The 3D printing industry is rapidly growing and increasingly adopted across various sectors, including manufacturing, healthcare, and defense. However, the operational setup often involves hazardous environments, necessitating remote monitoring through cameras and other sensors, which opens the door to cyber-based attacks. In this paper, we show that an adversary with access to video recordings of the 3D printing process can reverse-engineer the underlying 3D print instructions. Our model tracks the printer nozzle's movements during the printing process and maps the corresponding trajectory into G-code instructions. Further, it identifies the correct parameters, such as feed rate and extrusion rate, leading us to be able to successfully perform IP theft. To validate the success of IP theft, we design an equivalence checker that quantitatively compares two sets of 3D print instructions, evaluating their similarity in producing objects that are alike in shape, external appearance, and internal structure. Our equivalence checker, unlike other simple distance-based metrics such as normalized mean square error, is rotational as well as translational invariant. This is necessary to capture shifts in the base/start position of the reverse-engineered instructions relative to the actual 3D print instructions that can happen due to different camera positions. Our model achieves an average accuracy of 90.87% and generates 30.20% fewer instructions compared to the current state-of-the-art methods that produce instructions that either lead to faulty or incorrect (in terms of difference in shape and internal structure) 3D prints. Additionally, we use our model to reverse-engineer the 3D print instructions from a video recording and print a fully-functional counterfeit object.

Maritime systems, including ships and ports, are critical components of global infrastructure, essential for transporting over 80% of the world's goods and supporting internet connectivity. However, these systems face growing cybersecurity threats, as shown by recent attacks disrupting Maersk, one of the world's largest shipping companies, causing widespread impacts on international trade. The unique challenges of the maritime environment--such as diverse operational conditions, extensive physical access points, fragmented regulatory frameworks, and its deeply interconnected structure--require maritime-specific cybersecurity research. Despite the sector's importance, maritime cybersecurity remains underexplored, leaving significant gaps in understanding its challenges and risks. To address these gaps, we investigate how maritime system operators perceive and navigate cybersecurity challenges within this complex landscape. We conducted a user study comprising surveys and semi-structured interviews with 21 officer-level mariners. Participants reported direct experiences with shipboard cyber-attacks, including GPS spoofing and logistics-disrupting ransomware, demonstrating the real-world impact of these threats. Our findings reveal systemic and human-centric issues, such as training poorly aligned with maritime needs, insufficient detection and response tools, and serious gaps in mariners' cybersecurity understanding. Our contributions include a categorization of threats identified by mariners and recommendations for improving maritime security, including better training, response protocols, and regulation. These insights aim to guide future research and policy to strengthen the resilience of maritime systems.

In various industries, cybersecurity regulations have been enacted in an effort to drive improvements to organizational security postures. Despite the prominent influence of these regulations, there has been limited prior investigation of how organizations engage with these regulations and the challenges that they face. Assessing these factors is vital for understanding the impact of cybersecurity regulations in practice and how to enhance them moving forward.

Smart contracts are fundamental pillars of the blockchain, playing a crucial role in facilitating various business transactions. However, these smart contracts are vulnerable to exploitable bugs that can lead to substantial monetary losses. A recent study reveals that over 80% of these exploitable bugs, which are primarily functional bugs, can evade the detection of current tools. Automatically identifying functional bugs in smart contracts presents challenges from multiple perspectives. The primary issue is the significant gap between understanding the high-level logic of the business model and checking the low-level implementations in smart contracts. Furthermore, identifying deeply rooted functional bugs in smart contracts requires the automated generation of effective detection oracles based on various bug features.To address these challenges, we design and implement PromFuzz, an automated and scalable system to detect functional bugs in smart contracts. In PromFuzz, we first propose a novel Large Language Model (LLM)-driven analysis framework, which leverages a dual-agent prompt engineering strategy to pinpoint potentially vulnerable functions for further scrutiny. We then implement a dual-stage coupling approach, which focuses on generating invariant checkers that leverage logic information extracted from potentially vulnerable functions. Finally, we design a bug-oriented fuzzing engine, which maps the logical information from the high-level business model to the low-level smart contract implementations, and performs the bug-oriented fuzzing on targeted functions. We evaluate PromFuzz from 4 perspectives on 5 ground-truth datasets and compare it with multiple state-of-the-art methods. The results show that PromFuzz achieves 86.96% recall and 93.02% F1-score in detecting functional bugs, marking at least a 50% improvement in both metrics over state-of-the-art methods. Moreover, we perform an in-depth analysis on 10 real-world DeFi projects and detect 30 zero-day bugs. Our further case studies, the risky first deposit bug and the AMM price oracle manipulation bug on real-world DeFi projects, demonstrate the serious risks of the exploitable functional bugs in smart contracts. Up to now, 24 zero-day bugs have been assigned CVE IDs. Our discoveries have safeguarded assets totaling $18.2 billion from potential monetary losses.

The Controller Area Network (CAN) is a pivotal communication protocol extensively utilized in vehicles, aircraft, factories, and diverse cyber-physical systems (CPSs). The extensive CAN security literature resulting from decades of wide usage may create an impression of thorough scrutiny. However, a closer look reveals its reliance on a specific threat model with a limited range of abilities. Notably, recent works show that this model is outdated and that a more potent and versatile model could soon become the norm, prompting the need for a new defense paradigm. Unfortunately, the security impact of this emerging model on CAN systems has not received sufficient attention, and the defense systems addressing it are almost nonexistent. In this paper, we introduce ERACAN, the first comprehensive defense system against this new threat model. We first begin with a threat analysis to ensure that ERACAN comprehensively understands this model's capabilities, evasion tactics, and propensity to enable new attacks or enhance existing ones. ERACAN offers versatile protection against this spectrum of threats, providing attack detection, classification, and optional prevention abilities. We implement and evaluate ERACAN on a testbed and a real vehicle's CAN bus to demonstrate its low latency, real-time operation, and protective capabilities. ERACAN achieves detection rates of 100% and 99.7%+ for all attacks launched by the conventional and the enhanced threat models, respectively.

Detection of cyber-attacks in power systems is crucial for rapid corrective actions like isolation, disinfection and asset restoration. For real-time deployment, detection methods must not only be accurate and computationally efficient, but also interpretable for further action. While physics models can reliably detect cyber-attacks, diagnosing where and how assets were attacked is computationally demanding. To supplement detection models, we propose Physics-Assisted Statistics for Anomaly Localization (PASAL), a domain-informed data-driven method that directly identifies anomalous devices. PASAL leverages domain knowledge of the grid topology and incorporates correlation and variance statistics to model inter-sensor causal relationships. Consequently, PASAL offers inherent interpretability and computational efficiency. Our study demonstrates that PASAL swiftly localizes data integrity attacks with minimal false positives and has the potential to identify the type of attack.

Modern power systems are the backbone of our society, supplying electric energy for daily activities. With the integration of communication networks and high penetration of renewable energy sources (RESs), modern power systems have evolved into a cross-domain multilayer complex system of systems with improved efficiency, controllability, and sustainability. However, increasing numbers of unexpected events, including natural disasters, extreme weather, and cyberattacks, are compromising the functionality of modern power systems and causing tremendous societal and economic losses. Resilience, a desirable property, is needed in modern power systems to ensure their capability to withstand all kinds of hazards while maintaining their functions. This article presents a systematic review of recent power system resilience enhancement techniques and proposes new directions for enhancing modern power systems’ resilience considering their cross-domain multilayer features. We first answer the question, “what is power system resilience?” from the perspectives of its definition, constituents, and categorization. It is important to recognize that power system resilience depends on two interdependent factors: network design and system operation. Following that, we present a review of articles published since 2016 that have developed innovative methodologies to improve power system resilience and categorize them into infrastructural resilience enhancement and operational resilience enhancement. We discuss their problem formulations and proposed quantifiable resilience measures, as well as point out their merits and limitations. Finally, we argue that it is paramount to leverage higher order subgraph studies and scientific machine learning (SciML) for modern power systems to capture the interdependence and interactions across heterogeneous networks and data for holistically enhancing their infrastructural and operational resilience.

Model compression and model defense for deep neural networks (DNNs) have been extensively and individually studied. Considering the co-importance of model compactness and robustness in practical applications, several prior works have explored to improve the adversarial robustness of the sparse neural networks. However, the structured sparse models obtained by the existing works suffer severe performance degradation for both benign and robust accuracy, thereby causing a challenging dilemma between robustness and structuredness of compact DNNs. To address this problem, in this paper, we propose CSTAR, an efficient solution that simultaneously impose Compactness, high STructuredness and high Adversarial Robustness on the target DNN models. By formulating the structuredness and robustness requirement within the same framework, the compressed DNNs can simultaneously achieve high compression performance and strong adversarial robustness. Evaluations for various DNN models on different datasets demonstrate the effectiveness of CSTAR. Compared with the state-of-the-art robust structured pruning, CSTAR shows consistently better performance. For instance, when compressing ResNet-18 on CIFAR-10, CSTAR achieves up to 20.07% and 11.91% improvement for benign accuracy and robust accuracy, respectively. For compressing ResNet-18 with 16x compression ratio on Imagenet, CSTAR obtains 8.58% benign accuracy gain and 4.27% robust accuracy gain compared to the existing robust structured pruning.

The rapid growth of robotic aerial vehicles (RAVs) has attracted extensive interest in numerous public and civilian applications, from flying drones to quadrotors. Security of RAV systems is posting greater challenges as RAV controller software becomes more complex and exposes a growing attack surface. Memory isolation techniques, which virtually separate the memory space and conduct hardware-based memory access control, are believed to prevent the attacker from compromising the entire system by exploiting one memory vulnerability. In this paper, we propose Ares, a new variable-level vulnerability assessment framework to explore deeper bugs from a combined cyber-physical perspective. We present a data-driven method to illustrate that, despite state-of-the-art memory isolation efforts, RAV systems are still vulnerable to physics-aware data manipulation attacks. We augment RAV control states with intermediate state variables by tracing accessible control parameters and vehicle dynamics within the same isolated memory region. With this expanded state variable space, we apply multivariate statistical analysis to investigate inter-variable quantitative data dependencies and search for vulnerable state variables. Ares utilizes a reinforcement learning-based method to show how an attacker can exploit memory bugs and parameter defects in a legitimate memory view and elaborately craft adversarial variable values to disrupt a RAV's safe operations. We demonstrate the feasibility and capability of Ares on the widely-used ArduPilot RAV framework. Our extensive empirical evaluation shows that the attacker can leverage these vulnerable state variables to achieve various RAV failures during real-time operation, and even evade existing defense solutions.

Motion planning, which aims to find a high-quality collision-free path in the configuration space, is a fundamental task in robotic systems. Recently, learning-based motion planners, especially the graph neural network-powered, have shown promising planning performance. However, though the state-of-the-art GNN planner can efficiently extract and learn graph information, its inherent mechanism is not well suited for graph search process, hindering its further performance improvement. To address this challenge and fully unleash the potential of GNN in motion planning, this paper proposes GraphMP, a neural motion planner for both low and high-dimensional planning tasks. With the customized model architecture and training mechanism design, GraphMP can simultaneously perform efficient graph pattern extraction and graph search processing, leading to strong planning performance. Experiments on a variety of environments, ranging from 2D Maze to 14D dual KUKA robotic arm, show that our proposed GraphMP achieves significant improvement on path quality and planning speed over the state-of-the-art learning-based and classical planners; while preserving the competitive success rate.

Mobile context determination is an important step for many context-aware services such as location-based services, enterprise policy enforcement, building/room occupancy detection for power/HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the user is in “Location X”) are defined based on mobile context, it is paramount to verify the accuracy of the mobile context. Most of the existing solutions rely on context obtained directly from sensors which could be hacked, noisy or insufficient, which cannot be relied upon for security applications. In this article, we take a different approach by modeling mobile context based on past context data of related users and considering its unique challenges such as missing features. To this end, we propose three models for modeling mobile context based on symbolic time series data of feature-value pairs—two stochastic models based on the theory of Hidden Markov Models (HMMs) and one model based on deep learning—personalized model(HPContext),collaborative filtering model((HCFContext)), anddeep learning model(DeepContext) to eventually replaceHPContext.HPContextandDeepContextpredict the current context using sequential history of the user's own past context observations; whileHCFContextenhances the former with collaborative filtering features, which enables it to predict the current context of the primary user based on the context observations of users related to the primary user, e.g., same team colleagues in the company, gym friends, family members, etc.DeepContextmodels mobile context based on symbolic (i.e., categorical valued rather than continuous-valued) time series data using deep learning techniques. These models are then used to determine the context of the primary user at the current instant or some timesteps in to the future. Each of the proposed models can also be used to enhance or complement the context obtained from sensors. Finally, these models are thoroughly validated on a real-life dataset.

Neural network (NN)-based methods have emerged as an attractive approach for robot motion planning due to strong learning capabilities of NN models and their inherently high parallelism. Despite the current development in this direction, the efficient capture and processing of important sequential and spatial information, in a direct and simultaneous way, is still relatively under-explored. To overcome the challenge and unlock the potentials of neural networks for motion planning tasks, in this paper, we propose STP-Net, an end-to-end learning framework that can fully extract and leverage important spatio-temporal information to form an efficient neural motion planner. By interpreting the movement of the robot as a video clip, robot motion planning is transformed to a video prediction task that can be performed by STP-Net in both spatially and temporally efficient ways. Empirical evaluations across different seen and unseen environments show that, with nearly 100% accuracy (aka, success rate), STP-Net demonstrates very promising performance with respect to both planning speed and path cost. Compared with existing NN-based motion planners, STP-Net achieves at least 5×, 2.6× and 1.8× faster speed with lower path cost on 2D Random Forest, 2D Maze and 3D Random Forest environments, respectively. Furthermore, STP-Net can quickly and simultaneously compute multiple near-optimal paths in multi-robot motion planning tasks.

Unmanned Aerial Vehicles as a service (UAVaaS) has increased the field deployment of Robotic Aerial Vehicles (RAVs) for different services such as transportation and terrain exploration. These RAVs are controlled by firmware, which is often closed-source, developed by vendors, and flashed into the ROM. While these binary blobs enable off-the-shelf management of RAVs, end users (individuals or organizations) have no idea if the control firmware is designed and implemented correctly, and can only rely on firmware updates from vendors when any vulnerability is discovered. This paper proposes DisPatch, the first reverse engineering and patching framework for understanding and improving controller design and implementation within RAV firmware. DisPatch first decompiles binary instructions and recovers controller functions and core controller variables by combining control theory with program analysis using symbolic execution and data flow analysis. End users can then write a patch in a domain-specific language (DSL), which will be translated and injected into the binary firmware by DisPatch automatically. We have applied DisPatch to two instances of commodity firmware from3DR IRIS+ and MantisQ RAVs and demonstrated 100% and 80.7% accuracy respectively in the controller decompilation. We have also shown the ability to prevent severe controller performance degradation by patching two real-world bugs with in the firmware and without breaking other functionality. Finally, we show that DisPatch introduces less than 0.53% of space overhead and 1.48% of runtime overhead without violating the soft real-time deadlines. DisPatch provides the first step towards an RAV binary firmware reverse engineering and patching system to customize controller design and implementation.

Mobile devices aggregate various types of data from sensitive corporate documents to personal content. While users desire to access this content on a single device via a unified user experience and through any mobile app, protecting this data is challenging. Even though different data types have different security and privacy needs, mobile operating systems include only a few, if any, functionalities for fine-grained data protection. We present SWIRLS, an Android-based mobile OS that provides a policy-based information-flow data protection abstraction for mobile apps to support BYOD (bring-your-own-device) use cases. SWIRLS attaches security policies to individual pieces of data and enforces these policies as the data flows through the device. Unlike current BYOD solutions like VMs that create duplication overload, SWIRLS provides a single environment to access content from different security contexts using the same applications while monitoring for malicious data leakage. SWIRLS leverages a two-level hybrid information flow tracking (IFT) mechanism to track both intra-application flows and a higher level IFT based on processes for application isolation. Our evaluation presents BYOD data protection use-cases such as limiting document sharing, preventing leakage based on document classification and security policies based on geo-fencing. SWIRLS only imposes a low battery consumption and performance overhead.

Rapid prototyping makes additive manufacturing (or 3D printing) useful in critical application domains such as aerospace, automotive, and medical. The rapid expansion of these applications should prompt the examination of the underlying security of 3D printed objects. In this paper, we present Mystique, a novel class of stealthy attacks on printed objects that leverage the fourth dimension of emerging 4D printing technology to introduce embedded logic bombs through manufacturing process manipulation. Mystique enables visually benign objects to behave maliciously upon the activation of the logic bomb during operation. It leverages the manufacturing process to embed a physical logic bomb that can be triggered with specific stimuli to change the physical and mechanical properties of the printed objects. These changes in properties can potentially cause catastrophic operational failures when the objects are used in critical applications such as drones, prosthesis, or medical applications.

STereoLithography (STL) files describe the geometry of objects to be printed in additive manufacturing. Previous studies have shown that the STL files that describe functional objects can be attacked such that the objects appear normal during inspection, but fail during operation. Such attacks lead to damage to systems that use the objects and possibly loss of life. The detection of any defects caused due to the attacks nowadays is limited to the quality control process after the objects are manufactured.We present a Trusted Integrity Verifier (TIV) to detect such attacks on 3D printed objects in the early stage of the manufacturing process. These type of new attacks cannot be detected by traditional software security mechanisms since they only focus on the printers and do not consider the inputs (STL design files) to the printer. Early detection of attacks prevents from printing malicious objects resulting in saving time, resources and manufacturing efforts. TIV detects malicious STL files using multidisciplinary approaches unlike the traditional integrity verification techniques. TIV develops a void detection module based on computer vision techniques to identify the internal defects such as voids. Some of these features could be from the design and some could be due to the attack. To differentiate the malicious features from the design features, TIV develops safety verification module based on a numerical method. TIV's safety verification module is used to differentiate the malicious features from the design features by calculating the load bearing mechanical stress on the objects. These mechanical stresses are compared to the safety operational conditions to determine if the printed object will break or fail during its normal operation.To illustrate TIV's generality and scalability, we conducted a large-scale analysis on 16,000 real-world 3D print STL files. TIV verified the STL files successfully as either safe or malicious with high accuracy of 92% for object classification and 96.5% for void detection.

We propose NSYNC, a practical framework to compare side-channel signals for real-time intrusion detection in Additive Manufacturing (AM) systems. The motivation to develop NSYNC is that we find AM systems are asynchronous in nature and there is random variation in timing in a printing process. Although this random variation, referred to as time noise, is very small compared with the duration of a printing process, it can cause existing Intrusion Detection Systems (IDSs) to fail. To deal with this problem, NSYNC incorporates a dynamic synchronizer to find the timing relationship between two signals. This timing relationship, referred to as the horizontal displacement, can not only be used to mitigate the adverse effect of time noise on calculating the (vertical) distance between signals, but also be used as indicators for intrusion detection. An existing dynamic synchronizer is Dynamic Time Warping (DTW). However, we found in experiments that DTW not only consumes an excessive amount of computational resources but also has limited accuracy for processing side-channel signals. To solve this problem, we propose a novel dynamic synchronizer, called Dynamic Window Matching (DWM), to replace DTW. To compare NSYNC against existing IDSs, we built a data acquisition system that is capable of collecting six different types of side-channel signals and performed a total of 302 benign printing processes and a total of 200 malicious printing processes with two printers. Our experiment results show that existing IDSs leveraging side-channel signals in AM systems can only achieve an accuracy from 0.50 to 0.88, whereas our proposed NSYNC can reach an accuracy of 0.99.

Filter pruning has been widely used for neural network compression because of its enabled practical acceleration. To date, most of the existing filter pruning works explore the importance of filters via using intra-channel information. In this paper, starting from an inter-channel perspective, we propose to perform efficient filter pruning using Channel Independence, a metric that measures the correlations among different feature maps. The less independent feature map is interpreted as containing less useful information$/$knowledge, and hence its corresponding filter can be pruned without affecting model capacity. We systematically investigate the quantification metric, measuring scheme and sensitiveness$/$reliability of channel independence in the context of filter pruning. Our evaluation results for different models on various datasets show the superior performance of our approach. Notably, on CIFAR-10 dataset our solution can bring $0.75\%$ and $0.94\%$ accuracy increase over baseline ResNet-56 and ResNet-110 models, respectively, and meanwhile the model size and FLOPs are reduced by $42.8\%$ and $47.4\%$ (for ResNet-56) and $48.3\%$ and $52.1\%$ (for ResNet-110), respectively. On ImageNet dataset, our approach can achieve $40.8\%$ and $44.8\%$ storage and computation reductions, respectively, with $0.15\%$ accuracy increase over the baseline ResNet-50 model. The code is available at https://github.com/Eclipsess/CHIP_NeurIPS2021.

The safe operation of robotic aerial vehicles (RAV) requires effective security protection of their controllers against cyber-physical attacks. The frequency and sophistication of past attacks against such embedded platforms highlight the need for better defense mechanisms. Existing estimation-based control monitors have tradeoffs, with lightweight linear state estimators lacking sufficient coverage, and heavier data-driven learned models facing implementation and accuracy issues on a constrained real-time RAV. We present Mini-Me, a data-driven online monitoring framework that models the program-level control state dynamics to detect runtime data-oriented attacks against RAVs. Mini-Me leverages the internal dataflow information and control variable dependencies of RAV controller functions to train a neural network-based approximate model as the lightweight replica of the original controller programs. Mini-Me runs the minimal approximate model and detects malicious control state deviation by comparing the estimated outputs with those outputs calculated by the original controller program. We demonstrate Mini-Me on a widely adopted RAV physical model as well as popular RAV virtual models based on open-source firmware, ArduPilot and PX4, and show its effectiveness in detecting five types of attack cases with an average 0.34% space overhead and 2.6% runtime overhead.

Mobile location-based services are important context-aware services that are more and more used for enforcing security policies, for supporting indoor room navigation, and for providing personalized assistance. However, a major problem still remains unaddressed-the lack of solutions that work across buildings while not using additional infrastructure and also accounting for privacy and reliability needs. A privacy-preserving, multi-modal, cross-building, collaborative localization platform is proposed based on Wi-Fi Received Signal Strength Indicator (RSSI) (existing infrastructure), Cellular RSSI, sound, light, and geo-magnetic levels, that enables sub-room level localization. The solution is fully based on mobile phones and existing Wi-Fi infrastructure, and has privacy inherently built into it via cryptographically-secured onion routing and perturbation/randomization techniques. It also exploits the idea of weighted collaboration to increase the reliability as well as to limit the effect of noisy devices (due to sensor noise/privacy). The solution has been analyzed in terms of latency overhead due to onion-routing, request load on phones, privacy-accuracy tradeoffs, optimum parameters, granularity, different classification algorithms using real location data collected at multiple indoor and outdoor locations via an Android application. The additional features other than Wi-Fi RSSI values are shown to increase the accuracy to a maximum of 15 percent, while considering Geo-magnetic field is shown to enhance the granularity from 2.5 m to ≈1 m, a 60 percent improvement.

Mobile and IoT operating systems–and their ensuing software updates–are usually distributed as binary files. Given that these binary files are commonly closed source, users or businesses who want to assess the security of the software need to rely on reverse engineering. Further, verifying the correct application of the latest software patches in a given binary is an open problem. The regular application of software patches is a central pillar for improving mobile and IoT device security. This requires developers, integrators, and vendors to propagate patches to all affected devices in a timely and coordinated fashion. In practice, vendors follow different and sometimes improper security update agendas for both mobile and IoT products. Moreover, previous studies revealed the existence of a hidden patch gap: several vendors falsely reported that they patched vulnerabilities. Therefore, techniques to verify whether vulnerabilities have been patched or not in a given binary are essential. Deep learning approaches have shown to be promising for static binary analyses with respect to inferring binary similarity as well as vulnerability detection. However, these approaches fail to capture the dynamic behavior of these systems, and, as a result, they may inundate the analysis with false positives when performing vulnerability discovery in the wild. In particular, they cannot capture the fine-grained characteristics necessary to distinguish whether a vulnerability has been patched or not. In this paper, we present PATCHECKO, a vulnerability and patch presence detection framework for executable binaries. PATCHECKO relies on a hybrid, cross-platform binary code similarity analysis that combines deep learning-based static binary analysis with dynamic binary analysis. PATCHECKO does not require access to the source code of the target binary nor that of vulnerable functions. We evaluate PATCHECKO on the most recent Google Pixel 2 smartphone and the Android Things IoT firmware images, within which 25 known CVE vulnerabilities have been previously reported and patched. Our deep learning model shows a vulnerability detection accuracy of over 93%. We further prune the candidates found by the deep learning stage–which includes false positives–via dynamic binary analysis. Consequently, PATCHECKO successfully identifies the correct matches among the candidate functions in the top 3 ranked outcomes 100% of the time. Furthermore, PATCHECKO's differential engine distinguishes between functions that are still vulnerable and those that are patched with an accuracy of 96%.

With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder) and classify drone mis-operations based on real-time sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data and learn the spatial and temporal dynamics in the sensor data. We validate the proposed deep-learning architectures via simulations and realworld experiments on a drone. Empirical results show that our solution is able to detect (with over 90% accuracy) and classify various types of drone mis-operations (with about 99% accuracy (simulation data) and upto 85% accuracy (experimental data)).

The safety of critical cyber-physical IoT devices hinges on the security of their embedded software that implements control algorithms for monitoring and control of the associated physical processes, e.g., robotics and drones. Reverse engineering of the corresponding embedded controller software binaries enables their security analysis by extracting high-level, domain-specific, and cyber-physical execution semantic information from executables. We present MISMO, a domain-specific reverse engineering framework for embedded binary code in emerging cyber-physical IoT control application domains. The reverse engineering outcomes can be used for firmware vulnerability assessment, memory forensics analysis, targeted memory data attacks, or binary patching for dynamic selective memory protection (e.g., important control algorithm parameters). MISMO performs semantic-matching at an algorithmic level that can help with the understanding of any possible cyber-physical security flaws. MISMO compares low-level binary symbolic values and high-level algorithmic expressions to extract domain-specific semantic information for the binary's code and data. MISMO enables a finer-grained understanding of the controller by identifying the specific control and state estimation algorithms used. We evaluated MISMO on 2,263 popular firmware binaries by 30 commercial vendors from 6 application domains including drones, self-driving cars, smart homes, robotics, 3D printers, and the Linux kernel controllers. The results show that MISMO can accurately extract the algorithm-level semantics of the embedded binary code and data regions. We discovered a zero-day vulnerability in the Linux kernel controllers versions 3.13 and above.

Mobile context determination is an important step for many context-aware services such as location-based services, enterprise policy enforcement, building/room occupancy detection for power/HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the user is in "Location X") are defined based on mobile context, it is paramount to verify the accuracy of the mobile context. To this end, two stochastic models based on the theory of Hidden Markov Models (HMMs) to obtain mobile context are proposed-personalized model (HPContext) and collaborative filtering model (HCFContext). The former predicts the current context using sequential history of the user's past context observations; the latter enhances HPContext with collaborative filtering features, which enables it to predict the current context of the primary user based on the context observations of users related to the primary user, e.g., same team colleagues in company, gym friends, family members, etc. Each of the proposed models can also be used to enhance/complement the context obtained from sensors. Furthermore, since privacy is a concern in collaborative filtering, a privacy-preserving method is proposed to derive HCFContext model parameters based on the concepts of homomorphic encryption. Finally, these models are thoroughly validated on a real-life dataset.

Recent major attacks against unmanned aerial vehicles (UAV) and their controller software necessitate domain-specific cyber-physical security protection. Existing offline formal methods for (untrusted) controller code verification usually face state-explosion. On the other hand, runtime monitors for cyber-physical UAVs often lead to too-late notifications about unsafe states that makes timely safe operation recovery impossible.

Automated vulnerability assessment and exploit generation for computing systems have been explored for decades. However, these approaches are incomplete in assessing industrial control systems, where networks of computing devices and physical processes interact for safety-critical missions. We present an attack synthesis algorithm against such cyber-physical electricity grids. The algorithm explores both discrete network configurations and continuous dynamics of the plant's embedded control system to search for attack strategies that evade detection with conventional monitors. The algorithm enabling this exploration is rooted in recent developments in the hybrid system verification research: it effectively approximates the behavior of the system for a set of possible attacks by computing sensitivity of the system's response to variations in the attack parameters. For parts of the attack space, the proposed algorithm can infer whether or not there exists a feasible attack that avoids triggering protection measures such as relays and steady-state monitors. The algorithm can take into account constraints on the attack space such as the power system topology and the set of controllers across the plant that can be compromised without detection. With a proof-of-concept prototype, we demonstrate the synthesis of transient attacks in several typical electricity grids and analyze the robustness of the synthesized attacks to perturbations in the network parameters.

Trustworthy operation of industrial control systems depends on secure and real-time code execution on the embedded programmable logic controllers (PLCs). The controllers monitor and control the critical infrastructures, such as electric power grids and healthcare platforms, and continuously report back the system status to human operators. We present Zeus, a contactless embedded controller security monitor to ensure its execution control flow integrity. Zeus leverages the electromagnetic emission by the PLC circuitry during the execution of the controller programs. Zeus's contactless execution tracking enables non-intrusive monitoring of security-critical controllers with tight real-time constraints. Those devices often cannot tolerate the cost and performance overhead that comes with additional traditional hardware or software monitoring modules. Furthermore, Zeus provides an air-gap between the monitor (trusted computing base) and the target (potentially compromised) PLC. This eliminates the possibility of the monitor infection by the same attack vectors.

Power grid operations rely on the trustworthy operation of critical control center functionalities, including the so-called Economic Dispatch (ED) problem. The ED problem is a large-scale optimization problem that is periodically solved by the system operator to ensure the balance of supply and load while maintaining reliability constraints. In this paper, we propose a semantics-based attack generation and implementation approach to study the security of the ED problem.1 Firstly, we generate optimal attack vectors to transmission line ratings to induce maximum congestion in the critical lines, resulting in the violation of capacity limits. We formulate a bilevel optimization problem in which the attacker chooses manipulations of line capacity ratings to maximinimize the percentage line capacity violations under linear power flows. We reformulate the bilevel problem as a mixed integer linear program that can be solved efficiently. Secondly, we describe how the optimal attack vectors can be implemented in commercial energy management systems (EMSs). The attack explores the dynamic memory space of the EMS, and replaces the true line capacity ratings stored in data regions with the optimal attack vectors. In contrast to the well-known false data injection attacks to control systems that require compromising distributed sensors, our approach directly implements attacks to the control center server. Our experimental results on benchmark power systems and five widely utilized EMSs show the practical feasibility of our attack generation and implementation approach.

Mobile phones provide an excellent opportunity for building context-aware applications. In particular, location-based services are important context-aware services that are more and more used for enforcing security policies, for supporting indoor room navigation, and for providing personalized assistance. However, a major problem still remains unaddressed--the lack of solutions that work across buildings while not using additional infrastructure and also accounting for privacy and reliability needs. In this paper, a privacy-preserving, multi-modal, cross-building, collaborative localization platform is proposed based on Wi-Fi RSSI (existing infrastructure), Cellular RSSI, sound and light levels, that enables room-level localization as main application (though sub room level granularity is possible). The privacy is inherently built into the solution based on onion routing, and perturbation/randomization techniques, and exploits the idea of weighted collaboration to increase the reliability as well as to limit the effect of noisy devices (due to sensor noise/privacy). The proposed solution has been analyzed in terms of privacy, accuracy, optimum parameters, and other overheads on location data collected at multiple indoor and outdoor locations.

Summary Lack of trust has become one of the main concerns of users who tend to utilize one or multiple Cloud providers. Trustworthy Cloud‐based computing and data storage require secure and efficient solutions which allow clients to remotely store and process their data in the Cloud. User‐side encryption is an established method to secure the user data on the Cloud. However, using encryption, we lose processing capabilities, such as searching, over the Cloud data. In this paper, we present RESeED, a tool that provides user‐transparent and Cloud‐agnostic regular‐expression search functionality over encrypted data across multiple Clouds. Upon a client's intent to upload a new document to the Cloud, RESeED analyzes the document's content and updates its data structures accordingly. Then, it encrypts and transfers the document to the Cloud. RESeED provides the regular‐expression search functionality over encrypted data by translating the search queries on‐the‐fly to finite automata and analyzing concise and secure representations of the data before asking the Cloud to download the encrypted documents. RESeED's parallel architecture enables efficient search over large‐scale (and potentially big data scale) data‐sets. We evaluate the performance of RESeED experimentally and demonstrate its scalability and correctness using real‐world data‐sets fromarXiv.organd Internet Engineering Task Force (IETF). Our results show that RESeED produces accurate query responses with a reasonable (≃6%) storage overhead. The results also demonstrate that for many search queries, RESeED performs faster in compare with thegreputility that functions on unencrypted data. Copyright © 2017 John Wiley & Sons, Ltd.

Trustworthy and usable healthcare requires not only effective disease diagnostic procedures to ensure delivery of rapid and accurate outcomes, but also lightweight user privacy-preserving capabilities for resource-limited medical sensing devices. In this paper, we present MedSen, a portable, inexpensive and secure smartphone-based biomarker1 detection sensor to provide users with easy-to-use real-time disease diagnostic capabilities without the need for in-person clinical visits. To minimize the deployment cost and size without sacrificing the diagnostic accuracy, security and time requirement, MedSen operates as a dongle to the user's smartphone and leverages the smartphone's computational capabilities for its real-time data processing. From the security viewpoint, MedSen introduces a new hardware-level trusted sensing framework, built in the sensor, to encrypt measured analog signals related to cell counting in the patient's blood sample, at the data acquisition point. To protect the user privacy, MedSen's in-sensor encryption scheme conceals the user's private information before sending them out for cloud-based medical diagnostics analysis. The analysis outcomes are sent back to Med-Sen for decryption and user notifications. Additionally, MedSen introduces cyto-coded passwords to authenticate the user to the cloud server without the need for explicit screen password entry. Each user's password constitutes a predetermined number of synthetic beads with different dielectric characteristics. MedSen mixes the password beads with the user's blood before submitting the data for diagnostics analysis. The cloud server authenticates the user based on the statistics and characteristics of the beads with the blood sample, and links the user's identity to the encrypted analysis outcomes. We have implemented a real-world working prototype of MedSen through bio-sensor fabrication and smartphone app (Android) implementations. Our results show that MedSen can reliably classify different users based on their cyto-coded passwords with high accuracy. MedSen's built-in analog signal encryption guarantees the user's privacy by considering the smartphone and cloud server possibly untrusted (curious but honest). MedSen's end-to-end time requirement for disease diagnostics is approximately 0.2 seconds on average.

Mobile devices are increasingly becoming a melting pot of different types of data ranging from sensitive corporate documents to commercial media to personal content produced and shared via online social networks. While it is desirable for such diverse content to be accessible from the same device via a unified user experience and through a rich plethora of mobile apps, ensuring that this data remains protected has become challenging. Even though different data types have very different security and privacy needs and accidental instances of data leakage are common, today's mobile operating systems include few, if any, facilities for fine-grained data protection and isolation. In this paper, we present SWIRLS, an Android-based mobile OS that provides a rich policy-based information-flow data protection abstraction for mobile apps to support BYOD (bring-your-own-device) use cases. SWIRLS allows security and privacy policies to be attached to individual pieces of data contained in signed and encrypted capsules, and enforces these policies as the data flows through the device. Unlike current BYOD solutions like VMs and containers that create duplication and cognitive overload, SWIRLS provides a single environment that allows users to access content belonging to different security contexts using the same applications without fear of inadverdant or malicious data leakage. SWIRLS also unburdens app developers from having to worry about security policies, and provides APIs through which they can create seamless multi-security-context user interfaces. To implement it's abstractions, SWIRLS develops a cryptographically protected capsule distribution and installation scheme, enhances Taintdroid-based taint-tracking mechanisms to support efficient kernel and user-space security policy enforcement, implements techniques for persisting security context along with data, and provides transparent security-context switching mechanisms. Using our Android-based prototype (>25K LOC), we show a number of data protection use-cases such as isolation of personal and work data, limiting document sharing and preventing leakage based on document classification, and security policies based on geo-and time-fencing. Our experiments show that SWIRLS imposes a very minimal overhead in both battery consumption and performance.

It is critical to monitor IT systems that are part of energy delivery system infrastructure. The problem with intrusion detection systems (IDSes) is that they often produce thousands of alerts daily that must be dealt with by administrators manually. To provide situational awareness, detection systems usually employ (alert, priority) mappings that are either built in the IDS without consideration of the high-level mission objectives of the infrastructure, or manually defined by administrators through a time-consuming task that requires deep system-level expertise. In this paper, we present Seclius, an online security evaluation framework that translates low-level IDS alerts into a high-level system security measure and provides a ranking of past malicious events and affected system assets based on how crucial they are for the organization. Seclius significantly reduces human involvement by automatically learning system characteristics, providing a simple formalism that administrators can use to define security requirements. Experiments on a process control network with real vulnerabilities and a multistep attack show that Seclius can accurately report system security with low performance overhead and support the time-constrained security decision-making process that is necessary for critical infrastructure.

Capabilities for trustworthy cloud-based computing and data storage require usable, secure and efficient solutions which allow clients to remotely store and process their data in the cloud. In this paper, we present RESeED, a tool which provides user-transparent and cloud-agnostic search over encrypted data using regular expressions without requiring cloud providers to make changes to their existing infrastructure. When a client asks RESeED to upload a new file in the cloud, RESeED analyzes the file's content and updates novel data structures accordingly, encrypting and transferring the new data to the cloud. RESeED provides regular expression search over this encrypted data by translating queries on-the-fly to finite automata and analyzes efficient and secure representations of the data before asking the cloud to download the encrypted files. We evaulate a working prototype of RESeED experimentally (currently publicly available) and show the scalability and correctness of our approach using real-world data sets from arXiv.org and the IETF. We show absolute accuracy for RESeED, with very low (6%) overhead, and high performability, even beating grep for some benchmarks.

Despite the increasing number of social engineering attacks through web browser applications, detection of socially engineered trojan downloads by enticed victim users remains a challenging endeavor. In this paper, we present TroGuard, a semi-automated web-based trojan detection solution, that notifies the user if the application she downloaded behaves differently than what she expected at download time. TroGuard builds on the hypothesis that in spite of millions of currently downloadable executables on the Internet, almost all of them provide functionalities from a limited set. Additionally, because each functionality, e.g., text editor, requires particular system resources, it exhibits a unique system-level activity pattern. During an offline process, TroGuard creates a profile dictionary of various functionalities. This profile dictionary is then used to warn the user if she downloads an executable whose observed activity does not match its advertised functionality (extracted through automated analysis of the download website). Our experimental results prove the above mentioned premise empirically and show that TroGuard can identify real-world socially engineered trojan download attacks effectively.

We present Reseed, a tool that provides user-transparent and Cloud-agnostic regular expression search over encrypted data without requiring trust in the Cloud, or changes to Cloud infrastructure. Upon receiving a search query, Reseed translates it to a finite automata and analyzes efficient and secure representations of the data before asking the Cloud to download the matching encrypted files. We demonstrate and evaluate a working prototype of Reseed and show the scalability and correctness of our approach using data from arXiv.org. For more details see our Technical Report.

Preserving the availability and integrity of networked computing systems in the face of fast-spreading intrusions requires advances not only in detection algorithms, but also in automated response techniques. In this paper, we propose a new approach to automated response called the response and recovery engine (RRE). Our engine employs a game-theoretic response strategy against adversaries modeled as opponents in a two-player Stackelberg stochastic game. The RRE applies attack-response trees (ART) to analyze undesired system-level security events within host computers and their countermeasures using Boolean logic to combine lower level attack consequences. In addition, the RRE accounts for uncertainties in intrusion detection alert notifications. The RRE then chooses optimal response actions by solving a partially observable competitive Markov decision process that is automatically derived from attack-response trees. To support network-level multiobjective response selection and consider possibly conflicting network security properties, we employ fuzzy logic theory to calculate the network-level security metric values, i.e., security levels of the system's current and potentially future states in each stage of the game. In particular, inputs to the network-level game-theoretic response selection engine, are first fed into the fuzzy system that is in charge of a nonlinear inference and quantitative ranking of the possible actions using its previously defined fuzzy rule set. Consequently, the optimal network-level response actions are chosen through a game-theoretic optimization process. Experimental results show that the RRE, using Snort's alerts, can protect large networks for which attack-response trees have more than 500 nodes.

Trustworthy operation of safety-critical infrastructures necessitates efficient solutions that satisfy both realtimeness and security requirements simultaneously. We present Sechduler, a formally verifiable security-aware operating system scheduler that dynamically makes sure that system computational resources are allocated to individual waiting tasks in an optimal order such that, if feasible, neither realtime nor security requirements of the system are violated. Additionally, if not both of the requirements can be satisfied simultaneously, Sechduler makes use of easy-to-define linear temporal logic-based policies as well as automatically generated Buchi automaton-based monitors, compiled as loadable kernel modules, to enforce which requirements should get the priority. Our experimental results show that Sechduler can adaptively enforce the system-wide logic-based temporal policies within the kernel and with minimal performance overhead of 3 % on average to guarantee high level of combined security and realtimeness simultaneously.

Trustworthy cloud services require practical secure storage and data processing techniques that enable end-users to upload sensitive data and perform computations remotely without having to first download the data. In this paper, we present Dragon fruit, a cloud provider-agnostic searchable cloud data storage solution, that allows the utilization of several existing cloud providers, and the execution of search queries over encrypted data by customers, using unmodified cloud infrastructures. In particular, Dragon fruit makes use of filename search capabilities that most existing cloud providers support to embed searchable data within the cloud. Dragon fruit supports queries using complex Boolean expressions on structured data formats such as JSON. Our evaluation on a real-world test-bed show that Dragon fruit is able to work with several cloud providers such as Google-Drive and Drop box simultaneously and is able to respond to search requests within a few seconds proving a reasonable performance overhead for practical usage.

Trustworthy operation of safety-critical infrastructures necessitates efficient solutions that satisfy both realtimeness and security requirements simultaneously. In this paper, we present Sechduler, a formally verifiable security-aware operating system scheduler that dynamically makes sure that system computational resources are allocated to individual waiting tasks in an optimal order such that, if feasible, neither real time nor security requirements of the system are violated. Additionally, if not both of the requirements can be satisfied simultaneously, Sechduler makes use of easy-to-define linear temporal logic-based policies as well as automatically generated Buchi automaton-based monitors, compiled as loadable kernel modules, to enforce which requirements should get the priority. Our experimental results show that Sechduler can adaptively enforce the system-wide logic-based temporal policies within the kernel and with minimal performance overhead of 3% on average to guarantee high level of combined security and realtimeness simultaneously.

Preserving the availability and integrity of security-critical computer systems in a fast-spreading sophisticated intrusions environment, requires advance algorithms, accurate and efficient intrusion diagnosis, along side with root-cause analysis techniques. In this paper we introduce FloTracker that is an online log-free host-based root-cause analysis detection engine, with instantaneous forensics capabilities. FloTracker presents security administrators as well as automated response systems, with immediate forensics information. For instance, it will identify a system's entry point of intrusion as soon as a critical security incident occurs, e.g., a sensitive system file modification is detected within the target system. To this end, FloTracker automatically defines an access control policy set (possibly with no access restriction) for the target system that facilitates real-time backtracking of an intrusion, given a detection point. Our experimental results on a real-world SE-Linux test-bed showed that the FloTracker could efficiently update the system's configuration thus modifications will not affect the functionalities of the system, yet providing a log-free and instantaneous root-cause analysis capability.

The advanced metering infrastructure (AMI) is a crucial component of the smart grid, replacing traditional analog devices with computerized smart meters. Smart meters have not only allowed for efficient management of many end-users, but also have made AMI an attractive target for remote exploits and local physical tampering with the end goal of stealing energy. While smart meters posses multiple sensors and data sources that can indicate energy theft, in practice, the individual methods exhibit many false positives. In this paper, we present AMIDS, an AMI intrusion detection system that uses information fusion to combine the sensors and consumption data from a smart meter to more accurately detect energy theft. AMIDS combines meter audit logs of physical and cyber events with consumption data to more accurately model and detect theft-related behavior. Our experimental results on normal and anomalous load profiles show that AMIDS can identify energy theft efforts with high accuracy. Furthermore, AMIDS correctly identified legitimate load profile changes that more elementary analyses classified as malicious.

To protect complex power-grid control networks, efficient security assessment techniques are required. However, efficiently making sure that calculated security measures match the expert knowledge is a challenging endeavor. In this paper, we present EliMet, a framework that combines information from different sources and estimates the extent to which a control network meets its security objective. Initially, during an offline phase, a state-based model of the network is generated, and security-level of each state is measured using a generic and easy-to-compute metric. EliMet then passively observes system operators' online reactive behavior against security incidents, and accordingly refines the calculated security measure values. Finally, to make the values comply with the expert knowledge, EliMet actively queries operators regarding those states for which sufficient information was not gained during the passive observation. Our experimental results show that EliMet can optimally make use of prior knowledge as well as automated inference techniques to minimize human involvement and efficiently deduce the expert knowledge regarding individual states of that particular system.

Preserving the availability and integrity of networked computing systems in the face of fast-spreading intrusions requires advances not only in detection algorithms, but also in automated response techniques. In this paper, we propose a new approach to automated response called the Response and Recovery Engine (RRE). Our engine employs a game-theoretic response strategy against adversaries modeled as opponents in a two-player Stackelberg stochastic game. RRE applies attack-response trees to analyze undesired security events and their countermeasures using Boolean logic to combine lower-level attack consequences. In addition, RRE accounts for uncertainties in intrusion detection alert notifications. RRE then chooses optimal response actions by solving a partially observable competitive Markov decision process that is automatically derived from attack-response trees. Experimental results show that RRE, using Snort's alerts, can protect large networks for which attack-response trees have more than 900 nodes.