Face recognition offers seamless human-machine interaction and efficiency. However, its widespread adoption has heightened security and privacy concerns due to the risks associated with compromised biometric data, such as spoofing and unauthorized tracking. To mitigate these concerns, this paper introduces a novel privacy-preserving face recognition framework that integrates an enhanced dual-path feature compression approach with homomorphic encryption (HE) for secure and efficient authentication. We leverage the robust deep neural network model FaceNet to extract discriminative 512-dimensional feature vectors and propose two significantly improved complementary feature compression methods tailored specifically for encrypted biometric systems: (1) Partitioned Principal Component Analysis (P-PCA), which employs a novel segment-wise PCA transformation, preserving localized discriminative information and supporting revocable biometric templates; and (2) Segment-wise Locality-Sensitive Hashing (S-LSH), introducing segment-specific hashing optimized for efficient binary representation and privacy-preserving encrypted-domain computations. Both compressed real-valued and binary features are securely encrypted using HE, enabling direct encrypted-domain similarity computations without exposing sensitive biometric data. Extensive experiments demonstrate that our method achieves competitive authentication performance while maintaining computational efficiency and practical feasibility.

Understanding group information and collective behaviors is an ongoing area of research, encompassing natural phenomena and human dynamics. Quantifying interactions and interdependencies at the group level can be valuable for understanding complex and dynamical systems. The Border Gateway Protocol (BGP), the default inter-domain routing protocol for the Internet, operates within a large, complex, and dynamic system vulnerable to security threats. Traditional BGP anomaly detection focuses on single observables from individual Autonomous Systems (ASes), which inadequately addresses the multidimensional, multi-viewpoint nature of the Internet and interdomain routing. This paper introduces a novel approach for quantifying group AS-level information and dynamics. We present the first ever application of Multidimensional Recurrence Quantification Analysis (MdRQA) to any computer system, offering a robust BGP anomaly detection technique that identifies anomalies earlier than traditional single-AS observable methods. This research marks a significant advancement in BGP anomaly detection, treating it as a group dynamics problem within the Internet’s complex and distributed system. • Investigation of multiple Autonomous Systems (ASes) in terms of group information and dynamics for the purposes of group-AS level BGP anomaly detection. • The first time Multidimensional Recurrence Quantification Analysis (MdRQA) has been applied to groups of computer-controlled systems. • The first time MdRQA incorporates all extant RQA metrics. • MdRQA provides more information and detects the incident earlier than the standard single observable variant and some deep learning approaches.

The Border Gateway Protocol (BGP), acting as the communication protocol that binds the Internet, remains vulnerable despite Internet security advancements. This is not surprising, as the Internet was not designed to be resilient to cyber-attacks, therefore the detection of anomalous activity was not of prime importance to the Internet creators. Detection of BGP anomalies can potentially provide network operators with an early warning system to focus on protecting networks, systems, and infrastructure from significant impact, improve security posture and resilience, while ultimately contributing to a secure global Internet environment. In this paper, we present a novel technique for the detection of BGP anomalies in different events. This research uses publicly available datasets of BGP messages collected from the repositories, Route Views and Réseaux IP Européens (RIPE). Our contribution is the application of a time series data mining approach, Matrix Profile (MP), to detect BGP anomalies in all categories of BGP events. Advantages of the MP detection technique compared to extant approaches include that it is domain agnostic, is assumption-free, requires few parameters, does not require training data, and is scalable and storage efficient. The single hyper-parameter analyzed in MP shows it is robust to change. Our results indicate the MP detection scheme is competitive against existing detection schemes. A novel BGP anomaly detection scheme is also proposed for further research and validation.

Malware detection is an important area of cyber security. Computer systems rely on malware detection applications to prevent malware attacks from succeeding. Malware detection is not a straightforward task, as new variants of malware are generated at an increasing rate. Machine learning (ML) has been utilised to generate predictive classification models to identify new malware variants which conventional malware detection methods may not detect. Machine learning, has however, been found to be vulnerable to different types of adversarial attacks, in which an attacker is able to negatively affect the classification ability of the ML model. Several defensive measures to prevent adversarial poisoning attacks have been developed, but they often rely on the use of a trusted clean dataset to help identify and remove adversarial examples from the training dataset. The defence in this paper does not require a trusted clean dataset, but instead, identifies intentional false negatives (zero day malware classified as benign) at the testing stage by examining the activation weights of the ML model. The defence was able to identify 94.07% of the successful targeted poisoning attacks.

Building automation systems, or building management systems, control services such as heating, airconditioning and security access in facilities. A common protocol used to transmit data regarding the status of components is BACnet. Unfortunately, whilst security is included in the BACnet standard, it is rarely implemented by vendors of building automation systems. This lack of attention to security can lead to vulnerabilities in the protocol being exploited with the result that the systems and the buildings they control can be compromised. This paper describes a proof-of-concept protocol attack on a BACnet system and examines the potential of modeling the basis of the attack.

The ubiquitous nature of Bluetooth technology presents opportunities for intelligence gathering based on historical and real-time device presence data. This information can be of value to law enforcement agencies, intelligence organizations, and industry. Despite the introduction of the Bluetooth Low Energy standard that incorporates anonymity preservation mechanisms, the presence of devices that support Classic Bluetooth that uses unique and persistent device identifiers is expected to remain significant for a number of years. The common approach to finding discoverable Classic Bluetooth devices relies on a standard inquiry process that is not truly passive. Furthermore, this approach fails to detect devices that remain undiscoverable. Ubertooth One, a low-cost open source Bluetooth development platform, can assist with overcoming this limitation in a truly passive manner, making it an attractive digital forensic instrument. Using vehicle-based sensors and parallel multi-method device discovery, we conduct a practical evaluation of Ubertooth One for passive discovery and contrast its discovery rate to the standard method. Based on 83 comparative field experiments, we show that Ubertooth One can produce forensically sound observations while able to discover up to ten times as many devices. We also show that this method can identify repeat device presence, as we observe 2370 instances of repeat observations on different days in single and multiple location scenarios. We conclude that this passive technique can complement the standard method and has the potential be used as a viable alternative.