Related papers: TAC: Hybrid IAM Privilege Escalation Detection

In recent years, cloud storage technology has been widely used in many fields such as education, business, medical and more because of its convenience and low cost. With the widespread applications of cloud storage technology, data access…

Retrieval-Augmented Generation (RAG) systems introduce a critical vulnerability: contextual leakage, where adversaries exploit instruction-following to exfiltrate Personally Identifiable Information (PII) via adaptive extraction. Current…

The growing availability of hardware-based trusted execution environments (TEEs) in commodity processors has recently advanced support (i.e., design, implementation and deployment frameworks) for network-based secure services. Examples of…

Machine learning models have been shown to leak sensitive information about their training datasets. Models are increasingly deployed on devices, raising concerns that white-box access to the model parameters increases the attack surface…

Cybersecurity vulnerability information is often recorded by multiple channels, including government vulnerability repositories, individual-maintained vulnerability-gathering platforms, or vulnerability-disclosure email lists and forums.…

Securing Dew-Enabled Edge-of-Things (EoT) networks against sophisticated intrusions is a critical challenge. This paper presents HybridGuard, a framework that integrates machine learning and deep learning to improve intrusion detection.…

Measures of power grid vulnerability are often assessed by the amount of damage an adversary can exact on the network. However, the cascading impact of such attacks is often overlooked, even though cascades are one of the primary causes of…

Emerging deployments of Generative AI increasingly execute inference across decentralized and heterogeneous edge devices rather than on a single trusted server. In such environments, a single device failure or misbehavior can disrupt the…

In this paper, we focus on addressing the challenges of detecting malicious attacks in networks by designing an advanced Explainable Intrusion Detection System (xIDS). The existing machine learning and deep learning approaches have…

We consider collaborative inference at the wireless edge, where each client's model is trained independently on its local dataset. Clients are queried in parallel to make an accurate decision collaboratively. In addition to maximizing the…

Hierarchical federated learning (HFL) leverages edge servers for partial aggregation in edge computing. Yet existing FL methods lack mechanisms for jointly optimizing cluster assignment and client selection under data heterogeneity. This…

Cyberattacks have grown into a major risk for organizations, with common consequences being data theft, sabotage, and extortion. Since preventive measures do not suffice to repel attacks, timely detection of successful intruders is crucial…

Serverless computing has achieved widespread adoption, with over 70% of AWS organizations using serverless solutions [1]. Meanwhile, machine learning inference workloads increasingly migrate to Function-as-a-Service (FaaS) platforms for…

Recently, the IaaS (Infrastructure as a Service) Cloud (e.g., Amazon EC2) has been widely used by many organizations. However, some IaaS security issues create serious threats to its users. A typical issue is the timing channel. This kind…

Scientific workflows have become highly heterogenous, leveraging distributed facilities such as High Performance Computing (HPC), Artificial Intelligence (AI), Machine Learning (ML), scientific instruments (data-driven pipelines) and edge…

With the prevalence of Internet of Things (IoT) applications, IoT devices interact closely with our surrounding environments, bringing us unparalleled smartness and convenience. However, the development of secure IoT solutions is getting a…

NarrowBand-Internet of Things (NB-IoT) is a new 3GPP radio access technology designed to provide better coverage for Low Power Wide Area (LPWA) networks. To provide reliable connections with extended coverage, a repetition transmission…

Intelligent applications based on machine learning are impacting many parts of our lives. They are required to operate under rigorous practical constraints in terms of service latency, network bandwidth overheads, and also privacy. Yet…

This paper looks into the problem of detecting network anomalies by analyzing NetFlow records. While many previous works have used statistical models and machine learning techniques in a supervised way, such solutions have the limitations…

Anomalies often occur in real-world information networks/graphs, such as malevolent users, malicious comments, banned users, and fake news in social graphs. The latest graph anomaly detection methods use a novel mechanism called truncated…