The growing complexity and diversity of network traffic have made the detection of previously unseen cyberattacks a critical challenge. While supervised learning models perform well on known threats, they often fail to generalise to novel attack types. In earlier work, we introduced the Unknown Network Attack Detector (UNAD), an unsupervised ensemble-based framework trained exclusively on benign traffic to detect anomalies. This paper presents an enhanced version of UNAD, referred to as UNAD+, which incorporates three key improvements. First, a Weighted Majority Voting (WMV) mechanism replaces majority voting to prioritise stronger detectors and eliminate ambiguous predictions. Second, a supervised refinement stage is introduced, where pseudo-labelled anomalies are used to train a secondary classifier that improves detection accuracy and reduces false positives. Third, a post-hoc explainability layer is added, combining LIME and surrogate tree modelling to provide both local and global interpretability of the system’s decisions. Evaluations on CICIDS2017 and NSL-KDD show that UNAD+ substantially improves detection performance compared with the original UNAD baseline, achieving an F1-score of up to 98.25
Deepfake technology poses significant threats to digital security and media integrity, necessitating robust detection methods. This study introduces a novel approach for enhancing deepfake detection by leveraging unique techniques for analyzing audio data from multiple benchmark datasets. By extracting audio features from Mel, Delta Mel, and Delta-Delta Mel spectrograms, applying image processing techniques to these features, and employing Siamese networks, our method demonstrated unprecedented performances with FakeAVCeleb and MLAAD datasets. Through extensive experimentation, our approach achieved near-perfect accuracy in both validation and testing phases among these datasets. This significant improvement in accuracy, particularly through the focused analysis of audio characteristics, offers a promising direction for developing more robust and resilient deepfake detection systems. These findings highlight the potential of our technique as a promising solution for combating the malicious use of deepfake technology and provides a strong foundation for future research in multimedia forensics and cybersecurity.
Low- or unskilled labour is prevalent in the logistics sector, which simultaneously faces an acute shortage of skilled workers. As part of the RessourcE joint research project, we explore how to identify and promote the potential of employees in such roles. We present a concept for an AI-based tool that analyses competency profiles to predict employee development potential. This tool aims to support companies in recognizing individuals suited for broader responsibilities or career advancement. We also address implementation challenges and propose evidence-based solutions derived from a needs and requirements analysis. Our findings are grounded in early-stage experiments using synthetic human resource (HR) datasets and large language models.
Synthetic aperture radar (SAR) change detection identifies differences between co-registered images acquired at different times. While SAR is highly suitable for disaster monitoring due to its all-weather and day-night capability, severe speckle complicates the detection task. We present a hybrid pipeline comprising edge-preserving denoising, fusion of three standard difference maps into a unified indicator, two-cluster Fuzzy C-Means to obtain high-confidence change and unchanged seeds, and a compact three-level Mini-U-Net trained with weighted Binary Cross-Entropy and Dice loss to classify uncertain pixels through sliding-window inference. Across three SAR datasets, the proposed method surpasses recent baselines in overall accuracy, F1 score and intersection over union, achieving effective speckle suppression and accurate change delineation.
Autonomous agents offer a promising approach to managing complex, resource-intensive operations such as vehicle routing, scheduling, and offshore decommissioning, where fleets must coordinate interdependent tasks across geographical locations under environmental and logistical constraints. This paper presents a simulation–optimization framework in which autonomous agents make local task selections using meta-heuristic-weighted decision criteria. These criteria, along with schedules and task queue sizes, are co-evolved by a Genetic Algorithm to minimize KPIs. We applied our approach to a North Sea decommissioning case study involving pipeline and structure removal, achieving up to 36
