ICST 2024
Mon 27 - Fri 31 May 2024 Canada
Mon 27 May 2024 11:30 - 12:00 at Room 4 - ITEQS II Chair(s): Mehrdad Saadatmand

In previous work, we proposed an end-to-end early intrusion detection system to identify network attacks in real-time before they complete and could cause any more damage to the system under attack. To implement the approach, we have trained a Convolution Neural Network (CNN) model with attention mechanism in a supervised manner to extract relevant features from raw network traffic in order to classify network flows into different types of attacks. In this preliminary work, we discuss and compare the results of using the Recurrent Neural Network (RNN) model with an attention mechanism to detect the attacks earlier. Furthermore, the model not only classifies the given flow, but it also ranks the packets in the flow with respect to their importance for prediction. This ranking can be used for further investigation of the detected network attacks. We empirically evaluate our approach on the CICIDS2017 dataset. Preliminary results show that the RNN model with an attention mechanism can achieve better classification performance than our previous work with CNN model.

Mon 27 May

Displayed time zone: Eastern Time (US & Canada) change

11:00 - 12:30
ITEQS IIITEQS at Room 4
Chair(s): Mehrdad Saadatmand RISE Research Institutes of Sweden
11:00
30m
Full-paper
Automated SQA Framework with Predictive Machine Learning in Airfield Software
ITEQS
Ridwan Hossain , Akramul Azim Ontario Tech University, Linda Cato Team Eagle, Bruce Wilkins Team Eagle
11:30
30m
Full-paper
Early Detection with Explainability of Network Attacks Using Deep LearningBest Paper
ITEQS
Tanwir Ahmad Åbo Akademi University, Dragos Truscan Åbo Akademi University
12:00
30m
Full-paper
Testing cyber-physical systems with explicit output coverageBest Paper
ITEQS
Jarkko Peltomäki Åbo Akademi University, Jesper Winsten , Maxime Methais , Ivan Porres Åbo Akademi University