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WebAssembly (WASM) has emerged as a crucial technology in smart contract development for several blockchain platforms. Unfortunately, since their introduction, WASM smart contracts have been subject to several security incidents caused by contract vulnerabilities, resulting in substantial economic losses. However, existing tools for detecting WASM contract vulnerabilities have accuracy limitations, one of the main reasons being the coarse-grained emulation of the on-chain data APIs. In this paper, we introduce WACANA, an analyzer for WASM contracts that accurately detects vulnerabilities through fine-grained emulation of on-chain data APIs. WACANA precisely simulates both the structure of on-chain data tables and their corresponding API functions, and integrates concrete and symbolic execution within a coverage-guided loop to balance accuracy and efficiency. Evaluations on a vulnerability dataset of 133 contracts show WACANA outperforming state-of-the-art tools in accuracy. Further validation on 5,602 real-world contracts confirms WACANA's practical effectiveness.

Some smart contracts violate decentralization principles by defining privileged accounts that manage other users' assets without permission, introducing centralization risks that have caused financial losses. Existing methods, however, face challenges in accurately detecting diverse centralization risks due to their dependence on predefined behavior patterns. In this paper, we propose JANUS, an automated analyzer for Solidity smart contracts that detects financial centralization risks independently of their specific behaviors. JANUS identifies differences between states reached by privileged and ordinary accounts, and analyzes whether these differences are finance-related. Focusing on the impact of risks rather than behaviors, JANUS achieves improved accuracy compared to existing tools and can uncover centralization risks with unknown patterns. To evaluate JANUS's performance, we compare it with other tools using a dataset of 540 contracts. Our evaluation demonstrates that JANUS outperforms representative tools in terms of detection accuracy for financial centralization risks . Additionally, we evaluate JANUS on a real-world dataset of 33,151 contracts, successfully identifying two types of risks that other tools fail to detect. We also prove that the state traversal method and variable summaries, which are used in JANUS to reduce the number of states to be compared, do not introduce false alarms or omissions in detection.

Deep learning has emerged as a promising technology for achieving Android malware detection. To further unleash its detection potentials, software visualization can be integrated for analyzing the details of app behaviors clearly. However, facing increasingly sophisticated malware, existing visualization-based methods, analyzing from one or randomly-selected few views, can only detect limited attack types. We propose and implement LensDroid, a novel technique that detects Android malware by visualizing app behaviors from multiple complementary views. Our goal is to harness the power of combining deep learning and software visualization to automatically capture and aggregate high-level features that are not inherently linked, thereby revealing hidden maliciousness of Android app behaviors. To thoroughly comprehend the details of apps, we visualize app behaviors from three related but distinct views of behavioral sensitivities, operational contexts and supported environments. We then extract high-order semantics based on the views accordingly. To exploit semantic complementarity of the views, we design a deep neural network based model for fusing the visualized features from local to global based on their contributions to downstream tasks. A comprehensive comparison with five baseline techniques is performed on datasets of more than 51K apps in three real-world typical scenarios, including overall threats, app evolution and zero-day malware. The experimental results show that the overall performance of LensDroid is better than the baseline techniques. We also validate the complementarity of the views and demonstrate that the multi-view fusion in LensDroid enhances Android malware detection., Comment: Submitted to TIFS

Several software mitigations have been proposed to defend against Spectre vulnerabilities. However, these countermeasures often suffer from high performance overhead, largely due to unnecessary protections. We propose LightSLH, designed to mitigate this overhead by hardening instructions only when they are under threat from Spectre vulnerabilities. LightSLH leverages program analysis techniques based on abstract interpretation to identify all instructions that could potentially lead to Spectre vulnerabilities and provides provable protection. To enhance analysis efficiency and precision, LightSLH employs novel taint and value domains. The taint domain enables bit-level taint tracking, while the value domain allows LightSLH to analyze complex program structures such as pointers and structures. Furthermore, LightSLH uses a two-stage abstract interpretation approach to circumvent potential analysis paralysis issues. We demonstrate the security guarantees of LightSLH and evaluate its performance on cryptographic algorithm implementations from OpenSSL. LightSLH significantly reduces the overhead associated with speculative-load-hardening techniques. Our results show that LightSLH introduces no protection and thus no overhead on 4 out of the 7 studied algorithms, which contrasts with existing countermeasures that introduce additional overhead due to unnecessary hardening. Additionally, LightSLH performs, for the first time, a rigorous analysis of the security guarantees of RSA against Spectre v1, highlighting that the memory access patterns generated by the scatter-gather algorithm depend on secrets, even for observers at the cache line granularity, necessitating protection for such accesses.

At present, millions of Ethereum smart contracts are created per year and attract financially motivated attackers. However, existing analyzers do not meet the need to precisely analyze the financial security of large numbers of contracts. In this paper, we propose and implement FASVERIF, an automated analyzer for fine-grained analysis of smart contracts' financial security. On the one hand, FASVERIF automatically generates models to be verified against security properties of smart contracts. On the other hand, our analyzer automatically generates the security properties, which is different from existing formal verifiers for smart contracts. As a result, FASVERIF can automatically process source code of smart contracts, and uses formal methods whenever possible to simultaneously maximize its accuracy. We evaluate FASVERIF on a vulnerabilities dataset by comparing it with other automatic tools. Our evaluation shows that FASVERIF greatly outperforms the representative tools using different technologies, with respect to accuracy and coverage of types of vulnerabilities.

Group oriented applications are getting more and more popular in mobile Internet and call for secure and efficient secret sharing (SS) scheme to meet their requirements. A $(t,n)$ threshold SS scheme divides a secret into $n$ shares such that any $t$ or more than $t$ shares can recover the secret while less than $t$ shares cannot. However, an adversary, even without a valid share, may obtain the secret by impersonating a shareholder to recover the secret with $t$ or more legal shareholders. Therefore, this paper uses linear code to propose a threshold changeable secret sharing (TCSS) scheme, in which threshold should increase from $t$ to the exact number of all participants during secret reconstruction. The scheme does not depend on any computational assumption and realizes asymptotically perfect security. Furthermore, based on the proposed TCSS scheme, a group authentication scheme is constructed, which allows a group user to authenticate whether all users are legal group members at once and thus provides efficient and flexible m-to-m authentication for group oriented applications.

Android users are now suffering severe threats from unwanted behaviors of various apps. The analysis of apps' audit logs is one of the essential methods for some device manufacturers to unveil the underlying malice within apps. We propose and implement AppAngio, a novel system that reveals contextual information in Android app behaviors by API-level audit logs. Our goal is to help analysts of device manufactures understand what has happened on users' devices and facilitate the identification of the malice within apps. The key module of AppAngio is identifying the path matched with the logs on the app's control-flow graph (CFG). The challenge, however, is that the limited-quantity logs may incur high computational complexity in the log matching, where there are a large number of candidates caused by the coupling relation of successive logs. To address the challenge, we propose a divide and conquer strategy that precisely positions the nodes matched with log records on the corresponding CFGs and connects the nodes with as few backtracks as possible. Our experiments show that AppAngio reveals the contextual information of behaviors in real-world apps. Moreover, the revealed results assist the analysts in identifying malice of app behaviors and complement existing analysis schemes. Meanwhile, AppAngio incurs negligible performance overhead on the Android device., Comment: Accepted by TIFS

Current formal approaches have been successfully used to find design flaws in many security protocols. However, it is still challenging to automatically analyze protocols due to their large or infinite state spaces. In this paper, we propose a novel framework that can automatically verifying security protocols without any human intervention. Experimental results show that SmartVerif automatically verifies security protocols that cannot be automatically verified by existing approaches. The case study also validates the effectiveness of our dynamic strategy., Comment: arXiv admin note: text overlap with arXiv:1403.1142, arXiv:1703.00426 by other authors

Quantum secret sharing (QSS) schemes without entanglement have huge advantages in scalability and are easier to realize as they only require sequential communications of a single quantum system. However, these schemes often come with drawbacks such as exact ($n, n$) structure, security flaws and absences of effective cheating detections. To address these problems, we propose a verifiable framework by utilizing entanglement-free states to construct ($t, n$)-QSS schemes. Our work is the heuristic step towards information-theoretical security in entanglement-free QSS, and it sheds light on how to establish effective verification mechanism against cheating. As a result, the proposed framework has a significant importance in constructing QSS schemes for versatile applications in quantum networks due to its intrinsic scalability, flexibility and information theoretical security.