Edge and IoT environments face growing security threats due to their distributed and dynamic nature, where malicious nodes can compromise clustering, coordination, and data integrity. We introduce TRACED, a fully distributed framework for trust-aware clustering and role assignment in adversarial environments. TRACED continuously evaluates node trustworthiness via lightweight challenge-response mechanisms, heartbeat monitoring, and behavioral analysis, updating trust scores with exponential moving averages. Nodes dynamically transition between worker and cluster head roles based on trust thresholds, capability, and resilience metrics. The framework integrates a Sybil protection layer using unique ID checks, publickey validation, and rate limiting to prevent fake node infiltration. Additionally, TRACED features robust heartbeat-based failure detection, adaptive role reassignment, and detailed metrics logging for post-analysis. Through extensive simulations across multiple scenarios, TRACED demonstrated resilience against Sybil attacks, dynamically isolated misbehaving nodes, and maintained functional clustering without central coordination. These results confirm that TRACED enables secure, adaptive, and fully decentralized clustering in volatile edge and IoT environments.

The rising popularity of mental wellbeing technologies has led many individuals to explore binaural beats—an emerging form of sound therapy proliferating on web and mobile platforms. However, it currently remains unknown whether users can trust binaural tracks on online platforms, or if they deceive unsuspecting users. Our research aims to address this problem by understanding (1) what binaural beats listeners expect from tracks and (2) whether online tracks conform to these expectations. To understand user expectations, we perform thematic analysis on online forum threads and blog posts to extract binaural beats goals and expectations tied to these goals. Next, we design a methodology to measure binaural beats tracks’ conformance to commonly held user expectations. This methodology comprises, (1) obtaining a track’s intent to induce a mental state through track metadata analysis, (2) extracting a track’s binaural beats time-frequency model using Fast Fourier Transform, (3) mapping user expectations to rules that identify deceptive tracks, and validating them on the track’s extracted intent and time-frequency model. We evaluate ∼7K binaural beats tracks and find that only 7.5% conform to commonly held user expectations, while the remaining 92.5% deceive users with deviant claims (e.g., eroticism, weight loss) or deliver contradicting binaural beats. Our study underscores the significance of understanding users’ expectations and verifying conformance of online wellness technologies to expose discrepancies in expectations.

Binary instrumentation tools are widely used to facilitate the development of hardware and software systems. Traditionally, these tools are designed around a fixed Instruction Set Architecture (ISA) specification. However there is a shift in the architectural community towards open ISAs, whose key feature is the ability to add custom ISA extensions. The lack of extensibility in traditional binary instrumentation tools limits their capacity to adapt to these evolving ISAs, thus hindering their ability to analyze and modify binaries built for open ISAs.

In Voice Assistant (VA) platforms, when users add devices to their accounts and give voice commands, complex interactions occur between the devices, skills, VA clouds, and vendor clouds. These interactions are governed by the device management capabilities (DMC) of VA platforms, which rely on device names, types, and associated skills in the user account. Prior work studied vulnerabilities in specific VA components, such as hidden voice commands and bypassing skill vetting. However, the security and privacy implications of device management flaws have largely been unexplored. In this paper, we introduce DMC-Xplorer, a testing framework for the automated discovery of VA device management flaws. We first introduce VA description language (VDL), a new domain-specific language to create VA environments for testing, using VA and skill developer APIs. DMC-Xplorer then selects VA parameters (device names, types, vendors, actions, and skills) in a combinatorial approach and creates VA environments with VDL. It issues real voice commands to the environment via developer APIs and logs event traces. It validates the traces against three formal security properties that define the secure operation of VA platforms. Lastly, DMC-Xplorer identifies the root cause of property violations through intervention analysis to identify VA device management flaws. We exercised DMC-Xplorer on Amazon Alexa and Google Home and discovered two design flaws that can be exploited to launch four attacks. We show that malicious skills with default permissions can eavesdrop on privacy-sensitive device states, prevent users from controlling their devices, and disrupt the services on the VA cloud.

Physical sensor attacks against robotic vehicles (RV) have become a serious concern due to their prevalence and potential physical threat. However, RV software developers often do not deploy appropriate countermeasures. This hesitance stems from their belief that attackers face substantial challenges when conducting sensor attacks, e.g., nullifying sensor redundancy in hardware and circumventing sensor filters in software. Yet, we discover that attackers can overcome the challenges by fulfilling specific prerequisites and finely tuning attack parameters. The misconceptions that the developers have arisen from a lack of study regarding the level of difficulty attackers face in successfully achieving their attack goals, which we call "attack hardness".In this paper, we examine the hardness of 12 well-known sensor attacks. We first identify the prerequisites required to conduct the attacks successfully. We then quantify the hardness of each attack as how frequent the prerequisites enabling a specific attack are in the real world. To automate this analysis, we introduce RVPROBER, an attack prerequisite analysis framework. RVPROBER discovered that the 12 sensor attacks require, on average, 4.4 prerequisites, highlighting that previous literature has often missed important details required to perform these attacks. By satisfying the identified prerequisites and tuning attack parameters, we increased the number of successful attacks from 6 to 11. Moreover, our analysis showed that an average of 57.08% of actual RV users are vulnerable to sensor attacks. Finally, starting from the identified prerequisites, we analyzed the reasons behind the success of each attack and found previously-unknown root causes, such as design flaws in the RV software’s fail-safe logic.

Intermittent (batteryless) devices operate solely using energy harvested from their environment. These devices turn on when they have energy and turn off during energy scarcity. Intermittent devices have recently become increasingly popular in smart buildings, manufacturing plants, and medical implantables as they eliminate the need for battery replacement and enable green computing. Despite their growing adoption in critical applications, the privacy implications of intermittent devices remain largely unexplored. In this paper, we introduce a novel remote side-channel attack. Our observation is that the network packet frequency of an intermittent device can be exploited to learn its turn-on/off patterns. From these patterns, we can infer the energy availability of a device, which reveals privacy-sensitive information about its operating environment, e.g., the presence or absence of individuals. To realize our attack, we develop a three-stage hierarchical inference framework that leverages the timestamped network packet sequence of intermittent devices. Our framework automatically extracts a set of temporal features from inter-packet-arrival timings. It then employs a series of models to uncover (1) whether a target intermittent device is present in the environment, (2) its energy harvester type (e.g., vibration or water flow), and (3) its energy availability conditions (e.g., high-vibration or no-vibration). To validate our attack effectiveness, we conduct experiments in two environments: a smart home and a miniature manufacturing plant equipped with three intermittent devices powered by solar energy, vibration, and temperature. By analyzing their energy availability patterns, we are able to infer user activities and presence in the smart home and the robot’s movement patterns in the manufacturing plant with an average accuracy of 85%. This sensitive information enables an adversary to launch domain-specific attacks, such as burglarizing a smart home when the user is asleep or timely tampering with plant sensors to cause maximum damage.

Pairing schemes establish cryptographic keys to secure communication among IoT devices. Existing pairing approaches that rely on trusted central entities, human interaction, or shared homogeneous context are prone to a single point of failure, have limited usability, and require additional sensors. Recent work has explored event timings observed by devices with heterogeneous sensing modalities as proof of co-presence for decentralized pairing. Yet, this approach incurs high pairing time, cannot pair sensors that sense continuous physical quantities and does not support group pairing, making it infeasible for many IoT deployments. In this paper, we design and develop IoTCupid, a secure group pairing system for IoT devices with heterogeneous sensing modalities, without requiring active user involvement. IoTCupid operates in three phases: (a) detecting events sensed by both instant and continuous sensors with a novel window-based derivation technique, (b) grouping the events through a fuzzy clustering algorithm to extract inter-event timings, and (c) establishing group keys among devices with identical inter-event timings through a partitioned group password-authenticated key exchange scheme. We evaluate IoTCupid in smart home and office environments with 11 heterogeneous devices and show that it effectively pairs all devices with only 2 group keys with a minimal pairing overhead.

Smart homes contain diverse sensors and actuators controlled by IoT apps that provide custom automation. Prior works showed that an adversary could exploit physical interaction vulnerabilities among apps and put the users and environment at risk, e.g., to break into a house, an adversary turns on the heater to trigger an app that opens windows when the temperature exceeds a threshold. Currently, the safe behavior of physical interactions relies on either app code analysis or dynamic analysis of device states with manually derived policies by developers. However, existing works fail to achieve sufficient breadth and fidelity to translate the app code into their physical behavior or provide incomplete security policies, causing poor accuracy and false alarms.

Automated program repair (APR) methods aim to identify patches for a given bug and apply them with minimal human intervention. To date, existing APR approaches focus on repairing software bugs, such as memory safety bugs. However, our analysis of popular robotic vehicle (RV) control software shows that most of their bugs are not memory bugs but rather logic bugs. These bugs, while not causing software crashes, can cause an RV to reach an undesired physical state (e.g., hitting the ground). To fix these logic bugs, we introduce PGPatch, a policy-guided program repair framework for RV control programs, which identifies the correct patch for a given logic bug and applies it without human intervention. PGPatch takes, as input, existing or new logic formulas used to discover logic bugs. It then leverages the formulas using a dedicated dynamic analysis to classify the previously known logic bugs into a patch type. It next uses a customized algorithm, based on the identified patch type and violated formula, to produce a source code patch as output. Lastly, it creates repeatable tests to verify the patch’s completeness, ensuring that the patch is correct and does not degrade the RV’s performance. We evaluate PGPatch on selected bug cases from three popular RV control software and find that it correctly fixes 258 out of 297 logic bugs (86.9%). We additionally recruit 18 experienced RV developers and users and conduct a user study that demonstrates how using PGPatch makes fixing bugs in RV software significantly quicker and less error-prone.

Digital signatures are basic cryptographic tools to provide authentication and integrity in the emerging ubiquitous systems in which resource-constrained devices are expected to operate securely and efficiently. However, existing digital signatures might not be fully practical for such resource-constrained devices (e.g., medical implants) that have energy limitations. Some other computationally efficient alternatives (e.g., one-time/multiple-time signatures) may introduce high memory and/or communication overhead due to large private key and signature sizes. In this paper, our contributions are two-fold: First, we develop a new lightweight multiple-time digital signature scheme called Signer Efficient Multiple-time Elliptic Curve Signature ($\mathtt {SEMECS}{}$), which is suitable for resource-constrained embedded devices.$\mathtt {SEMECS}{}$achieves optimal signature and private key sizes for an EC-based signaturewithoutrequiring any EC operation (e.g., EC scalar multiplication or addition) at the signer. We prove$\mathtt {SEMECS}{}$is secure (in random oracle model) with a tight security reduction. Second, we fully implemented$\mathtt {SEMECS}{}$on 8-bit AVR microprocessor with a comprehensive energy consumption analysis and comparison. Our experiments confirm up to 19× less battery-consumption for$\mathtt {SEMECS}{}$as compared to its fastest (full-time) counterpart, SchnorrQ, while offering significant performance advantages over its multiple-time counterparts in various fronts. We open-source our implementation for public testing and adoption.

Public key Encryption with Keyword Search (PEKS) aims in mitigating the impacts of data privacy versus utilization dilemma by allowing any user in the system to send encrypted files to the server to be searched by a receiver. The receiver can retrieve the encrypted files containing specific keywords by providing the corresponding trapdoors of these keywords to the server. Despite their merits, the existing PEKS schemes introduce a high end-to-end delay that may hinder their adoption in practice. Moreover, they do not scale well for large security parameters and provide no post-quantum security promises. In this paper, we propose two novel lattice-based PEKS schemes that offer a high computational efficiency along with better security assurances than that of the existing alternatives. Specifically, our NTRU-PEKS scheme achieves 18 times lower end-to-end delay than the most efficient pairing-based alternatives. Our LWE-PEKS offers provable security in the standard model with a reduction to the worst-case lattice problems. We fully implemented our NTRU-PEKS scheme and benchmarked its performance as deployed on Amazon Web Services cloud infrastructures.

Efficient authentication is vital for IoT applications with stringent minimum-delay requirements (e.g., energy delivery systems). This requirement becomes even more crucial when the IoT devices are battery-powered, like small aerial drones, and the efficiency of authentication directly translates to more operation time. Although some fast authentication techniques have been proposed, some of them might not fully meet the needs of the emerging delay-aware IoT. In this paper, we propose a new signature scheme called ARIS that pushes the limits of the existing digital signatures, wherein a commodity hardware can verify 83,333 signatures per second. ARIS also enables the fastest signature generation along with the lowest energy consumption and end-to-end delay among its counterparts. These significant computational advantages come with a larger storage requirement, which is a favorable trade-off for some critical delay-aware applications. These desirable features are achieved by harnessing message encoding with cover-free families and a special elliptic curve based oneway function. We prove the security of ARIS under the hardness of the elliptic curve discrete logarithm problem in the random oracle model. We provide an open-sourced implementation of ARIS on commodity hardware and 8-bit AVR microcontroller for public testing and verification.

Abstract The ability to query and update over encrypted data is an essential feature to enable breach-resilient cyber-infrastructures. Statistical attacks on searchable encryption (SE) have demonstrated the importance of sealing information leaks in access patterns. In response to such attacks, the community has proposed the Oblivious Random Access Machine (ORAM). However, due to the logarithmic communication overhead of ORAM, the composition of ORAM and SE is known to be costly in the conventional client-server model, which poses a critical barrier toward its practical adaptations. In this paper, we propose a novel hardware-supported privacy-enhancing platform called Practical Oblivious Search and Update Platform (POSUP), which enables oblivious keyword search and update operations on large datasets with high efficiency. We harness Intel SGX to realize efficient oblivious data structures for oblivious search/update purposes. We implemented POSUP and evaluated its performance on a Wikipedia dataset containing ≥229 keyword-file pairs. Our implementation is highly efficient, taking only 1 ms to access a 3 KB block with Circuit-ORAM. Our experiments have shown that POSUP offers up to 70× less end-to-end delay with 100× reduced network bandwidth consumption compared with the traditional ORAM-SE composition without secure hardware. POSUP is also at least 4.5× faster for up to 99.5% of keywords that can be searched compared with state-of-the-art Intel SGX-assisted search platforms.

We introduce a simple, yet efficient digital signature scheme which offers post-quantum security promise. Our scheme, named TACHYON, is based on a novel approach for extending one-time hash-based signatures to (polynomially bounded) many-time signatures, using the additively homomorphic properties of generalized compact knapsack functions. Our design permits TACHYON~to achieve several key properties. First, its signing and verification algorithms are the fastest among its current counterparts with a higher level of security. This allows TACHYON~to achieve the lowest end-to-end delay among its counterparts, while also making it suitable for resource-limited signers. Second, its private keys can be as small as κ bits, where κ is the desired security level. Third, unlike most of its lattice-based counterparts, TACHYON~does not require any Gaussian sampling during signing, and therefore, is free from side-channel attacks targeting this process. We also explore various speed and storage trade-offs for TACHYON, thanks to its highly tunable parameters. Some of these trade-offs can speed up TACHYON signing in exchange for larger keys, thereby permitting TACHYON~to further improve its end-to-end delay.

Dynamic Searchable Symmetric Encryption (DSSE) allows to delegate keyword search and file update over an encrypted database via encrypted indexes, and therefore provides opportunities to mitigate the data privacy and utilization dilemma in cloud storage platforms. Despite its merits, recent works have shown that efficient DSSE schemes are vulnerable to statistical attacks due to the lack of forward-privacy, whereas forward-private DSSE schemes suffers from practicality concerns as a result of their extreme computation overhead. Due to significant practical impacts of statistical attacks, there is a critical need for new DSSE schemes that can achieve the forward-privacy in a more practical and efficient manner. We propose a new DSSE scheme that we refer to as Forward-private Sublinear DSSE (FS-DSSE). FS-DSSE harnesses special secure update strategies and a novel caching strategy to reduce the computation cost of repeated queries. Therefore, it achieves forward-privacy, sublinear search complexity, low end-to-end delay, and parallelization capability simultaneously. We fully implemented our proposed method and evaluated its performance on a real cloud platform. Our experimental evaluation results showed that the proposed scheme is highly secure and highly efficient compared with state-of-the-art DSSE techniques. Specifically, FS-DSSE is up to three magnitude of times faster than forward-secure DSSE counterparts, depending on the frequency of the searched keyword in the database.