drone entering a restricted airspace; locate its position; identify its purpose; and, should the identification procedure mark it as a threat, neutralize it.acceptedVersionPeer reviewe

Robust filtering schemes for machine learning systems to defend Adversarial Attack

Robust filtering schemes for machine learning systems to defend Adversarial Attac

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

Expanding Australia\u27s defence capabilities for technological asymmetric advantage in information, cyber and space in the context of accelerating regional military modernisation: A systemic design approach

Introduction. The aim of the project was to conduct a systemic design study to evaluate Australia\u27sopportunities and barriers for achieving a technological advantage in light of regional military technological advancement. It focussed on the three domains of (1) cybersecurity technology, (2) information technology, and (3) space technology. Research process. Employing a systemic design approach, the study first leveraged scientometric analysis, utilising informetric mapping software (VOSviewer) to evaluate emerging trends and their implications on defence capabilities. This approach facilitated a broader understanding of the interdisciplinary nature of defence technologies, identifying key areas for further exploration. The subsequent survey study, engaging 828 professionals across STEM, space, aerospace, defence/ law enforcement, and ICT, aimed to assess the impact, deployment likelihood, and developmental timelines of the identified technologies. Finally, five experts were interviewed to help elaborate on the findings in the survey and translate them into implications for the ADF. Findings. Key findings revealed significant overlaps in technology clusters, highlighting ten specific technologies or trends as potential force multipliers for the ADF. Among these, cybersecurity of critical infrastructure and optimisation and other algorithmic technologies were recognised for their immediate potential and urgency, suggesting a prioritisation for development investment. The analysis presented a clear imperative for urgent and prioritised technological investments, specifically in cybersecurity and information technologies, followed by space technologies. The research also suggested partnerships that Australia should develop to keep ahead in terms of regional military modernisation. Implications. To maintain a competitive edge, there is an urgent need for investment in the development and application of these technologies, as nearly all disruptive technologies identified for their potential impact, deployment/utilization likelihood, extensive use, and novelty for defence purposes are needed in the near-term (less than 5 years – cybersecurity and information technologies) or medium-term (less than 10 years – space technologies). In line with this, technology investments should be prioritized as follows: Priority 1 includes Cyber Security of critical infrastructure and optimization algorithms; Priority 2 encompasses Unmanned and autonomous systems and weapons, Deep/Machine Learning, and Space-based command and communications systems; and Priority 3 involves Industry 4.0 technologies, Quantum technology, Electromagnetic and navigation warfare systems, Hypersonic weapons, and Directed energy weapons. At the policy level, underfunding, bureaucratic inertia and outdated procurement models needed to be addressed to enhance agility of innovation. More critically, Australia needed to come up with creative ways to recruit, train and retain human capital to develop, manage and use these sophisticated technologies. Finally, in order to maintain a lead over competitors (China, Russia, Iran, North Korea) in the regional military technology competition, the survey and interviews indicate that Australia should continue its military technology alliances with long-standing partners (US, Europe, Israel), broaden its collaborations with more recent partners (Japan, Singapore, South Korea), and establish partnerships with new ones (India, Malaysia, Vietnam, Pacific Island nations). Conclusion. This study sheds light on the future direction for the ADF and Defence in general, underscoring the importance of strategic investments in up-and-coming technologies. By pinpointing strategic voids, potential partnerships, and sovereign technologies with high potential, this report acts as a roadmap for bolstering Australia’s defence capabilities and safeguarding its strategic interests amidst regional technological changes