Secure firmware update is an important stage in the Internet of Things (IoT) device life-cycle. Prior techniques, designed for other computational settings, are not readily suitable for IoT devices, since they do not consider idiosyncrasies of a realistic large-scale IoT deployment. This motivates our design of architecture for secure software update of realistic embedded devices ( ASSURED ), a secure and scalable update framework for IoT. ASSURED includes all stakeholders in a typical IoT update ecosystem, while providing end-to-end security between manufacturers and devices. To demonstrate its feasibility and practicality, ASSURED is instantiated and experimentally evaluated on two commodity hardware platforms. Results show that ASSURED is considerably faster than current update mechanisms in realistic settings.

Quantum computing poses a significant global threat to modern security mechanisms. As such, security experts and public sectors have issued guidelines to help organizations transition their software to post-quantum cryptography (PQC). However, there is a lack of (semi-)automatic tools to support this transition, particularly for software deployed as binary executables. To address this gap, in this work, we first propose a set of requirements necessary for this type of tool to detect quantum-vulnerable software executables. Following these requirements, we introduce QED: a toolchain for Quantum-vulnerable Executable Detection. QED uses a three-phase approach to identify quantum-vulnerable dependencies in a given set of executables, from file-level to API-level, and finally, precise identification of a static trace that triggers a quantum-vulnerable API. The key benefit of this design is that it provides efficiency without compromising accuracy, as it incorporates fast initial analyses to filter out executables unlikely to be quantum-vulnerable that in turn allows the more resource-intensive analysis to be performed on a smaller subset of executables. To demonstrate this claim, we evaluate QED on both a synthetic dataset with four cryptography libraries and a real-world dataset with over 200 software executables. The results show that: (1) QED discerns quantum-vulnerable from quantum-safe executables with 100% accuracy in the synthetic dataset; (2) QED is practical and scalable, completing analyses on average in less than 4 seconds per real-world executable; and (3) QED reduces the manual workload required by analysts to identify quantum-vulnerable executables in the real-world dataset by more than 90%. We believe QED can become an essential tool to facilitate the transition to PQC, particularly for small and medium-sized businesses with limited resources.

The rise in IoT-driven distributed data analytics, coupled with increasing privacy concerns, has led to a demand for effective privacy-preserving and federated data collection/model training mechanisms. In response, approaches such as Federated Learning (FL) and Local Differential Privacy (LDP) have been proposed and attracted much attention over the past few years. However, they still share the common limitation of being vulnerable to poisoning attacks wherein adversaries compromising edge devices feed forged (a.k.a. “poisoned”) data to aggregation back-ends, undermining the integrity of FL/LDP results. In this work, we propose a system-level approach to remedy this issue based on a novel security notion of Proofs of Stateful Execution (PoSX) for IoT/embedded devices’ software. To realize the PoSX concept, we design SLAPP: a System-Level Approach for Poisoning Prevention. SLAPP leverages commodity security features of embedded devices – in particular ARM TrustZone-M security extensions – to verifiably bind raw sensed data to their correct usage as part of FL/LDP edge device routines. As a consequence, it offers robust security guarantees against poisoning. Our evaluation, based on real-world prototypes featuring multiple cryptographic primitives and data collection schemes, showcases SLAPP’s security and low overhead.

Anomaly detection has emerged as a popular technique for detecting malicious activities in local area networks (LANs). Various aspects of LAN anomaly detection have been widely studied. Nonetheless, the privacy concern about individual users or their relationship in LAN has not been thoroughly explored in the prior work. In some realistic cases, the anomaly detection analysis needs to be carried out by an external party, located outside the LAN. Thus, it is important for the LAN admin to release LAN data to this party in a private way in order to protect privacy of LAN users; at the same time, the released data must also preserve the utility of being able to detect anomalies. This paper investigates the possibility of privately releasing ARP data that can later be used to identify anomalies in LAN. We present four approaches, namely, naïve, histogram-based, naïve-, and histogram-based- and show that they satisfy different levels of differential privacy—a rigorous and provable notion for quantifying privacy loss in a system. Our real-world experimental results confirm practical feasibility of our approaches. With a proper privacy budget, all of our approaches preserve more than 75% utility of detecting anomalies in the released data.

Implementation flaws in cryptographic libraries, design flaws in underlying cryptographic primitives, and weaknesses in protocols using both, can all lead to exploitable vulnerabilities in software. Manually fixing such issues is challenging and resource consuming, especially when maintaining legacy software that contains broken or outdated cryptography, and for which source code may not be available. While there is existing work on identifying cryptographic primitives (often in the context of malware analysis), none of this prior work has focused on replacing such primitives with stronger (or more secure ones) after they have been identified. This paper explores feasibility of designing and implementing a toolchain for Augmentation and Legacy-software Instrumentation of Cryptographic Executables (ALICE). The key features of ALICE are: (i) automatically detecting and extracting implementations of weak or broken cryptographic primitives from binaries without requiring source code or debugging symbols, (ii) identifying the context and scope in which such primitives are used, and performing program analysis to determine the effects of replacing such implementations with more secure ones, and (iii) replacing implementations of weak primitives with those of stronger or more secure ones. We demonstrate practical feasibility of our approach on cryptographic hash functions with several popular cryptographic libraries and real-world programs of various levels of complexity. Our experimental results show that ALICE can locate and replace insecure hash functions, even in large binaries (we tested ones of size up to 1.5MB), while preserving existing functionality of the original binaries, and while incurring minimal execution-time overhead in the rewritten binaries. We also open source ALICE's code at https://github.com/SRI-CSL/ALICE.