Security analysts are overwhelmed by the volume of alerts and the low context provided by many detection systems. Early-stage investigations typically require manual correlation across multiple log sources, a task that is usually time-consuming. In this paper, we present an experimental, agentic workflow that leverages large language models (LLMs) augmented with predefined queries and constrained tool access (structured SQL over Suricata logs and grep-based text search) to automate the first stages of alert investigation. The proposed workflow integrates queries to provide an overview of the available data, and LLM components that selects which queries to use based on the overview results, extracts raw evidence from the query results, and delivers a final verdict of the alert. Our results demonstrate that the LLM-powered workflow can investigate log sources, plan an investigation, and produce a final verdict that has a significantly higher accuracy than a verdict produced by the same LLM without the proposed workflow. By recognizing the inherent limitations of directly applying LLMs to high-volume and unstructured data, we propose combining existing investigation practices of real-world analysts with a structured approach to leverage LLMs as virtual security analysts, thereby assisting and reducing the manual workload.

As machine intelligence evolves, the need to test and compare the problem-solving abilities of different AI models grows. However, current benchmarks are often simplistic, allowing models to perform uniformly well and making it difficult to distinguish their capabilities. Additionally, benchmarks typically rely on static question-answer pairs that the models might memorize or guess. To address these limitations, we introduce Dynamic Intelligence Assessment (DIA), a novel methodology for testing AI models using dynamic question templates and improved metrics across multiple disciplines such as mathematics, cryptography, cybersecurity, and computer science. The accompanying dataset, DIA-Bench, contains a diverse collection of challenge templates with mutable parameters presented in various formats, including text, PDFs, compiled binaries, visual puzzles, and CTF-style cybersecurity challenges. Our framework introduces four new metrics to assess a model’s reliability and confidence across multiple attempts. These metrics revealed that even simple questions are frequently answered incorrectly when posed in varying forms, highlighting significant gaps in models’ reliability. Notably, API models like GPT-4o often overestimated their mathematical capabilities, while ChatGPT-4o demonstrated better performance due to effective tool usage. In self-assessment OpenAI’s o1-mini proved to have the best judgement on what tasks it should attempt to solve. We evaluated 25 state-of-the-art LLMs using DIA-Bench, showing that current models struggle with complex tasks and often display unexpectedly low confidence, even with simpler questions. The DIA framework sets a new standard for assessing not only problem-solving, but also a model’s adaptive intelligence and ability to assess its limitations. The dataset is publicly available on the project’s page: https://github.com/DIA-Bench.

With digital technologies now being part of the fabric of our societies, identifying and managing cybersecurity threats becomes imperative. Within the European Union, several initiatives are underway, aiming to motivate, regulate and eventually orchestrate the establishment of capacity and enhancement of situational awareness, incident response, and preparedness capabilities, with an expected emphasis on operators of essential services and state actors entrusted with cybersecurity. In this context, the institution of cooperation and information exchange channels to allow for coordinated cross-border responses to large-scale incidents is particularly prioritized. Motivated by the above, this work presents a conceptual blueprint in support of architecting and establishing interoperable Cyber Security Operations Centres that combine capacity for situational awareness, incident response, and preparedness, also benefiting from the interplay between them, ultimately enhancing national cybersecurity capabilities, cross-border collaboration, and national supervision of their critical sectors, in line with current and upcoming regulatory requirements and the ever-increasing need for national and international cooperation.

Motivated by the introduction of CACAO, the first open standard that harmonizes the way we document courses of action in a machine-readable format for interoperability, and the benefits for cybersecurity operations derived from utilizing, and coupling and sharing course of action playbooks with cyber threat intelligence, we introduce a uniform metadata template that supports managing and integrating course of action playbooks into knowledge representation and knowledge management systems. We demonstrate the applicability of our approach through two use-case implementations. We utilize the playbook metadata template to introduce functionality and integrate course of action playbooks, such as CACAO, into the MISP threat intelligence platform and the OASIS Threat Actor Context ontology.

Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents such as terrorism, general criminal offences, or even domestic violence. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. In this paper we conduct a comparison between a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation. We evaluated both models from different points of view, including accuracy, computational and data complexity, flexibility and reliability. Our results show that a semantic segmentation model provides considerable amount of flexibility and resilience in the low data environment compared to classical deep model models, although its configuration and tuning presents a challenge in achieving the same levels of accuracy as an end-to-end model.

This paper presents PSO, an ontological framework and a methodology for improving physical security and insider threat detection. PSO can facilitate forensic data analysis and proactively mitigate insider threats by leveraging rule-based anomaly detection. In all too many cases, rule-based anomaly detection can detect employee deviations from organizational security policies. In addition, PSO can be considered a security provenance solution because of its ability to fully reconstruct attack patterns. Provenance graphs can be further analyzed to identify deceptive actions and overcome analytical mistakes that can result in bad decision-making, such as false attribution. Moreover, the information can be used to enrich the available intelligence (about intrusion attempts) that can form use cases to detect and remediate limitations in the system, such as loosely-coupled provenance graphs that in many cases indicate weaknesses in the physical security architecture. Ultimately, validation of the framework through use cases demonstrates and proves that PS0 can improve an organization's security posture in terms of physical security and insider threat detection.

Big data has become a great asset for many organizations, promising improved operations and new business opportunities. However, big data has increased access to sensitive information that when processed can directly jeopardize the privacy of individuals and violate data protection laws. As a consequence, data controllers and data processors may be imposed tough penalties for non-compliance that can result even to bankruptcy. In this paper, we discuss the current state of the legal regulations and analyse different data protection and privacy-preserving techniques in the context of big data analysis. In addition, we present and analyse two real-life research projects as case studies dealing with sensitive data and actions for complying with the data regulation laws. We show which types of information might become a privacy risk, the employed privacy-preserving techniques in accordance with the legal requirements, and the influence of these techniques on the data processing phase and the research results.

Blacklists and whitelists are often employed to filter outgoing and incoming traffic on computer networks. One central function of these lists is to mitigate the security risks posed by malware threats by associating a reputation (for instance benign or malicious) to end-point hosts. The creation and maintenance of these lists is a complex and time-consuming process for security experts. As a consequence, blacklists and whitelists are prone to various errors, inconsistencies and omissions, as only a tiny fraction of end-point hosts are effectively covered by the reputation lists. In this paper, we present a machine learning model that is able to automatically detect whether domain names and IP addresses are benign, malicious or sinkholes. The model relies on a deep neural architecture and is trained on a large passive DNS database. Evaluation results demonstrate the effectiveness of the approach, as the model is able to detect malicious DNS records with a F1score of 0.96. In other words, the model is able to detect 95 % of the malicious hosts with a false positive rate of 1:1000.