相关论文: Dishonest Approximate Computing: A Coming Crisis f…
We study the worst-case behavior of Block Coordinate Descent (BCD) type algorithms for unconstrained minimization of coordinate-wise smooth convex functions. This behavior is indeed not completely understood, and the practical success of…
High-performance and safety-critical system architects must accurately evaluate the application-level silent data corruption (SDC) rates of processors to soft errors. Such an evaluation requires error propagation all the way from particle…
The power of fuzz testing lies in its random, often brute-force, generation and execution of inputs to trigger unexpected behaviors and vulnerabilities in software applications. However, given the reality of infinite possible input…
Coded distributed computing (CDC) is a new technique proposed with the purpose of decreasing the intense data exchange required for parallelizing distributed computing systems. Under the famous MapReduce paradigm, this coded approach has…
We propose a novel approach to approximate Bayesian computation (ABC) that seeks to cater for possible misspecification of the assumed model. This new approach can be equally applied to rejection-based ABC and to popular regression…
Cybercriminals have been exploiting cryptocurrencies to commit various unique financial frauds. Covert cryptomining - which is defined as an unauthorized harnessing of victims' computational resources to mine cryptocurrencies - is one of…
Unlike fixed-gain robust control, which trades off performance with modeling uncertainty, direct adaptive control uses partial modeling information for online tuning. The present paper combines retrospective cost adaptive control (RCAC), a…
Cloud Computing is a set of IT Services that are provided to a customer over a network and these services are delivered by third party provider who owns the infrastructure and reduce the burden at user's end. Nowadays researchers devoted…
Distributed Denial of Service attacks have become a significant threat to industries and governments leading to substantial financial losses. With the growing reliance on internet services, DDoS attacks can disrupt services by overwhelming…
The ability to enforce robust and dynamic access controls on cloud-hosted data while simultaneously ensuring confidentiality with respect to the cloud itself is a clear goal for many users and organizations. To this end, there has been much…
The security of AI-generated content (AIGC) detection is crucial for ensuring multimedia content credibility. To enhance detector security, research on adversarial attacks has become essential. However, most existing adversarial attacks…
Cloud computing environments are increasingly vulnerable to security threats such as distributed denial-of-service (DDoS) attacks and SQL injection. Traditional security mechanisms, based on rule matching and feature recognition, struggle…
This study critically examines the methodological rigor in credit card fraud detection research, revealing how fundamental evaluation flaws can overshadow algorithmic sophistication. Through deliberate experimentation with improper…
Out-of-distribution (OOD) detection remains a fundamental challenge for deep neural networks, particularly due to overconfident predictions on unseen OOD samples during testing. We reveal a key insight: OOD samples predicted as the same…
Traditional CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) schemes are increasingly vulnerable to automated attacks powered by deep neural networks (DNNs). Existing adversarial attack methods often rely…
An Air Force evaluation of Multics, and Ken Thompson's Turing award lecture ("Reflections on Trusting Trust"), showed that compilers can be subverted to insert malicious Trojan horses into critical software, including themselves. If this…
Identifying the failure modes of cloud computing systems is a difficult and time-consuming task, due to the growing complexity of such systems, and the large volume and noisiness of failure data. This paper presents a novel approach for…
The front end bottleneck in datacenter workloads has come under increased scrutiny, with the growing code footprint, involvement of numerous libraries and OS services, and the unpredictability in the instruction stream. Our examination of…
Nowadays, the number of emerging embedded systems rapidly grows in many application domains, due to recent advances in artificial intelligence and internet of things. The main inherent specification of these application-specific systems is…
Large optimization problems with hard constraints arise in many settings, yet classical solvers are often prohibitively slow, motivating the use of deep networks as cheap "approximate solvers." Unfortunately, naive deep learning approaches…