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Damith C. Ranasinghe

Publications and source records attributed to Damith C. Ranasinghe.

At least 37 records · Page 2Linked to original sources
arXiv2023

Make out like a (Multi-Armed) Bandit: Improving the Odds of Fuzzer Seed Scheduling with T-Scheduler

Fuzzing is a highly-scalable software testing technique that uncovers bugs in a target program by executing it with mutated inputs. Over the life of a fuzzing campaign, the fuzzer accumulates inputs inducing new and interesting target behaviors, drawing from these inputs for further mutation. This rapidly results in a large number of inputs to select from, making it challenging to quickly and accurately select the "most promising" input for mutation. Reinforcement learning (RL) provides a natural solution to this "seed scheduling" problem: the fuzzer dynamically adapts its selection strategy by learning from past results. However, existing RL approaches are (a) computationally expensive (reducing fuzzer throughput) and/or (b) require hyperparameter tuning (reducing generality across targets and input types). To this end, we propose T-Scheduler, a seed scheduler built on multi-armed bandit theory that automatically adapts to the target without any hyperparameter tuning. We evaluate T-Scheduler over 35 CPU-yr of fuzzing, comparing it to 11 state-of-the-art schedulers. Our results show that T-Scheduler improves on these 11 schedulers on both bug-finding and coverage-expansion abilities.

cs.CR↗
arXiv2023

Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense

We present a new algorithm to learn a deep neural network model robust against adversarial attacks. Previous algorithms demonstrate an adversarially trained Bayesian Neural Network (BNN) provides improved robustness. We recognize the adversarial learning approach for approximating the multi-modal posterior distribution of a Bayesian model can lead to mode collapse; consequently, the model's achievements in robustness and performance are sub-optimal. Instead, we first propose preventing mode collapse to better approximate the multi-modal posterior distribution. Second, based on the intuition that a robust model should ignore perturbations and only consider the informative content of the input, we conceptualize and formulate an information gain objective to measure and force the information learned from both benign and adversarial training instances to be similar. Importantly. we prove and demonstrate that minimizing the information gain objective allows the adversarial risk to approach the conventional empirical risk. We believe our efforts provide a step toward a basis for a principled method of adversarially training BNNs. Our model demonstrate significantly improved robustness--up to 20%--compared with adversarial training and Adv-BNN under PGD attacks with 0.035 distortion on both CIFAR-10 and STL-10 datasets.

cs.LG↗
arXiv2023

ConservationBots: Autonomous Aerial Robot for Fast Robust Wildlife Tracking in Complex Terrains

Today, the most widespread, widely applicable technology for gathering data relies on experienced scientists armed with handheld radio telemetry equipment to locate low-power radio transmitters attached to wildlife from the ground. Although aerial robots can transform labor-intensive conservation tasks, the realization of autonomous systems for tackling task complexities under real-world conditions remains a challenge. We developed ConservationBots-small aerial robots for tracking multiple, dynamic, radio-tagged wildlife. The aerial robot achieves robust localization performance and fast task completion times -- significant for energy-limited aerial systems while avoiding close encounters with potential, counter-productive disturbances to wildlife. Our approach overcomes the technical and practical problems posed by combining a lightweight sensor with new concepts: i) planning to determine both trajectory and measurement actions guided by an information-theoretic objective, which allows the robot to strategically select near-instantaneous range-only measurements to achieve faster localization, and time-consuming sensor rotation actions to acquire bearing measurements and achieve robust tracking performance; ii) a bearing detector more robust to noise and iii) a tracking algorithm formulation robust to missed and false detections experienced in real-world conditions. We conducted extensive studies: simulations built upon complex signal propagation over high-resolution elevation data on diverse geographical terrains; field testing; studies with wombats (Lasiorhinus latifrons; nocturnal, vulnerable species dwelling in underground warrens) and tracking comparisons with a highly experienced biologist to validate the effectiveness of our aerial robot and demonstrate the significant advantages over the manual method.

cs.RO↗
arXiv2023

SplITS: Split Input-to-State Mapping for Effective Firmware Fuzzing

Ability to test firmware on embedded devices is critical to discovering vulnerabilities prior to their adversarial exploitation. State-of-the-art automated testing methods rehost firmware in emulators and attempt to facilitate inputs from a diversity of methods (interrupt driven, status polling) and a plethora of devices (such as modems and GPS units). Despite recent progress to tackle peripheral input generation challenges in rehosting, a firmware's expectation of multi-byte magic values supplied from peripheral inputs for string operations still pose a significant roadblock. We solve the impediment posed by multi-byte magic strings in monolithic firmware. We propose feedback mechanisms for input-to-state mapping and retaining seeds for targeted replacement mutations with an efficient method to solve multi-byte comparisons. The feedback allows an efficient search over a combinatorial solution-space. We evaluate our prototype implementation, SplITS, with a diverse set of 21 real-world monolithic firmware binaries used in prior works, and 3 new binaries from popular open source projects. SplITS automatically solves 497% more multi-byte magic strings guarding further execution to uncover new code and bugs compared to state-of-the-art. In 11 of the 12 real-world firmware binaries with string comparisons, including those extensively analyzed by prior works, SplITS outperformed, statistically significantly. We observed up to 161% increase in blocks covered and discovered 6 new bugs that remained guarded by string comparisons. Significantly, deep and difficult to reproduce bugs guarded by comparisons, identified in prior work, were found consistently. To facilitate future research in the field, we release SplITS, the new firmware data sets, and bug analysis at https://github.com/SplITS-Fuzzer

cs.CR↗
arXiv2023

Icicle: A Re-Designed Emulator for Grey-Box Firmware Fuzzing

Emulation-based fuzzers enable testing binaries without source code, and facilitate testing embedded applications where automated execution on the target hardware architecture is difficult and slow. The instrumentation techniques added to extract feedback and guide input mutations towards generating effective test cases is at the core of modern fuzzers. But, modern emulation-based fuzzers have evolved by re-purposing general-purpose emulators; consequently, developing and integrating fuzzing techniques, such as instrumentation methods, are difficult and often added in an ad-hoc manner, specific to an instruction set architecture (ISA). This limits state-of-the-art fuzzing techniques to few ISAs such as x86/x86-64 or ARM/AArch64; a significant problem for firmware fuzzing of diverse ISAs. This study presents our efforts to re-think emulation for fuzzing. We design and implement a fuzzing-specific, multi-architecture emulation framework -- Icicle. We demonstrate the capability to add instrumentation once, in an architecture agnostic manner, with low execution overhead. We employ Icicle as the emulator for a state-of-the-art ARM firmware fuzzer -- Fuzzware -- and replicate results. Significantly, we demonstrate the availability of new instrumentation in Icicle enabled the discovery of new bugs. We demonstrate the fidelity of Icicle and efficacy of architecture agnostic instrumentation by discovering LAVA-M benchmark bugs, requiring a known and specific operational capability of instrumentation techniques, across a diverse set of instruction set architectures (x86-64, ARM/AArch64, RISC-V, MIPS). Further, to demonstrate the effectiveness of Icicle to discover bugs in a currently unsupported architecture in emulation-based fuzzers, we perform a fuzzing campaign with real-world MSP430 firmware binaries and discovered 7 new bugs.

cs.CR↗
arXiv2023

Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models

Despite our best efforts, deep learning models remain highly vulnerable to even tiny adversarial perturbations applied to the inputs. The ability to extract information from solely the output of a machine learning model to craft adversarial perturbations to black-box models is a practical threat against real-world systems, such as autonomous cars or machine learning models exposed as a service (MLaaS). Of particular interest are sparse attacks. The realization of sparse attacks in black-box models demonstrates that machine learning models are more vulnerable than we believe. Because these attacks aim to minimize the number of perturbed pixels measured by l_0 norm-required to mislead a model by solely observing the decision (the predicted label) returned to a model query; the so-called decision-based attack setting. But, such an attack leads to an NP-hard optimization problem. We develop an evolution-based algorithm-SparseEvo-for the problem and evaluate against both convolutional deep neural networks and vision transformers. Notably, vision transformers are yet to be investigated under a decision-based attack setting. SparseEvo requires significantly fewer model queries than the state-of-the-art sparse attack Pointwise for both untargeted and targeted attacks. The attack algorithm, although conceptually simple, is also competitive with only a limited query budget against the state-of-the-art gradient-based whitebox attacks in standard computer vision tasks such as ImageNet. Importantly, the query efficient SparseEvo, along with decision-based attacks, in general, raise new questions regarding the safety of deployed systems and poses new directions to study and understand the robustness of machine learning models.

cs.LG↗
arXiv2023

RamBoAttack: A Robust Query Efficient Deep Neural Network Decision Exploit

Machine learning models are critically susceptible to evasion attacks from adversarial examples. Generally, adversarial examples, modified inputs deceptively similar to the original input, are constructed under whitebox settings by adversaries with full access to the model. However, recent attacks have shown a remarkable reduction in query numbers to craft adversarial examples using blackbox attacks. Particularly, alarming is the ability to exploit the classification decision from the access interface of a trained model provided by a growing number of Machine Learning as a Service providers including Google, Microsoft, IBM and used by a plethora of applications incorporating these models. The ability of an adversary to exploit only the predicted label from a model to craft adversarial examples is distinguished as a decision-based attack. In our study, we first deep dive into recent state-of-the-art decision-based attacks in ICLR and SP to highlight the costly nature of discovering low distortion adversarial employing gradient estimation methods. We develop a robust query efficient attack capable of avoiding entrapment in a local minimum and misdirection from noisy gradients seen in gradient estimation methods. The attack method we propose, RamBoAttack, exploits the notion of Randomized Block Coordinate Descent to explore the hidden classifier manifold, targeting perturbations to manipulate only localized input features to address the issues of gradient estimation methods. Importantly, the RamBoAttack is more robust to the different sample inputs available to an adversary and the targeted class. Overall, for a given target class, RamBoAttack is demonstrated to be more robust at achieving a lower distortion within a given query budget. We curate our extensive results using the large-scale high-resolution ImageNet dataset and open-source our attack, test samples and artifacts on GitHub.

cs.LG↗
arXiv2023

Ember-IO: Effective Firmware Fuzzing with Model-Free Memory Mapped IO

Exponential growth in embedded systems is driving the research imperative to develop fuzzers to automate firmware testing to uncover software bugs and security vulnerabilities. But, employing fuzzing techniques in this context present a uniquely challenging proposition; a key problem is the need to deal with the diverse and large number of peripheral communications in an automated testing framework. Recent fuzzing approaches: i) employ re-hosting methods by executing code in an emulator because fuzzing on resource limited embedded systems is slow and unscalable; and ii) integrate models of hardware behaviour to overcome the challenges faced by the massive input-space to be explored created by peripheral devices and to generate inputs that are effective in aiding a fuzzer to make progress. Our efforts expounds upon program execution behaviours unique to firmware to address the resulting input-space search problem. The techniques we propose improve the fuzzer's ability to generate values likely to progress execution and avoids time consumed on mutating inputs that are functionally equivalent to other test cases. We demonstrate the methods are highly efficient and effective at overcoming the input-space search problem. Our emulation-based implementation, Ember-IO, when compared to the existing state-of-the-art fuzzing framework across 21 firmware binaries, demonstrates up to 255% improvement in blocks covered. Further Ember-IO discovered 6 new bugs in the real-world firmware, previously not identified by state-of-the-art fuzzing frameworks. Importantly, Ember-IO integrated with the state-of-the-art fuzzer, Fuzzware, demonstrates similar or improved coverage across all firmware binaries whilst reproducing 3 of the 6 new bugs discovered by Ember-IO.

cs.CR↗
arXiv2022

NoisFre: Noise-Tolerant Memory Fingerprints from Commodity Devices for Security Functions

Building hardware security primitives with on-device memory fingerprints is a compelling proposition given the ubiquity of memory in electronic devices, especially for low-end Internet of Things devices for which cryptographic modules are often unavailable. However, the use of fingerprints in security functions is challenged by the small, but unpredictable variations in fingerprint reproductions from the same device due to measurement noise. Our study formulates a novel and pragmatic approach to achieve highly reliable fingerprints from device memories. We investigate the transformation of raw fingerprints into a noise-tolerant space where the generation of fingerprints is intrinsically highly reliable. We derive formal performance bounds to support practitioners to easily adopt our methods for applications. Subsequently, we demonstrate the expressive power of our formalization by using it to investigate the practicability of extracting noise-tolerant fingerprints from commodity devices. Together with extensive simulations, we have employed 119 chips from five different manufacturers for extensive experimental validations. Our results, including an end-to-end implementation demonstration with a low-cost wearable Bluetooth inertial sensor capable of on-demand and runtime key generation, show that key generators with failure rates less than $10^-6$ can be efficiently obtained with noise-tolerant fingerprints with a single fingerprint snapshot to support ease-of-enrollment.

cs.CR↗
arXiv2022

SecuCode: Intrinsic PUF Entangled Secure Wireless Code Dissemination for Computational RFID Devices

The simplicity of deployment and perpetual operation of energy harvesting devices provides a compelling proposition for a new class of edge devices for the Internet of Things. In particular, Computational Radio Frequency Identification (CRFID) devices are an emerging class of battery-free, computational, sensing enhanced devices that harvest all of their energy for operation. Despite wireless connectivity and powering, secure wireless firmware updates remains an open challenge for CRFID devices due to: intermittent powering, limited computational capabilities, and the absence of a supervisory operating system. We present, for the first time, a secure wireless code dissemination (SecuCode) mechanism for CRFIDs by entangling a device intrinsic hardware security primitive Static Random Access Memory Physical Unclonable Function (SRAM PUF) to a firmware update protocol. The design of SecuCode: i) overcomes the resource-constrained and intermittently powered nature of the CRFID devices; ii) is fully compatible with existing communication protocols employed by CRFID devices in particular, ISO-18000-6C protocol; and ii) is built upon a standard and industry compliant firmware compilation and update method realized by extending a recent framework for firmware updates provided by Texas Instruments. We build an end-to-end SecuCode implementation and conduct extensive experiments to demonstrate standards compliance, evaluate performance and security.

cs.CR↗
arXiv2022

TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems

Deep neural networks are vulnerable to attacks from adversarial inputs and, more recently, Trojans to misguide or hijack the model's decision. We expose the existence of an intriguing class of spatially bounded, physically realizable, adversarial examples -- Universal NaTuralistic adversarial paTches -- we call TnTs, by exploring the superset of the spatially bounded adversarial example space and the natural input space within generative adversarial networks. Now, an adversary can arm themselves with a patch that is naturalistic, less malicious-looking, physically realizable, highly effective achieving high attack success rates, and universal. A TnT is universal because any input image captured with a TnT in the scene will: i) misguide a network (untargeted attack); or ii) force the network to make a malicious decision (targeted attack). Interestingly, now, an adversarial patch attacker has the potential to exert a greater level of control -- the ability to choose a location-independent, natural-looking patch as a trigger in contrast to being constrained to noisy perturbations -- an ability is thus far shown to be only possible with Trojan attack methods needing to interfere with the model building processes to embed a backdoor at the risk discovery; but, still realize a patch deployable in the physical world. Through extensive experiments on the large-scale visual classification task, ImageNet with evaluations across its entire validation set of 50,000 images, we demonstrate the realistic threat from TnTs and the robustness of the attack. We show a generalization of the attack to create patches achieving higher attack success rates than existing state-of-the-art methods. Our results show the generalizability of the attack to different visual classification tasks (CIFAR-10, GTSRB, PubFig) and multiple state-of-the-art deep neural networks such as WideResnet50, Inception-V3 and VGG-16.

cs.CV↗
arXiv2022

Transferable Graph Backdoor Attack

Graph Neural Networks (GNNs) have achieved tremendous success in many graph mining tasks benefitting from the message passing strategy that fuses the local structure and node features for better graph representation learning. Despite the success of GNNs, and similar to other types of deep neural networks, GNNs are found to be vulnerable to unnoticeable perturbations on both graph structure and node features. Many adversarial attacks have been proposed to disclose the fragility of GNNs under different perturbation strategies to create adversarial examples. However, vulnerability of GNNs to successful backdoor attacks was only shown recently. In this paper, we disclose the TRAP attack, a Transferable GRAPh backdoor attack. The core attack principle is to poison the training dataset with perturbation-based triggers that can lead to an effective and transferable backdoor attack. The perturbation trigger for a graph is generated by performing the perturbation actions on the graph structure via a gradient based score matrix from a surrogate model. Compared with prior works, TRAP attack is different in several ways: i) it exploits a surrogate Graph Convolutional Network (GCN) model to generate perturbation triggers for a blackbox based backdoor attack; ii) it generates sample-specific perturbation triggers which do not have a fixed pattern; and iii) the attack transfers, for the first time in the context of GNNs, to different GNN models when trained with the forged poisoned training dataset. Through extensive evaluations on four real-world datasets, we demonstrate the effectiveness of the TRAP attack to build transferable backdoors in four different popular GNNs using four real-world datasets.

cs.CR↗
arXiv2022

Wisecr: Secure Simultaneous Code Disseminationto Many Batteryless Computational RFID Devices

Emerging ultra-low-power tiny scale computing devices in Cyber-Physical Systems %and Internet of Things (IoT) run on harvested energy, are intermittently powered, have limited computational capability, and perform sensing and actuation functions under the control of a dedicated firmware operating without the supervisory control of an operating system. Wirelessly updating or patching the firmware of such devices is inevitable. We consider the challenging problem of simultaneous and secure firmware updates or patching for a typical class of such devices -- Computational Radio Frequency Identification (CRFID) devices. We propose Wisecr, the first secure and simultaneous wireless code dissemination mechanism to multiple devices that prevent malicious code injection attacks and intellectual property (IP) theft, whilst enabling remote attestation of code installation. Importantly, Wisecr is engineered to comply with existing ISO compliant communication protocol standards employed by CRFID devices and systems. We comprehensively evaluate Wisecr's overhead, demonstrate its implementation over standards-compliant protocols, analyze its security and implement an end-to-end realization with popular CRFID devices -- the open-source code is released on GitHub.

cs.CR↗
arXiv2022

Leaving Your Things Unattended is No Joke! Memory Bus Snooping and Open Debug Interface Exploits

Internet of Things devices are widely adopted by the general population. People today are more connected than ever before. The widespread use and low-cost driven construction of these devices in a competitive marketplace render Internet-connected devices an easier and attractive target for malicious actors. This paper demonstrates non-invasive physical attacks against IoT devices in two case studies in a tutorial style format. The study focuses on demonstrating the: i)exploitation of debug interfaces, often left open after manufacture; and ii)the exploitation of exposed memory buses. We illustrate a person could commit such attacks with entry-level knowledge, inexpensive equipment, and limited time (in 8 to 25 minutes).

cs.CR↗
arXiv2021

Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables

Human activity recognition (HAR) is an important research field in ubiquitous computing where the acquisition of large-scale labeled sensor data is tedious, labor-intensive and time consuming. State-of-the-art unsupervised remedies investigated to alleviate the burdens of data annotations in HAR mainly explore training autoencoder frameworks. In this paper: we explore generative adversarial network (GAN) paradigms to learn unsupervised feature representations from wearable sensor data; and design a new GAN framework-Geometrically-Guided GAN or Guided-GAN-for the task. To demonstrate the effectiveness of our formulation, we evaluate the features learned by Guided-GAN in an unsupervised manner on three downstream classification benchmarks. Our results demonstrate Guided-GAN to outperform existing unsupervised approaches whilst closely approaching the performance with fully supervised learned representations. The proposed approach paves the way to bridge the gap between unsupervised and supervised human activity recognition whilst helping to reduce the cost of human data annotation tasks.

cs.LG↗
arXiv2021

The Megopolis Resampler: Memory Coalesced Resampling on GPUs

The resampling process employed in widely used methods such as Importance Sampling (IS), with its adaptive extension (AIS), are used to solve challenging problems requiring approximate inference; for example, non-linear, non-Gaussian state estimation problems. However, the re-sampling process can be computationally prohibitive for practical problems with real-time requirements. We consider the problem of developing highly parallelisable resampling algorithms for massively parallel hardware architectures of modern graphics processing units (GPUs) to accomplish real-time performance. We develop a new variant of the Metropolis algorithm -- Megopolis -- that improves performance without requiring a tuning parameter or reducing resampling quality. The Megopolis algorithm is built upon exploiting the memory access patterns of modern GPU units to reduce the number of memory transactions without the need for tuning parameters. Extensive numerical experiments on GPU hardware demonstrate that the proposed Megopolis algorithm is numerically stable and outperforms the original Metropolis algorithm and its variants -- Metropolis-C1 and Metropolis-C2 -- in speed and quality metrics. Further, given the absence of open tools in this domain and facilitating fair comparisons in the future and supporting the signal processing community, we also open source the complete project, including a repository of source code with Megopolis and all other comparison methods.

cs.DC↗
arXiv2021

Distributed Multi-object Tracking under Limited Field of View Sensors

We consider the challenging problem of tracking multiple objects using a distributed network of sensors. In the practical setting of nodes with limited field of views (FoVs), computing power and communication resources, we develop a novel distributed multi-object tracking algorithm. To accomplish this, we first formalise the concept of label consistency, determine a sufficient condition to achieve it and develop a novel \textit{label consensus approach} that reduces label inconsistency caused by objects' movements from one node's limited FoV to another. Second, we develop a distributed multi-object fusion algorithm that fuses local multi-object state estimates instead of local multi-object densities. This algorithm: i) requires significantly less processing time than multi-object density fusion methods; ii) achieves better tracking accuracy by considering Optimal Sub-Pattern Assignment (OSPA) tracking errors over several scans rather than a single scan; iii) is agnostic to local multi-object tracking techniques, and only requires each node to provide a set of estimated tracks. Thus, it is not necessary to assume that the nodes maintain multi-object densities, and hence the fusion outcomes do not modify local multi-object densities. Numerical experiments demonstrate our proposed solution's real-time computational efficiency and accuracy compared to state-of-the-art solutions in challenging scenarios. We also release source code at https://github.com/AdelaideAuto-IDLab/Distributed-limitedFoV-MOT for our fusion method to foster developments in DMOT algorithms.

cs.MA↗
arXiv2021

ReaDmE: Read-Rate Based Dynamic Execution Scheduling for Intermittent RF-Powered Devices

This paper presents a method for remotely and dynamically determining the execution schedule of long-running tasks on intermittently powered devices such as computational RFID. Our objective is to prevent brown-out events caused by sudden power-loss due to the intermittent nature of the powering channel. We formulate, validate and demonstrate that the read-rate measured from an RFID reader (number of successful interrogations per second) can provide an adequate means of estimating the powering channel condition for passively powered CRFID devices. This method is attractive because it can be implemented without imposing an added burden on the device or requiring additional hardware. We further propose ReaDmE, a dynamic execution scheduling scheme to mitigate brownout events to support long-run execution of complex tasks, such as cryptographic algorithms, on CRFID. Experimental results demonstrate that the ReaDmE method can improve CRFID's long-run execution success rate by 20% at the critical operational range or reduce time overhead by up to 23% compared to previous execution scheduling methods.

cs.PF↗