With the progressing development and ubiquitousness of Artificial Intelligence (AI) observed in last decade, the need for creating methods which are explainable and/or interpretable for humans has become a pressing matter. The ability to understand how a system makes a decision is necessary to help develop trust, settle issues...More>>
Publications & Demonstrators
All accepted publications from SPARTA partners under its funding as well as videos presenting some of the work done under SPARTA
Achieving Explainability of Intrusion Detection System by Hybrid Oracle-Explainer Approach
M. Szczepanski, M. Choras, M. Pawlicki, R. Kozik.
On the Impact of Network Data Balancing in Cybersecurity Applications
Marek Pawlicki, Michał ChoraśRafał, KozikWitold Hołubowicz
Machine learning methods are now widely used to detect a wide range of cyberattacks. Nevertheless, the commonly used algorithms come with challenges of their own - one of them lies in network dataset characteristics. The dataset should be well-balanced in terms of the number of malicious data samples vs. benign...More>>
Disconnection attacks against LoRaWAN 1.0.X ABP devices
Giorgio Bernardinetti, Francesco Mancini, Giuseppe Bianchi
Previous research work has already documented vulnerabilities of LoRaWAN 1.0.x, in the form of Replay Attacks which may cause disconnection situations. To face (also) these concerns, modern network servers implement careful techniques to handle sequence numbers (frame counters) in the presence of unexpected/out-of-sequence messages. In this paper we show...More>>
Technical Threat Intelligence Analytics: What and How to Visualize for Analytic Process
R. Damasevicus, J. Toldinas, A. Venckauskas, S. Grigaliunas, N. Morkevicius
Visual Analytics uses data visualization techniques for enabling compelling data analysis by engaging graphical and visual portrayal. In the domain of cybersecurity, convincing visual representation of data enables to ascertain valuable observations that allow the domain experts to construct efficient cyberattack mitigation strategies and provide useful decision support. We...More>>
Machine Learning – The Results Are Not the only Thing that Matters! What About Security, Explainability and Fairness?
Michał Choraś, Marek Pawlicki, Damian Puchalski, Rafał Kozik
Recent advances in machine learning (ML) and the surge in computational power have opened the way to the proliferation of ML and Artificial Intelligence (AI) in many domains and applications. Still, apart from achieving good accuracy and results, there are many challenges that need to be discussed in order...More>>
Backstabber's Knife Collection: A Review of Open Source Software Supply Chain Attacks
Ohm M., Plate H., Sykosch A., Meier M.
A software supply chain attack is characterized by the injection of malicious code into a software package in order to compromise dependent systems further down the chain. Recent years saw a number of supply chain attacks that leverage the increasing use of open source during software development, which is...More>>
ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods
Schlegel, Udo; Cakmak, Eren; Keim, Daniel A.
Abstract Explainable artificial intelligence (XAI) methods aim to reveal the non-transparent decision-making mechanisms of black-box models. The evaluation of insight generated by such XAI methods remains challenging as the applied techniques depend on many factors (e.g., parameters and human interpretation). We propose ModelSpeX, a visual analytics workflow to interactively extract...More>>
Defending Network Intrusion Detection Systems against Adversarial Evasion Attacks
Marek Pawlicki; Michał Choraś; Rafał Kozik
Intrusion Detection and the ability to detect attacks is a crucial aspect to ensure cybersecurity. However, what if an IDS (Intrusion Detection System) itself is attacked; in other words what defends the defender? In this work, the focus is on countering attacks on machine learning-based cyberattack detectors. In principle, we...More>>
LITNET-2020: An Annotated Real-World Network Flow Dataset for Network Intrusion
Damasevicius, Robertas; Venckauskas, Algimantas; Grigaliunas, Sarunas; Toldinas, Jevgenijus; Morkevicius, Nerijus; Aleliunas, Tautvydas; Smuikys, Paulius.
Network intrusion detection is one of the main problems in ensuring the security of modern computer networks, Wireless Sensor Networks (WSN), and the Internet-of-Things (IoT). In order to develop efficient network-intrusion-detection methods, realistic and up-to-date network flow datasets are required. Despite several recent efforts, there is still a lack of...More>>