Cybersecurity

Abstract Collision resistance is one of the most fundamental properties in cryptography. With the development of quantum computing, significant attention has been directed toward understanding the quantum query complexity of collision-finding problems in hash functions. The quantum query complexity of collision-finding in general non-uniform random functions remained an open problem until the rec…

Abstract Distributed Denial of Service attacks (DDoS) are a common and influential network malicious behavior. The timely and accurate detection of Distributed Denial-of-Service (DDoS) attacks constitutes a critically significant research imperative in cyber security. Most current research focuses on classification based on statistical characteristics of network traffic, but less considers the si…

Abstract Cyber threat intelligence (CTI) serves as the cognitive hub for cybersecurity defense, deeply integrating the domain knowledge of security experts with the characteristics of attack behaviors. Constructing attack knowledge graphs from CTI provides critical support for attack chain reconstruction and defensive decision-making. However, traditional knowledge graph construction methods exhi…

Abstract Unmanned Aerial Systems (UAS) have become increasingly integral to industries such as agriculture, surveillance, transportation, and entertainment, raising significant concerns about their vulnerability to cyberthreats. Concurrently, cyberattacks have grown drastically in both frequency and complexity. In response, researchers have increasingly turned to Machine Learning (ML) techniques …

Abstract Training deep learning models requires substantial financial and human resources, so once deployed in untrusted environments, these models immediately attract the attention of attackers who seek to steal and misuse them. Traditional model protection methods are ineffective in addressing model accuracy, performance, and proactive defense. To this end, we present an active defensive approa…

Abstract Website fingerprinting attacks are critical for extracting website information and identifying illegal websites visited by users in anonymous networks such as Tor. However, existing attacks struggle to extract effective features from unmonitored websites due to the diversity. Although increasing unmonitored training data can improve effectiveness, it also increases attacker costs. To add…

Abstract In recent years, cloud-edge-end collaborative federated learning frameworks have been widely used in many scenarios and achieved good results. However, with the complexity of application requirements, the problems of device heterogeneity and data heterogeneity become more prominent. Traditional frameworks often face challenges such as uneven allocation of computational resources and inef…

Abstract Multi-Layer Perceptrons are widely used for Distributed Denial-of-Service detection due to their high accuracy in distinguishing malicious from benign traffic. However, their vulnerability to adversarial perturbations, subtle input modifications that evade detection, remains largely unexplored which poses significant risks for real-world deployment. This study evaluates the susceptibilit…

Abstract Microservice has become a dominant approach for building large-scale Internet applications. The microservice-based system (MS) consists of thousands of services, and its complex interactions make it highly susceptible to unforeseen cascading failures. Cascading failure models are commonly used to analyze the system’s tolerance, while the existing models overlook MS’s features and fail to…

Abstract Internet of Things (IoT) networks are increasingly targeted by advanced botnet attacks, posing serious risks to security and system stability. However, many existing intrusion detection systems (IDS) struggle to balance detection accuracy, real-time efficiency, and interpretability—especially in resource-constrained environments. In this paper, we introduce GELAX, a novel detection frame…

Abstract Recent breakthroughs in genome sequencing have revolutionized genetic diagnostics, yet secure sharing of sensitive genomic data remains a critical barrier to clinical collaboration.We address this challenge through Threshold Labeled Private Set Intersection (TLPSI), a novel cryptographic protocol without using computation-heavy homomorphic encryption. enabling confidential diagnostic lab…

Abstract Homomorphic signatures have important potential in cloud computing and data privacy protection, but there are still problems such as low signature efficiency, high overhead, and difficulty in instantiation. To solve these problems, an efficient leveled fully homomorphic signature scheme LFHSS with shortened signature values is constructed. The scheme is based on the GPV framework and the…

Abstract Internet of things (IoT) devices are widely exploited by botnets and other cyberattacks to carry out various malicious activities. Traditional detection methods focus on specific device types or attack patterns and have limited coverage. In this paper, we propose a novel detection model for compromised IoT devices based on heterogeneous information networks (HINs), which can detect compr…

Abstract Deep learning excels in detecting source code vulnerabilities, where image-based detection methods overcome ignoring deep code semantic information in token-based methods and the inefficiency of graph-based methods. Unfortunately, current image-based methods cannot sufficiently extract vulnerability-related features due to three key limitations: (1) the inappropriateness of the construct…

Abstract CAPTCHAs are widely used as authentication methods in mobile applications and web-based services to prevent AI bots from gaining unauthorized access. Early CAPTCHAs relied on image-based techniques, distorting text within images to make it difficult for bots to decipher. However, modern AI-powered optical character recognition (OCR) systems can now easily bypass these measures. To addres…

Abstract Encryption is the most direct technique to protect data confidentiality when users outsource their data to the cloud. Typically, ciphertexts are stored in the cloud, while cryptographic keys are managed by a key management server (KMS). However, this approach introduces new challenges in securely managing both keys and ciphertexts. Specifically, two crucial issues remain unresolved. One …

Abstract Cryptographically relevant quantum computers (CRQCs) would break widely deployed public-key cryptography (RSA/ECC) via Shor’s algorithm, enabling retroactive decryption of captured ciphertext (“harvest now, decrypt later”). This paper presents an enterprise-cloud transition framework that couples (i) standards-based algorithm selection using NIST’s post-quantum standards (FIPS 203–205), …

Abstract With the widespread deployment of the lightweight cryptography (LWC) standard Ascon in resource-constrained devices, research on physical attacks against Ascon, especially fault attacks, has made noticeable progress in recent years. Existing fault attacks on Ascon often require substantial fault injections. To address this, we propose scoring functions with multiple distinguishers for st…

Abstract Cyberspace Surveying and Mapping (CSM) involves the identification and analysis of digital assets to support network management and security, yet its domain-specific named entity recognition (NER) remains underexplored. A key challenge is the semantic gap between general-domain corpora and CSM domain texts, the suboptimal performance of existing named entity recognition (NER) models in a…

Abstract Smart grid (SG) facilitates our lives by providing more reliable electricity and enabling better integration of renewable energy sources. Currently, numerous authentication and key agreement (AKA) protocols have been proposed to secure SG communication. However, these solutions often result in considerable cost, making them inappropriate for resource-constrained SG environment. In this p…