Federated Learning (FL) enables multiple users to collaboratively train a machine learning model without sharing raw data, making it suitable for privacy-sensitive applications. However, local model or weight updates can still leak sensitive information. Secure aggregation protocols mitigate this risk by ensuring that only the aggregated updates are revealed. Among these, single-setup secure aggregation protocols, where key generation and exchange occur only once, are the most efficient due to reduced communication and computation overhead. However, existing single-setup secure aggregation protocols often lack support for dynamic user participation and do not provide strong privacy guarantees such as forward and backward secrecy.

Detecting lateral movement threats in large-scale system logs is a critical challenge due to the scarcity of labelled attack data, the presence of imbalanced datasets, and the sophisticated nature of modern adversaries. To address these issues, we propose ConceptUML , a semantic-driven, fully unsupervised threat detection framework designed to automatically identify anomalies related to lateral movement in heterogeneous log data. ConceptUML is structured around a three-phase architecture. In Phase 1 (Latent Semantic Learning) , contextualized embeddings generated by Sentence-BERT are combined with Non-negative Matrix Factorization to extract abstract concepts from system logs and external threat intelligence sources such as MITRE ATT&CK and CAPEC. In Phase 2 (Unsupervised Threat Detection) , a Hidden Markov Model is applied to cluster logs based on learned concepts, and each cluster is scored according to its semantic similarity to known adversarial techniques. Phase 3 (Decision Refinement) uses topic modelling to further isolate malicious event log subsets from within suspicious clusters, enabling high-precision triage. We evaluate ConceptUML using four real-world event log datasets, including Windows Event Logs and multiple subsets of the LMD-23 dataset, encompassing attacks such as exploitation of hashing techniques and remote services. The enhanced model with topic modelling achieves up to 92.54% detection quality and reduces detection error to as low as 8.14%, outperforming several baseline approaches including AutoEncoder, LogAnomaly, LOF, and DBScan. Our results confirm that ConceptUML delivers interpretable, scalable, and highly effective detection of lateral movement threats without requiring labelled training data or extensive manual feature engineering.

The digitisation of agriculture, integral to Agriculture 4.0, has brought significant benefits while simultaneously escalating cybersecurity risks. With the rapid adoption of smart farming technologies and infrastructure, the agricultural sector has become an attractive target for cyberattacks. This paper presents a systematic literature review that assesses the applicability of existing cyber threat intelligence (CTI) techniques within smart farming infrastructures (SFIs). We develop a comprehensive taxonomy of CTI techniques and sources, specifically tailored to the SFI context, addressing the unique cyber threat challenges in this domain. A crucial finding of our review is the identified need for a virtual Chief Information Security Officer (vCISO) in smart agriculture. While the concept of a vCISO is not yet established in the agricultural sector, our study highlights its potential significance. The implementation of a vCISO could play a pivotal role in enhancing cybersecurity measures by offering strategic guidance, developing robust security protocols, and facilitating real-time threat analysis and response strategies. This approach is critical for safeguarding the food supply chain against the evolving landscape of cyber threats. Our research underscores the importance of integrating a vCISO framework into smart farming practices as a vital step towards strengthening cybersecurity. This is essential for protecting the agriculture sector in the era of digital transformation, ensuring the resilience and sustainability of the food supply chain against emerging cyber risks.

Proactive identification and the management of disruption risks play a crucial role in the achievement of a global supply chain’s aims. Given the velocity and volume by which such disruption events occur, it is impractical to expect supply chain managers to determine the occurrence of such events manually. Given the pressures facing global supply chains due to the COVID-19 crisis, it is important for supply chain managers to proactively identify disruption risks to their supply chains and manage them to either achieve the outcomes or develop plans by which resilience against them can be built. In this paper, we demonstrate how the integration of natural language processing and reinforcement learning, which are fundamental artificial intelligence methods, can be used to assist supply chain risk managers in the timely identification of such disruption events. We explain in detail our proposed approach, namely RL-SCRI and show its superiority over the current models in achieving its aim.

Understanding the attributes of critical data and implementing suitable security measures help organisations bolster their data-protection strategies and diminish the potential impacts of ransomware incidents. Unauthorised extraction and acquisition of data are the principal objectives of most cyber invasions. We underscore the severity of this issue using a recent attack by the Clop ransomware group, which exploited the MOVEit Transfer vulnerability and bypassed network-detection mechanisms to exfiltrate data via a Command and Control server. As a countermeasure, we propose a method called Buffer-Based Signature Verification (BBSV). This approach involves embedding 32-byte tags into files prior to their storage in the cloud, thus offering enhanced data protection. The BBSV method can be integrated into software like MOVEit Secure Managed File Transfer, thereby thwarting attempts by ransomware to exfiltrate data. Empirically tested using a BBSV prototype, our approach was able to successfully halt the encryption process for 80 ransomware instances from 70 ransomware families. BBSV not only stops the encryption but also prevents data exfiltration when data are moved or written from the original location by adversaries. We further develop a hypothetical exploit scenario in which an adversary manages to bypass the BBSV, illicitly transmits data to a Command and Control server, and then removes files from the original location. We construct an extended state space, in which each state represents a tuple that integrates user authentication and system components at the filesystem level.

In the rapidly changing cybersecurity landscape, threat hunting has become a critical proactive defense against sophisticated cyber threats. While traditional security measures are essential, their reactive nature often falls short in countering malicious actors’ increasingly advanced tactics. This paper explores the crucial role of threat hunting, a systematic, analyst-driven process aimed at uncovering hidden threats lurking within an organization's digital infrastructure before they escalate into major incidents. Despite its importance, the cybersecurity community grapples with several challenges, including the lack of standardized methodologies, the need for specialized expertise, and the integration of cutting-edge technologies like artificial intelligence (AI) for predictive threat identification. To tackle these challenges, this survey paper offers a comprehensive overview of current threat hunting practices, emphasizing the integration of AI-driven models for proactive threat prediction. Our research explores critical questions regarding the effectiveness of various threat hunting processes and the incorporation of advanced techniques such as augmented methodologies and machine learning. Our approach involves a systematic review of existing practices, including frameworks from industry leaders like IBM and CrowdStrike. We also explore resources for intelligence ontologies and automation tools. The background section clarifies the distinction between threat hunting and anomaly detection, emphasizing systematic processes crucial for effective threat hunting. We formulate hypotheses based on hidden states and observations, examine the interplay between anomaly detection and threat hunting, and introduce iterative detection methodologies and playbooks for enhanced threat detection. Our review encompasses supervised and unsupervised machine learning approaches, reasoning techniques, graph-based and rule-based methods, as well as other innovative strategies. We identify key challenges in the field, including the scarcity of labeled data, imbalanced datasets, the need for integrating multiple data sources, the rapid evolution of adversarial techniques, and the limited availability of human expertise and data intelligence. The discussion highlights the transformative impact of artificial intelligence on both threat hunting and cybercrime, reinforcing the importance of robust hypothesis development. This paper contributes a detailed analysis of the current state and future directions of threat hunting, offering actionable insights for researchers and practitioners to enhance threat detection and mitigation strategies in the ever-evolving cybersecurity landscape.

Recommender systems have seen wide adoption in different domains. The motive of such systems has evolved from providing generic recommendations in the past to providing customized and user-focused recommendations. To achieve this aim, the complexity and sophistication of the underlying techniques such systems use have evolved. Current recommender systems use advanced Artificial Intelligence techniques to provide intelligent recommendations and adapt their future workings to the user’s interest and requirements. One such technique currently being used in the literature to achieve this aim is Reinforcement Learning. However, a drawback of this technique is that it is data intensive and needs to be trained on data that represent different scenarios to ensure that the recommended output in a given scenario is accurate. In this article, we present an approach, namely Reinforcement Learning-based News Recommendation System (RL-NRS), to address this drawback in the domain of news recommendation. We explain the different stages of RL-NRS in detail and compare its performance with news articles recommended by Google for a particular search term.