Large Vision-Language Models (LVLMs) are vulnerable to a growing array of multimodal jailbreak attacks, necessitating defenses that are both generalizable to novel threats and efficient for practical deployment.Many current strategies fall short, either targeting specific attack patterns, which limits generalization, or imposing high computational overhead.While lightweight anomaly-detection methods offer a promising direction, we find that their common one-class design tends to confuse unseen benign inputs with malicious ones, leading to unreliable over-rejection.To address this, we propose Representational Contrastive Scoring (RCS), a framework built on a key insight: the most potent safety signals reside within the LVLM's own internal representations.Our approach inspects the internal geometry of these representations, learning a lightweight projection to maximally separate benign and malicious inputs in safety-critical layers.This enables a simple yet powerful contrastive score that differentiates true malicious intent from mere distribution shift.Our instantiations, MCD (Mahalanobis Contrastive Detection) and KCD (K-nearest Contrastive Detection), achieve state-of-the-art performance on a challenging evaluation protocol designed to test generalization to unseen attack types.This work demonstrates that effective jailbreak detection can be achieved by applying simple, interpretable statistical methods to the internal representations, offering a practical path towards safer LVLM deployment.Our code is available on Github 1 .

Anomaly-Based Intrusion Detection Systems (IDSs) have been extensively researched for their ability to detect zero-day attacks. These systems establish a baseline of normal behavior using benign traffic data and flag deviations from this norm as potential threats. They generally experience higher false alarm rates than signature-based IDSs. Unlike image data, where the observed features provide immediate utility, raw network traffic necessitates additional processing for effective detection. It is challenging to learn useful patterns directly from raw traffic data or simple traffic statistics (e.g., connection duration, package inter-arrival time) as the complex relationships are difficult to distinguish. Therefore, some feature engineering becomes imperative to extract and transform raw data into new feature representations that can directly improve the detection capability and reduce the false positive rate. We propose a geometric feature learning method to optimize the feature extraction process. We employ contrastive feature learning to learn a feature space where normal traffic instances reside in a compact cluster. We further utilize H-Score feature learning to maximize the compactness of the cluster representing the normal behavior, enhancing the subsequent anomaly detection performance. Our evaluations using the NSL-KDD and N-BaloT datasets demonstrate that the proposed IDS powered by feature learning can consistently outperform state-of-the-art anomaly-based IDS methods by significantly lowering the false positive rate. Furthermore, we deploy the proposed IDS on a Raspberry Pi 4 and demonstrate its applicability on resource-constrained Internet of Things (IoT) devices, highlighting its versatility for diverse application scenarios.

Federated learning, while being a promising approach for collaborative model training, is susceptible to backdoor attacks due to its decentralized nature. Backdoor attacks have shown remarkable stealthiness, as they compromise model predictions only when inputs contain specific triggers. As a countermeasure, anomaly detection is widely used to filter out backdoor attacks in FL. However, the non-independent and identically distributed (non-IID) data distribution nature of FL clients presents substantial challenges in backdoor attack detection, as the data variety introduces variance among benign models, making them indistinguishable from malicious ones. In this work, we propose a novel distribution-aware backdoor detection mechanism, BoBa, to address this problem. To differentiate outliers arising from data variety versus backdoor attacks, we propose to break down the problem into two steps: clustering clients utilizing their data distribution, and followed by a voting-based detection. We propose a novel data distribution inference mechanism for accurate data distribution estimation. To improve detection robustness, we introduce an overlapping clustering method, where each client is associated with multiple clusters, ensuring that the trustworthiness of a model update is assessed collectively by multiple clusters rather than a single cluster. Through extensive evaluations, we demonstrate that BoBa can reduce the attack success rate to lower than 0.001 while maintaining high main task accuracy across various attack strategies and experimental settings.

Over the last decade, Internet of Things (IoT) has permeated our daily life with a broad range of applications. However, a lack of adequate security in IoT devices renders IoT systems vulnerable to various network-based cyberattacks, potentially causing severe damage. Recent works have explored using machine learning to build anomaly detection models for defending against such attacks. In this paper, we propose FeCo, a federated-contrastive-learning framework that coordinates in-network IoT devices to jointly learn intrusion detection models. FeCo utilizes federated learning to alleviate users’ privacy concerns as participating devices only submit their model parameters rather than raw local data. Compared to previous works, we develop a novel representation learning method based on contrastive learning that is able to learn a more accurate model for the benign class. FeCo significantly improves the intrusion detection accuracy compared to previous works. In addition, we implement a two-step feature selection scheme to avoid overfitting and reduce computation time. Through extensive experiments on the NSL-KDD dataset and the BaIoT dataset, we demonstrate that FeCo achieves as high as 8% accuracy improvement compared to the state-of-the-art and is robust to non-independent and identically distributed (non-IID) data. Our implementation of FeCo on a Raspberry Pi device further confirms the applicability of FeCo for resource-constrained IoT devices.

To facilitate dynamic spectrum sharing, the FCC has designated certified SAS administrators to implement their own spectrum access systems (SASs) that manage the shared spectrum usage in the novel CBRS band. As a premise, different SAS servers must conduct periodic inter-SAS coordination to synchronize service states and avoid allocation conflicts. However, SAS servers may inevitably stop service for regular upgrades, crash down, or even perform maliciously that deviate from the normal routines, posing a fundamental operation security problem --- the system shall be robust against these faults to guarantee secure and efficient spectrum sharing service. Unfortunately, the incumbent inter-SAS coordination mechanism, CPAS, is prone to SAS failures and does not support real-time allocation. Recent proposals that rely on blockchain smart contracts or state machine replication mechanisms to realize fault-tolerant inter-SAS coordination require all SASs to follow a unified allocation algorithm. They however face performance bottlenecks and cannot accommodate the current fact that different SASs hold their own proprietary allocation algorithms.

Time-sensitive applications, such as 5G and IoT, are imposing increasingly stringent security and reliability requirements on network time synchronization. Precision time protocol (PTP) is a de facto solution to achieve high precision time synchronization. It is widely adopted by many industries. Existing efforts in securing the PTP focus on the protection of communication channels, but little attention has been given to the threat of malicious insiders. In this paper, we first present the security vulnerabilities of PTP and discuss why the current defense mechanisms are unable to counter Byzantine insiders. We demonstrate how a malicious insider can spoof a time source to arbitrarily shift the system time of a victim node on an IoT testbed. We further demonstrate the harmful consequence of the attack on a real Turtlebot3 robotic platform as the robot fails to locate itself and follows a false trajectory. As a countermeasure, we propose multi-source PTP, in short, MS-PTP, a Byzantine-resilient network time synchronization mechanism that relies on time crowdsourcing. MS-PTP changes the current PTP's single source hierarchy to a multi-source client-server architecture, in which PTP clients take responses from multiple time servers and apply a novel secure aggregation scheme to eliminate the effect of malicious responses from unreliable sources. MS-PTP is able to counter f Byzantine failures when the total number of time sources n used by a client satisfies n>=3f+1. We provide rigorous proof for its non-parametric accuracy guarantee---achieving bounded error regardless of the Byzantine population. We implemented a prototype of MS-PTP on our IoT testbed and the results show its resilience against Byzantine insiders while maintaining high synchronization accuracy.

The spectrum access system (SAS) is being deployed as a key component of the emerging spectrum sharing paradigm to address the spectrum crunch facing the U.S. wireless industry. Ensuring security and privacy of this system against potential attacks is a task of paramount importance. In this article, we first introduce the SAS system, describing its three-tier access model, its functional architecture, and the spectrum management protocol. We then provide a comprehensive analysis of a variety of security and privacy attacks that an SAS is vulnerable to, and discuss their countermeasures. We identify key challenges, formalize threat models, and organize the discussion of SAS security into four categories: 1) SAS server security and privacy; 2) citizens broadband radio service device security; 3) security of environment sensing capability; and 4) communication protocol security. Finally, we suggest future research directions for spectrum management security.

Automotive communication networks, represented by the CAN bus, are acclaimed for enabling real-time communication between vehicular ECUs but also criticized for their lack of effective security mechanisms. Various attacks have demonstrated that this security deficit renders a vehicle vulnerable to adversarial control that jeopardizes passenger safety. A recent standardization effort led by AUTOSAR has provided general guidelines for developing next-generation automotive communication technologies with built-in security mechanisms. A key security mechanism is message authentication between ECUs for countering message spoofing and replay attack. While many message authentication schemes have been proposed by previous work, the important issue of session key establishment with AUTOSAR compliance was not well addressed. In this paper, we fill this gap by proposing an AUTOSAR-compliant key management architecture that takes into account practical requirements imposed by the automotive environment. Based on this architecture, we describe a baseline session key distribution protocol called SKDC that realizes all designed security functionalities, and propose a novel secret-sharing-based protocol called SSKT that yields improved communication efficiency. Both SKDC and SSKT are customized for CAN/CAN-FD bus deployment. We implemented the two protocols on commercial microcontroller boards and evaluated their performance with hardware experiment and extrapolation analysis. The result shows while both protocols are performant, SSKT achieves superior computation and communication efficiency at scale.