IEEE Transactions on Dependable and Secure Computing

Wireless charging is becoming an essential power supply pattern for electronic devices. Currently, mainstream smartphones are almost compatible with wireless charging. However, when the charging efficiency is continuously improved, its security challenge ...

Smart contract transactions are increasingly interleaved by cross-contract calls. While many tools have been developed to identify a common set of vulnerabilities, the cross-contract vulnerability is overlooked by existing tools. Cross-contract ...

In recent works, robust networks have consistently exhibited more discriminative saliency map that proves to indicate sufficient adversarial robustness. In existed safe training paradigms e.g., adversarial training, however, the progressive saliency ...

Machine learning and its promising branch deep learning have proven to be effective in a wide range of application domains. Recently, several efforts have shown success in applying deep learning techniques for automatic vulnerability discovery, as ...

Class incremental learning from a pre-trained DNN model is gaining lots of popularity. Unfortunately, the pre-trained model also introduces a new attack vector, which enables an adversary to inject a backdoor into it and further compromise the downstream ...

Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting spectrum sensing data falsification (SSDF) attacks. However, the amount of data needed to train models and the scenario privacy concerns ...

Voltage stability assessment is essential for maintaining reliable power grid operations. Stability assessment approaches using deep learning address the shortfalls of the traditional time-domain simulation-based approaches caused by increased system ...

Artificial intelligence (AI)-based cybersecurity services offer significant promise in many scenarios, including malware detection, content supervision, and so on. Meanwhile, many commercial and government applications have raised the need for ...

Falsified data from compromised Phasor Measurement Units (PMUs) in a smart grid induce Energy Management Systems (EMS) to have an inaccurate estimation of the state of the grid, disrupting various operations of the power grid. Moreover, the PMUs deployed ...

Data poisoning can be served as an effective way to protect the dataset from surrogate training, whereby the performance of the surrogate model could be greatly influenced if trained with poisoned dataset. This paper focuses on an advanced scenario where ...

CAPTCHAs (completely automated public Turing tests to tell computers and humans apart) have been the main protection against malicious attacks on public systems for many years. Audio CAPTCHAs, as one of the most important CAPTCHA forms, provide an ...

Version Control System (VCS) plays an essential role in software supply chain, as it manages code projects and enables efficient collaboration. For a private repository, where source code is a high-profile asset and needs to be protected, VCS’ ...

As a promising service, Machine Learning as a Service (MLaaS) provides personalized inference functions for clients through paid APIs. Nevertheless, it is vulnerable to model extraction attacks, in which an attacker can extract a functionally-equivalent ...

Boolean-logic driven Markov processes (BDMPs) is a prominent dynamic extension of static fault trees to model repairable and complex dynamic systems. While BDMPs are intensively used in an industrial context for dependability analysis of energy systems, ...

Blockchain is extensively applied to many fields as an immutable distributed ledger. However, the immutability contradicts regulations such as the GDPR ruling “the right to be forgotten” of data. Besides, numerous emerging blockchain-based ...

Automatic modulation classification (AMC) has been envisioned as a significant element for security issues at the physical layer due to its indispensable role in accurate communications. Recent attention to deep learning has impacted the AMC, which ...

Dynamic searchable symmetric encryption (DSSE) allows efficient searches over encrypted databases and also supports clients in their updating of the data, such as those stored in a remote cloud server. However, recent attacks suggest the risk of leakage ...

Cooperative jamming (CJ) is a promising technique for enhancing the physical-layer security in wireless powered sensor networks. The secrecy performance of CJ-aided wireless powered sensor networks is affected by three issues including disguised ...

In the context of consortium blockchain, consensus protocols set permission mechanisms to maintain a relatively fixed group of participants. They can easily use distributed consistent algorithms for achieving deterministic and efficient consensus and ...

Mobile crowdsensing (MCS) has rapidly emerged as a popular paradigm for sensory data collection and benefited various location-based services and applications like road monitoring, smart transportation, and environmental monitoring. In practice, there ...

Multi-access Edge Computing (MEC) is an emerging computing model that provides the necessary on-demand resources and services to the edge of the network, ensuring powerful computing, storage capacity, mobility, location, and context awareness support to ...

The objective of this article is real-time detection of a change-point in the baseline distribution of the frequency signal generated by Phasor Measurement Units (PMUs) that could indicate potential for voltage collapse, false data injection, or other ...

Authentication and blacklisting mechanisms have a key role for service providers to deliver the service to correct users through digital channels. Nevertheless, there always have been concerns about privacy of the users against such mechanisms. The <...

Network embedding represents network nodes by a low-dimensional informative vector. While it is generally effective for various downstream tasks, it may leak some private information of networks, such as hidden private links. In this work, we address a ...

The emergence of cloud computing enables various healthcare institutions to outsource pre-diagnostic models and provide timely and convenient services for patients. However, healthcare institutions and patients have serious concerns about potential ...

The robustness of deep neural networks (DNNs) against adversarial example attacks has raised wide attention. For smoothed classifiers, we propose the worst-case adversarial loss over input distributions as a robustness certificate. Compared with previous ...

Federated learning (FL) trains a model over multiple datasets by collecting the local models rather than raw data, which can help facilitate distributed data analysis in many real-world applications. Since the model parameters can leak information about ...

While vulnerability databases are important sources of information for software security, it is known that information in these databases is inconsistent. How to rectify these incorrect data is a challenging issue. In this article, we employ developer ...

Machine Learning (ML) techniques can facilitate the automation of <underline>mal</underline>icious soft<underline>ware</underline> (malware for short) detection, but suffer from evasion attacks. Many studies counter such attacks in heuristic manners, ...

Outsourcing the heavy decryption computation to a cloud service provider has been a promising solution for a resource-constrained mobile device to deploy an attribute-based encryption scheme. However, the current attribute based encryption with outsourced ...