arXiv subjects

Risto Vaarandi

Publications and source records attributed to Risto Vaarandi.

3 recordsLinked to original sources

arXiv2025

Evaluating explainable AI for deep learning-based network intrusion detection system alert classification

A Network Intrusion Detection System (NIDS) monitors networks for cyber attacks and other unwanted activities. However, NIDS solutions often generate an overwhelming number of alerts daily, making it challenging for analysts to prioritize high-priority threats. While deep learning models promise to automate the prioritization of NIDS alerts, the lack of transparency in these models can undermine trust in their decision-making. This study highlights the critical need for explainable artificial intelligence (XAI) in NIDS alert classification to improve trust and interpretability. We employed a real-world NIDS alert dataset from Security Operations Center (SOC) of TalTech (Tallinn University Of Technology) in Estonia, developing a Long Short-Term Memory (LSTM) model to prioritize alerts. To explain the LSTM model's alert prioritization decisions, we implemented and compared four XAI methods: Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), Integrated Gradients, and DeepLIFT. The quality of these XAI methods was assessed using a comprehensive framework that evaluated faithfulness, complexity, robustness, and reliability. Our results demonstrate that DeepLIFT consistently outperformed the other XAI methods, providing explanations with high faithfulness, low complexity, robust performance, and strong reliability. In collaboration with SOC analysts, we identified key features essential for effective alert classification. The strong alignment between these analyst-identified features and those obtained by the XAI methods validates their effectiveness and enhances the practical applicability of our approach.

cs.CR↗

arXiv2025

A Systematic Literature Review of Cyber Security Monitoring in Maritime

In recent years, many cyber incidents have occurred in the maritime sector, targeting the information technology (IT) and operational technology (OT) infrastructure. One of the key approaches for handling cyber incidents is cyber security monitoring, which aims at timely detection of cyber attacks with automated methods. Although several literature review papers have been published in the field of maritime cyber security, none of the previous studies has focused on cyber security monitoring. The current paper addresses this research gap and surveys the methods, algorithms, tools and architectures used for cyber security monitoring in the maritime sector. For the survey, a systematic literature review of cyber security monitoring studies is conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) protocol. The first contribution of this paper is the bibliometric analysis of related literature and the identification of the main research themes in previous works. For that purpose, the paper presents a taxonomy for existing studies which highlights the main properties of maritime cyber security monitoring research. The second contribution of this paper is an in-depth analysis of previous works and the identification of research gaps and limitations in existing literature. The gaps and limitations include several dataset and evaluation issues and a number of understudied research topics. Based on these findings, the paper outlines future research directions for cyber security monitoring in the maritime field.

cs.CR↗

arXiv2024

Using Large Language Models for Template Detection from Security Event Logs

In modern IT systems and computer networks, real-time and offline event log analysis is a crucial part of cyber security monitoring. In particular, event log analysis techniques are essential for the timely detection of cyber attacks and for assisting security experts with the analysis of past security incidents. The detection of line patterns or templates from unstructured textual event logs has been identified as an important task of event log analysis since detected templates represent event types in the event log and prepare the logs for downstream online or offline security monitoring tasks. During the last two decades, a number of template mining algorithms have been proposed. However, many proposed algorithms rely on traditional data mining techniques, and the usage of Large Language Models (LLMs) has received less attention so far. Also, most approaches that harness LLMs are supervised, and unsupervised LLM-based template mining remains an understudied area. The current paper addresses this research gap and investigates the application of LLMs for unsupervised detection of templates from unstructured security event logs.

cs.CR↗