Reviewerly/exHarmony huggingface dataset:
Probabilistic Dynamic Modeling and Control for Skid-Steered Mobile Robots in Off-Road Environments

Skid-Steered Mobile Robots (SSMRs) are commonly deployed for autonomous navigation across challenging off-road terrains due to their high maneuverability. However, modeling the tire-terrain interactions for these robots when operating at their dynamic limits is challenging, since slipping and skidding govern their movement. During nominal operation, the data collected from the deviation of the robot’s measured states from their commanded values can be informative of these hard-to-model dynamics. In this work-in-progress paper, we propose a probabilistic motion model for SSMRs by leveraging least squares and Sparse Gaussian Process Regression (SGPR) algorithms. This model allows for a nonlinear stochastic Model Predictive Control (MPC) formulation that can be solved in real-time. Initial results on the application of GPR to account for unmodeled dynamics of a physics-simulated quadrotor are shown, suggesting that it can similarly be put to good use for off-road autonomy applications. We explain how these results reinforce the promising application of an SGPR model to risk-averse motion planning for SSMRs.
No Free Wireless Charge: Covert Channels via Wireless Charging on Mobile Devices

Manufacturers and users have embraced the shift from wired to wireless technologies—a shift that promises convenience, reduced cabling, and modernization. Manufacturers cut costs by cutting down on wiring; meanwhile, users experience increased accessibility and mobility on their wireless devices. Naturally, wireless media does not come with wires, but it does come with strings attached. In a wireless world, when device A talks to device B, communication is no longer physically constrained to the two of them. Instead, the communication channel is shared by many devices, opening up avenues for eavesdropping and interception.Wireless charging, which has become de facto functionality on many types of smart devices, is not immune to this phenomenon. Qi, the leading standard in this domain, provides a communication protocol that is unencrypted and insecure. In fact, the Qi standard enables several new means of wireless charging attack. In this paper, we propose three novel attacks on wireless charging stations, named LeakyCharge, SneakyCharge and CheatyCharge. Two are supply chain attacks, while the third would allow an adversary to perform random attacks.
Automating Vulnerability Management in the Software Development Lifecycle

Managing the presence of vulnerabilities in software can be a time and resource consuming process. The advancements in machine learning (ML) over the past few years have allowed us to automate parts of the software development lifecycle, including the identification of vulnerabilities starting from bug reports. However, such approaches have known gaps generally related with subpar effectiveness. In this PhD, we intend to propose a vulnerability management framework aiming at four main objectives: i) highly effective vulnerability identification starting from bug reports; ii) detailed vulnerability classification; iii) prediction of main aspects related with the correction (e.g., defect triage); and iv) recommending corrections based on the detailed knowledge obtained in the previous phases.
Authoritarianism in the discourse of online forums: A study of its articulations in the Swedish context

Abstract The overall aim of this study is to explore the authoritarian dimension in the far-right discourse of online forums. The study argues for a focus on the articulations of authoritarianism to understand the dynamics of far-right discourse. Four central features of authoritarianism are identified and explored: 1) the authoritarian values underlying articulated opinions on diverse issues; 2) the emotional dimension of authoritarianism; 3) the coexistence of civil and uncivil articulations of authoritarianism; and 4) the role of mainstream news as reference for and trigger of authoritarian responses. The qualitative study is based on data from two Swedish forums, Flashback and Familjeliv [Family life], and consists of 79 threads related to three issues on the agenda: disorder in school, gang crime, and transgender. The results show expressions of authoritarian–liberal value conflicts, and, most significantly, the vigour of an authoritarian culture on the forums, with implications for the normalisation of far-right discourse.
Secure Compiler Framework to Design Fault Attack Resistant Software

Fault injection attacks are a potent class of physical attacks where an injected fault during the execution of a cryptosystem is exploited to retrieve the secret key. The success of the fault attack depends on the software implementation and the underlying hardware. While there are several tools to detect vulnerable locations, none of them consider all the influencing aspects that, include the cipher algorithm, its implementation, and the underlying hardware. In this paper, we propose a three-stage secure compiler framework that automatically identifies exploitable instructions, quantifies exploitability, and adds countermeasures. It can enable tradeoffs based on performance requirements and the user’s security requirements. We demonstrate the versatility of the framework by evaluating nine different block cipher implementations.
EXPERT: EXPloiting DRAM ERror Types to Improve the Effective Forecasting Coverage in the Field

DRAM failures, which are mostly caused by DRAM uncorrectable errors (UCEs), are one of the most critical factors for reliable services in computing systems. Prior work demonstrates the potential to utilize machine learning techniques for forecasting DRAM UCEs. However, they do not have the knowledge that different DRAM UCEs can be classified into different types. To this end, we obtain the first field dataset from a large datacenter of Alibaba Cloud, with the labels of different UCE types. Then, we propose EXPERT, a design to exploit such information to improve the effective forecasting coverage of DRAM UCEs. Finally, we evaluate the effectiveness of our approach against two state-of-the-art forecaster designs in the field, and the results show that EXPERT achieves up to 18.43% improvements on the effective coverage in terms of F1-Score.
Using AI to Inject Vulnerabilities in Python Code

Developing software that is free of vulnerabilities is an ongoing challenge for developers, as their impact may not be apparent until they are exploited. Although security tools can help identify vulnerabilities, their effectiveness is questionable. To address this challenge and assist developers in implementing more secure code, we aim to develop AI-based vulnerability injection techniques. In fact, having code with security flaws is fundamental both for the evaluation of security tools and for training. Our research specifically focuses on building AI models that can detect potential locations in the code for vulnerability injection. To train these models, we created a dataset that includes vulnerable and secure (i.e., patched) versions of Python code collected from open-source repositories. Results show that our methodology successfully identifies potential locations where a vulnerability can be injected with high accuracy. We also compared our approach with the deterministic use of Regular Expressions (RegEx) created based on the vulnerability patches. As a result, our AI approach outperformed the RegEx by a large margin. This paper demonstrates the feasibility of using AI models to identify potential vulnerability injection locations in Python code, thereby paving the way for future research on injecting and exploiting these vulnerabilities with AI. Such research can help in evaluating the effectiveness of security tools and training security teams. This work represents the first step towards our roadmap of using AI to inject vulnerabilities in Python code.
Awesome Trusted Execution Environment

While protection of data at-rest and data in-transit can be achieved using standard technologies, the protection of data in-use is still, to a large extent, an open issue. Multiple techniques enable the protection of data processing in untrusted environments, but the one which is gaining the largest consensus is unarguably Trusted Execution Environments. In this tutorial, we focus on a specific TEE technology offering, namely Intel SGX. We discuss key features of Intel SGX – including: secure enclaves, remote attestation, and sealed storage – and present different methods and tools that can be used to make data computation secure, at an acceptable cost in terms of performance penalty.
Stochastic Computing as a Defence Against Adversarial Attacks

Neural networks (NNs) are increasingly often employed in safety critical systems. It is therefore necessary to ensure that these NNs are robust against malicious interference in the form of adversarial attacks, which cause an NN to misclassify inputs. Many proposed defenses against such attacks incorporate randomness in order to make it harder for an attacker to find small input modifications that result in misclassification. Stochastic computing (SC) is a type of approximate computing based on pseudo-random bit-streams that has been successfully used to implement convolutional neural networks (CNNs). Some results have previously suggested that such stochastic CNNs (SCNNs) are partially robust against adversarial attacks. In this work, we will demonstrate that SCNNs do indeed possess inherent protection against some powerful adversarial attacks. Our results show that the white-box C&W attack is up to 16x less successful compared to an equivalent binary NN, and Boundary Attack even fails to generate adversarial inputs in many cases.
How to Resuscitate a Sick VM in the Cloud

A guest virtual machine in a cloud platform may fall “sick” when its kernel encounters a fatal low-level bug or is subverted by an adversary. The VM owner is hence likely to lose her control over it due to a kernel hang or being denied of remote accesses. While the VM can be rebooted with the assistance from the cloud server, the owner not only faces service disruption but also is left with no opportunity to make an in-depth diagnosis and forensics on the spot, not to mention a live rectification. Currently, the cloud service provider has neither incentive nor the technology to assist owners to resuscitate their falling VMs. In this paper, we propose a new cloud service termed VMCare-As-A-Service (VaaS) with the vision that the owner of a sick VM applies her tools running on a special VM to repair it. VaaS demands innovative cloud technologies for the unique infrastructure support as well as new software security techniques for attacks neutralization and runtime rectification upon a running and corrupted kernel. We examine the ensuing research challenges and present several preliminary approaches to kindle the interests from the community.
A Concise Analysis of Pasting Attacks and their Impact on Image Classification

Neural networks are used for a variety of tasks in a wide range of applications, including high security applications, such as face recognition systems. These are used for identification, authentication and authorization. However, neural networks have been shown to be vulnerable against a variety of attacks that apply small perturbations to an input image in order to alter the predictions of a target model. In this paper, we present a simple pasting attack, which inserts objects, such as a face of a target into a source image. Since there is no reliance on gradients, it can be applied to any black-box image classifier. During evaluation, an average of 4.6 queries were sufficient to render an attack on a FaceNet model successful, 1.8 queries for an ImageNet classifier and 7.7 for the unknown black-box classifier used in the MLSec Competetion. By taking solely advantage of simple image operations, such as translation, scaling, rotation and change of transparency, the approach is lightweight and can be implemented in few lines of code. We make our code publicly available at: https://github.com/bunni90/FacePastingAttack.
Translating Natural Language Requirements to Formal Specifications: A Study on GPT and Symbolic NLP

Software verification is essential to ensure dependability and that a system or component fulfils its specified requirements. Natural language is the most common way of specifying requirements, although many verification techniques such as theorem proving depend upon requirements being written in formal specification languages. Automatically translating requirements into a formal specification language is a relevant and challenging research question, because developers often lack the necessary expertise. In our work we consider the application of natural language processing (NLP) to address that research question. This paper considers two distinct approaches to formalise natural language requirements: a symbolic method and a GPT-based method. The two methods are evaluated with respect to their ability to generate accurate Java Modeling Language (JML) from textual requirements, and the results show good promise for automatic formalisation of requirements.
A Workflow for Distributed and Resilient Attack Graph Generation

Among the existing attack models, Attack Graphs (AGs) represent a powerful abstraction to capture the notion of multi-step attacks i.e., ensembles of sequential vulnerability exploits taken by an attacker with a specific objective. A well-known issue in using AGs is their poor scalability due to the complexity of generating and analyzing all existing attack paths. To this aim, we propose a workflow for a more efficient generation of attack paths in a distributed and resilient manner. We describe the general workflow, emphasizing the research challenges and providing few preliminary solutions.
Space Booby Traps: Hacking Back and Assured Cyber Deterrence in Space

Critical infrastructure is becoming increasingly reliant on satellite services, yielding space systems as prime targets for adversaries. Space cyber defenses must be implemented to enable mission resilience. Hacking back (also called active defense) is an effective mechanism to defend digital systems but difficult to implement with attribution and escalation challenges. We propose a hackback architecture that engages a cyber booby trap facilitating autonomous, active defense against adversaries on space vehicles. The cyber booby trap ensures that only the attacker is impacted and imparts a proportionate response to a cyberattack.
Susceptibility of Autonomous Driving Agents to Learning-Based Action-Space Attacks

Intelligent vehicles with increasing complexity face cybersecurity threats. This paper studies action-space attacks on autonomous driving agents that make decisions using either a traditional modular processing pipeline or the recently proposed end-to-end driving model obtained via deep reinforcement learning (DRL). Such attacks alter the actuation signal and pose direct risks to the vehicle’s state. We formulate the attack construction as a DRL problem based on the input from either an extra camera or inertial measurement unit deployed. The attacks are designed to lurk until a safety-critical moment arises and cause a side collision upon activation. We analyze the behavioral differences between two driving agents when subjected to action-space attacks and demonstrate the superior resilience of the modular processing pipeline. We further investigate the performance and limitations of two enhancement methods, i.e., adversarial training through fine-tuning and progressive neural networks. The result offers valuable insights into vehicle safety from the viewpoints of both the assailant and the defender and informs the future design of autonomous driving systems.
Cascading Negative Transfer in Networks of Machine Learning Systems

Wide-spread use of transfer learning establishes inter-linkages between otherwise disparate parts of systems. These inter-linkages create systemic risks of cascading failure. This paper presents a formal framework for cascading negative transfer. A novel definition is provided for the efficiency of transfer learning networks. Catalysts and pre-conditions for cascading negative transfer are identified. Throughout, qualitative examples in unmanned aerial systems are used to clarify the presented theory. The conclusion is drawn that negative transfer can propagate among machine learning systems that are inter-linked by transfer learning, and that solution methods spanning algorithm design and systems engineering can address systemic risks from cascading negative transfer without eliminating transfer learning altogether.
How to Learn Collaboratively - Federated Learning to Peer-to-Peer Learning and What’s at Stake

Standard ML relies on training using a centrally collected dataset, while collaborative learning techniques such as Federated Learning (FL) enable data to remain decentralized at client locations. In FL, a central server coordinates the training process, reducing computation and communication expenses for clients. However, this centralization can lead to server congestion and heightened risk of malicious activity or data privacy breaches. In contrast, Peer-to-Peer Learning (P2PL) is a fully decentralized system where nodes manage both local training and aggregation tasks. While P2PL promotes privacy by eliminating the need to trust a single node, it also results in increased computation and communication costs, along with potential difficulties in achieving consensus among nodes. To address the limitations of both FL and P2PL, we propose a hybrid approach called Hubs-and-Spokes Learning (HSL). In HSL, hubs function similarly to FL servers, maintaining consensus but exerting less control over spokes. This paper argues that HSL’s design allows for greater availability and privacy than FL, while reducing computation and communication costs compared to P2PL. Additionally, HSL maintains consensus and integrity in the learning process.
Advancing Blockchain Security: from Vulnerability Detection to Transaction Revocation

Smart contracts are software built with immature development tools and/or by developers who usually resort to smart contract-specific languages like Solidity, with which they tend to lack adequate expertise. Despite the existence of tools for vulnerability detection, recent works have shown they are ineffective and fail to prevent vulnerable contracts from being deployed. As a result, the blockchain is full of immutable bugs associated with incorrect information that may need to be revoked. This Ph.D. aims at advancing the security of blockchain applications by creating a security assurance framework composed of tools and techniques for building blockchain systems on which we can rely. The objective is two-fold: i) detection of vulnerabilities in smart contracts and ii) handling the generally inevitable presence of undetected residual faults and vulnerabilities in smart contracts. Thus, we aim at accomplishing: i) the creation of a representative and reusable vulnerability model for blockchain systems; ii) the definition of a vulnerability injection approach for blockchain systems; iii) the proposal of a benchmark for smart contract vulnerability detection tools; iv) the proposal of techniques for creating effective smart contract vulnerability detection tools, based on an ensemble of heterogeneous tools; and finally v) the proposal of automatic techniques for efficient and secure blockchain transaction revocation.
Assurance for Autonomy – JPL’s past research, lessons learned, and future directions

Robotic space missions have long depended on automation, defined in the 2015 NASA Technology Roadmaps as “the automatically-controlled operation of an apparatus, process, or system using a pre-planned set of instructions (e.g., a command sequence),” to react to events when a rapid response is required. Autonomy, defined there as “the capacity of a system to achieve goals while operating independently from external control,” is required when a wide variation in circumstances precludes responses being pre-planned, instead autonomy follows an onboard deliberative process to determine the situation, decide the response, and manage its execution. Autonomy is increasingly called for to support adventurous space mission concepts, as an enabling capability or as a significant enhancer of the science value that those missions can return. But if autonomy is to be allowed to control these missions’ expensive assets, all parties in the lifetime of a mission, from proposers through ground control, must have high confidence that autonomy will perform as intended to keep the asset safe to (if possible) accomplish the mission objectives. The role of mission assurance is a key contributor to providing this confidence, yet assurance practices honed over decades of spaceflight have relatively little experience with autonomy. To remedy this situation, researchers in JPL’s software assurance group have been involved in the development of techniques specific to the assurance of autonomy. This paper summarizes over two decades of this research, and offers a vision of where further work is needed to address open issues.
Adversarial Patch Detection and Mitigation by Detecting High Entropy Regions

Neural networks have achieved remarkable performance in many applications, such as image classification and object detection, but security and robustness concerns have also been raised. Even the most advanced object detectors are vulnerable to localised patch attacks, where an adversary introduces a small adversarial patch into an image to either cause the detectors to miss real objects or to cause the detectors to detect objects that do not exist. Adversarial patches are able to force state-of-the-art object detectors to make false predictions with a high degree of confidence. These attacks can be carried out in the physical world, and defending against them is an open problem. In this paper, we propose a novel detection approach for real-world adversarial patches based on edge detection. The approach takes advantage of the fact that patches are high entropy regions featuring many edges and details. We evaluated our approach on a subset of the APRICOT and MS COCO datasets. In total, we achieve over 88% IoU on samples featuring adversarial patches.
Safe Explainable Agents for Autonomous Navigation using Evolving Behavior Trees

Machine learning and reinforcement learning are increasingly used to solve complex tasks in autonomous systems. However, autonomous agents represented by large neural networks are not transparent leading to their assurability and trustworthiness becoming critical challenges. Large models also result in a lack of interpretability which causes severe obstacles related to trust in autonomous agents and human-machine teaming. In this paper, we leverage the hierarchical structure of behavior trees and hierarchical reinforcement learning to develop a neurosymbolic model architecture for autonomous agents. The proposed model, referred to as Evolving Behavior Trees (EBTs), integrates the required components to represent the learning tasks as well as the switching between tasks to achieve complex long-term goals. We design an agent for autonomous navigation and we evaluate the approach against a state-of-the-art hierarchical reinforcement learning method using a Maze Simulation Environment. The results show autonomous agents represented by EBTs can be trained efficiently. The approach incorporates explicit safety constraints into the model and incurs significantly fewer safety violations during training and execution. Further, the model provides explanations for the behavior of the autonomous agent by associating the state of the executing EBT with agent actions.