Multivariate Time Series (MTS) anomaly detection is critical for various applications. Yet, most existing methods rely on forecasting or reconstruction-based frameworks, which suffer from inherent uncertainties in handling complex MTS data. The recently emerging imputation-based anomaly detection leverages partial observations to reconstruct the masked values, thereby enhancing reconstruction stability and detection performance. However, in existing imputation-based methods, training on the masked data may lead to the omission on the global spatiotemporal distributional features of the MTS. Additionally, multiple masking strategies must be employed to ensure all data points have the opportunity to be reconstructed, resulting in repeated training and significant computational consumption. To address these limitations, this paper proposes ImConDM, a novel anomaly detection framework integrating imputation with conditional diffusion models. It trains a diffusion model on the complete training data and leverages the controllable sampling capabilities of diffusion models to perform conditional imputation guided by observed values, enabling stable and comprehensive MTS feature learning without retraining. Extensive experiments on six real-world MTS datasets demonstrate that ImConDM significantly outperforms eleven state-of-the-art baselines. Notably, compared with the second-best model, it achieves a 20.5% improvement in detection accuracy on high-dimensional datasets and reduces training time by at least 58%. The executable codes of the experiments with datasets and our algorithms are available at https://github.com/Labman-Wangjunjie/ImConDM.

This paper presents Hydra, a dynamic BFT protocol that allows replicas to join and leave the system dynamically. It addresses the limitations of traditional static BFTs in managing membership changes and can be used to simplify the implementation of many features in modern blockchain applications. Hydra relies on weaker assumptions to achieve standard properties compared to the existing solution Dyno and introduces a configuration auto-transition protocol to ensure liveness. Through temporary configurations and explicitly defined replica responsibilities for request handling, Hydra pipelines membership requests alongside regular requests and realizes clarity, achieving a more efficient and smoother configuration transitions. It also employs a non-blocking configuration discovery mechanism, enabling new replicas to participate in consensus quickly. We formally prove Hydra's correctness under the dynamic BFT model. Experimental results demonstrate Hydra's ability to maintain throughput fluctuations within 5% during various replica join and leave scenarios, outperforming Dyno and existing BFT system supporting reconfiguration in both stability and efficiency. Hydra effectively manages scenarios that Dyno circumvents with stronger assumptions and quickly restores throughput to normal levels.

Wildcard Keyword Searchable Encryption (WKSE) has grown into a ubiquitous tool. It enables clients to search desired files with wildcard expressions. Although promising, previous schemes confront three barriers: (1) An adversary can launch a correlation attack to acquire the similarity between keywords. (2) The WKSE schemes exhibit false positives which can lead to wrong search results. (3) Existing feature extraction strategies limit the flexibility of search expressions. In this paper, we propose a Multi-Character Searchable Encryption scheme (MCSE) that overcomes the aforementioned barriers. To resist correlation attacks, we design the randomize pad model to encrypt the vector. To eradicate false positives, we apply the vector space model and complete feature extraction strategies so that a feature set uniquely identifies a keyword or expression. To enhance search flexibility, we introduce three distinct feature extraction strategies for keyword expressions, wildcard expressions, and logical expressions, enabling effective multi-character search. These strategies enable indexes to accom modate the search of diverse expressions. Finally, we prove that MCSE is indistinguishable against chosen-feature attacks and implement MCSE on two real datasets. Compared with state-of the-art schemes, the experiment results show that MCSE achieves good performance.

Federated Learning is an approach that enables multiple devices to collectively train a shared model without sharing raw data, thereby preserving data privacy. However, federated learning systems are vulnerable to data-poisoning attacks during the training and updating stages. Three data-poisoning attacks-label flipping, feature poisoning, and VagueGAN-are tested on FL models across one out of ten clients using the CIC and UNSW datasets. For label flipping, we randomly modify labels of benign data; for feature poisoning, we alter highly influential features identified by the Random Forest technique; and for VagueGAN, we generate adversarial examples using Generative Adversarial Networks. Adversarial samples constitute a small portion of each dataset. In this study, we vary the percentages by which adversaries can modify datasets to observe their impact on the Client and Server sides. Experimental findings indicate that label flipping and VagueGAN attacks do not significantly affect server accuracy, as they are easily detectable by the Server. In contrast, feature poisoning attacks subtly undermine model performance while maintaining high accuracy and attack success rates, highlighting their subtlety and effectiveness. Therefore, feature poisoning attacks manipulate the server without causing a significant decrease in model accuracy, underscoring the vulnerability of federated learning systems to such sophisticated attacks. To mitigate these vulnerabilities, we explore a recent defensive approach known as Random Deep Feature Selection, which randomizes server features with varying sizes (e.g., 50 and 400) during training. This strategy has proven highly effective in minimizing the impact of such attacks, particularly on feature poisoning.

Over the past three decades, standardizing organizations (e.g., the National Institute of Standards and Technology and Internet Engineering Task Force) have investigated the efficiency of cryptographic algorithms and provided (technical) guidelines for practitioners. For example, the (Datagram) Transport Layer Security “(D)TLS” 1.2/1.3 was designed to help industries implement and integrate such methods through underpinning infrastructures of Internet of Everything (IoE) environments with efficiency and efficacy in mind. The main goal underpinning such protocols is to protect the Internet connections between IoE machines from malicious activities such as unauthorized eavesdropping, monitoring, and tampering with messages. In theory, these protocols are supposed to be secure. Still, most existing implementations partially follow the standard features of (D)TLS 1.2/3, leaving them vulnerable to risks such as side-channel and network attacks. In this paper, we critically review the standard protocols deployed for the security management of data and connected machines, and also examine the recently discovered vulnerabilities that lead to successful zero-day attacks in IoE environments. Then, we discuss various potential countermeasures in the form of organizational policy enforcement strategies and mitigation approaches that can be used by cybersecurity practitioners, decision- and policy-makers. Finally, we identify both proactive and reactive solutions for further consideration and study, as well as propose alternative mechanisms and e-governance policies for standardizing organizations and engineers in future solution designs.

Leader-based BFT systems face potential disruption and performance degradation from malicious leaders, with current solutions often lacking scalability or greatly increasing complexity. In this paper, we introduce ABSE, an Adaptive Baseline Score-based Election approach to mitigate the negative impact of malicious leaders on leader-based BFT systems. ABSE is fully localized and proposes to accumulate scores for processes based on their contribution to consensus advancement, aiming to bypass less reliable participants when electing leaders. We present a formal treatment of ABSE, addressing the primary design and implementation challenges, defining its generic components and rules for adherence to ensure global consistency. We also apply ABSE to two different BFT protocols, demonstrating its scalability and negligible impact on protocol complexity. Finally, by building a system prototype and conducting experiments on it, we demonstrate that ABSE-enhanced protocols can effectively minimize the disruptions caused by malicious leaders, whilst incurring minimal additional resource overhead and maintaining base performance.

The Internet of Medical Things (IoMT) is getting extreme attraction as it motivates unprecedented growth in the healthcare industry. Security breaches in IoMT can lead to threatening patients’ lives. For IoMT, existing medical remote attestation techniques (EMRATs) have limitations such as neglecting operational symptoms of compromised systems, like inconsistent medical sensor readings. Moreover, EMRATs do not enable medical-forensic-based attestation history and are inefficient for mutual attestation between a doctor network and a sensor network monitoring a patient. This mutual attestation guarantees safe remote surgeries. In this paper for IoMT, we present a novel remote attestation protocol, BDMFA (Blockchain-supported and Deep learning Medical Forensic-enabling Attestation), to overcome the limitations of EMRATs. BDMFA utilizes deep learning and Blockchain to learn from sensor readings and store attestation history. We prove that BDMFA is resilient to a higher number of attacks than that resisted by EMRATs. Moreover, we present a proof-of-concept implementation for BDMFA using SMART (Secure and Minimal Architecture for Root of Trust). We proved the practical feasibility of BDMFA by implementing it using Omnetpp equipped with Castalia. For a system with 50 patient-sensors and 25 doctor-terminals, BDMFA needed only 2.6 s to complete attestation and less communication cost than that needed for related state-of-the-art protocols by 28.4%. For larger systems, we carried comparative analysis confirming that our proposed protocol BDMFA requires less cost and is more scalable and efficient than related protocols.

Android operating system restricts access to data by enabling data control flow and permission systems to reduce the risk of information theft. Therefore, attackers are constantly looking for alternative and stealthy approaches to exfiltrate private data from a targeted device. This paper presents CovertPower, a covert channel attack that exfiltrates user data by actively inducing power consumption on Android devices. At the transmitting end, our CovertPower app modulates binary data into a timed resource workload (e.g., processor, write-on-memory), producing power consumption bursts. On the receiving end, we acquire power consumption traces via a low-cost hardware tool that can be easily concealed in USB wall-socket adapters or powerbanks. Therefore, a signal processing-based decoder analyzes such traces and retrieves the exfiltrated information. We demonstrate the feasibility of our attack with a thorough experimental evaluation on 14 mobile devices and various real-world settings such as display state, ongoing activities, and charging technologies. Our attack achieves a transfer speed of up to 10bps with a high bit sequence similarity on most devices and settings considered.

Membership Inference Attacks (MIAs) infer whether a data point is in the training data of a machine learning model, posing privacy risks to sensitive data like medical records or financial data. Intuitively, data points that MIA accurately detects are vulnerable. Those data points may exist in the data of different target models, each susceptible to multiple MIAs. As such, the vulnerability of data points under multiple MIAs and target models represents a significant challenge. This article defines several metrics reflecting data points’ vulnerability and capturing vulnerable data points under multiple MIAs and target models. We implement 77 MIAs, with an average attack accuracy over target models ranging from 0.5 to 0.9, to support our analysis with our scalable and flexible platform, Various Membership Inference Attacks Platform (VMIAP). Based on the results, we observe that MIA has an inference tendency to some data points despite a low overall inference performance. Furthermore, previous approaches are unsuitable for finding vulnerable data points under multiple MIAs and target models. Finally, we explore the impact of retraining target, shadow, and attack models separately on the vulnerability of data points.

We consider the problem of publicly verifiable privacy-preserving data aggregation in the presence of a malicious aggregator colluding with malicious users. State-of-the-art solutions either split the aggregator into two parties under the assumption that they do not collude, or require many rounds of interactivity and have non-constant verification time. In this work, we propose mPVAS, the first publicly verifiable privacy-preserving data aggregation protocol that allows arbitrary collusion, without relying on trusted third parties during execution, where verification runs in constant time. We also show three extensions to mPVAS: mPVAS+, for improved communication complexity, mPVAS-IV, for the identification of malicious users, and mPVAS-UD, for graceful handling of reduced user availability without the need to redo the setup. We show that our schemes achieve the desired confidentiality, integrity, and authenticity. Finally, through both theoretical and experimental evaluations, we show that our schemes are feasible for real-world applications.

The growing integration of vehicles with external networks has led to a surge in attacks targeting their Controller Area Network (CAN) internal bus. As a countermeasure, various Intrusion Detection Systems (IDSs) have been suggested in the literature to prevent and mitigate these threats. With the increasing volume of data facilitated by the integration of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication networks, most of these systems rely on data-driven approaches such as Machine Learning (ML) and Deep Learning (DL) models. However, these systems are susceptible to adversarial evasion attacks. While many researchers have explored this vulnerability, their studies often involve unrealistic assumptions, lack consideration for a realistic threat model, and fail to provide effective solutions. In this paper, we present CANEDERLI (CAN Evasion Detection ResiLIence), a novel framework for securing CAN-based IDSs. Our system considers a realistic threat model and addresses the impact of adversarial attacks on DL-based detection systems. Our findings highlight strong transferability properties among diverse attack methodologies by considering multiple state-of-the-art attacks and model architectures. We analyze the impact of adversarial training in addressing this threat and propose an adaptive online adversarial training technique outclassing traditional fine-tuning methodologies with F1 scores up to 0.941. By making our framework publicly available, we aid practitioners and researchers in assessing the resilience of IDSs to a varied adversarial landscape.

Smart Parking Services (SPSs) enable cruising drivers to find the nearest parking lot with available spots, reducing the traveling time, gas, and traffic congestion. However, drivers risk the exposure of sensitive location data during parking query to an untrusted Smart Parking Service Provider (SPSP). Our motivation arises from a repetitive query to an updated database, i.e., how a driver can be repetitively paired with a previously-matched-but-forgotten lot. Meanwhile, we aim to achieve repetitive query in an oblivious and unlinkable manner. In this work, we present Mnemosyne2 : decentralized and privacy-preserving smart parking with secure repetition and full verifiability. Specifically, we design repetitive, oblivious, and unlinkable Secure k Nearest Neighbor (SkNN) with basic verifiability (correctness and completeness) for encrypted-andupdated databases. We build a local Ethereum blockchain to perform driver-lot matching via smart contracts. To adapt to the lot count update, we resort to the immutable blockchain for advanced verifiability (truthfulness). Last, we utilize decentralized blacklistable anonymous credentials to guarantee identity privacy. Finally, we formally define and prove privacy and security. We conduct extensive experiments over a real-world dataset and compare Mnemosyne2 with existing work. The results show that a query only needs 8 seconds (175 ms) on average for service waiting (verification) among 500 drivers.

The smart grid represents a pivotal innovation in modernizing the electricity sector, offering an intelligent, digitalized energy network capable of optimizing energy delivery from source to consumer. It hence represents the backbone of the energy sector of a nation. Due to its central role, the availability of the smart grid is paramount and is hence necessary to have in-depth control of its operations and safety. To this aim, researchers developed multiple solutions to assess the smart grid’s stability and guarantee that it operates in a safe state. Artificial intelligence and Machine learning algorithms have proven to be effective measures to accurately predict the smart grid’s stability. Despite the presence of known adversarial attacks and potential solutions, currently, there exists no standardized measure to protect smart grids against this threat, leaving them open to new adversarial attacks. In this paper, we propose GAN-GRID a novel adversarial attack targeting the stability prediction system of a smart grid tailored to real-world constraints. Our findings reveal that an adversary armed solely with the stability model’s output, devoid of data or model knowledge, can craft data classified as stable with an Attack Success Rate (ASR) of 0.99. Also by manipulating authentic data and sensor values, the attacker can amplify grid issues, potentially undetected due to a compromised stability prediction system. These results underscore the imperative of fortifying smart grid security mechanisms against adversarial manipulation to uphold system stability and reliability.

Threshold signature is a powerful cryptographic technique with a large number of real-life applications. As designed by Boneh and Komlo (CRYPTO’22), TAPS is a new threshold signature integrating privacy and accountability. It allows a combiner to combine t signature shares while protecting t and the signing group from the public. It also enables a tracer to trace a threshold signature to its original signing group. Despite being valuable, TAPS neglects the witnessing of tracing, i.e., leaves the tracing activity unrestrained. In this paper, we introduce Accountable and Private Threshold Signature with Hidden Witnesses (HiTAPS) that not only provides privacy and accountability, but also incorporates witnessed tracing. In specific, we first utilize Dynamic Threshold Public-Key Encryption (DTPKE) and ElGamal encryption to designate a set of t^′ witnesses for endorsing the tracing activity. We then compute a keyed-hash tag for the t^′ witnesses to initiate the tracing activity secretly. Moreover, we present an optimized protocol HiTAPS2 to reduce communication overhead of the combiner. We formalize the definitions, security, and privacy for HiTAPS. We formally prove its security and privacy. To evaluate the performance of HiTAPS and HiTAPS2, we build a prototype based on pypbc. Experimental results show that HiTAPS takes 217(370) ms to combine (track) a threshold signature of 5 signers (witnesses). The optimized HiTAPS2 only takes 137 ms to combine a threshold signature of 5 signers.

The Internet of Things rapid growth poses privacy and security challenges for the traditional key storage methods. Physical Unclonable Functions offer a potential solution but require secure fuzzy extractors to ensure reliable replication. This paper introduces X-Lock, a novel and secure computational fuzzy extractor that addresses the limitations faced by traditional solutions in resource-constrained IoT devices. X-Lock offers a reusable and robust solution, effectively mitigating the impacts of bias and correlation through its design. Leveraging the preferred state of a noisy source, X-Lock encrypts a random string of bits that can be later used as seed to generate multiple secret keys. To prove our claims, we provide a comprehensive theoretical analysis, addressing security considerations, and implement the proposed model. To evaluate the effectiveness and superiority of our proposal, we also provide practical experiments and compare the results with existing approaches. The experimental findings demonstrate the efficacy of our algorithm, showing comparable memory cost (≈2.4 KB for storing 5 keys of 128 bits) while being 3 orders of magnitude faster with respect to the state-of-the-art solution (0.086 ms against 15.51 s).