ICST 2023
Sun 16 - Thu 20 April 2023 Dublin, Ireland
Sun 16 Apr 2023 11:00 - 11:30 at Macken - ITEQS II

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 used a deep neural network which was trained 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 work, we discuss initial results of the benefits that an attention mechanism brings to the classification performance and the capacity of the network to detect the attacks earlier. We empirically evaluate our approach on a data set CICIDS2017 dataset. Preliminary results show that the attention mechanism improves both the balanced accuracy of the classifier as well as the early detection of attacks.

Sun 16 Apr

Displayed time zone: Dublin change

11:00 - 12:30
ITEQS IIITEQS at Macken
11:00
30m
Talk
Preliminary results in using attention for increasing attack identification efficiency
ITEQS
Tanwir Ahmad Åbo Akademi University, Dragos Truscan Åbo Akademi University, Jüri Vain Tallinn University of Technology, Estonia
11:30
30m
Talk
Lightweight Method for On-the-fly Detection of Multivariable Atomicity Violations (Best Paper)Best Paper Award
ITEQS
Chang-Hui Bae Gyeongsang National University, Euteum Choi Gyeongsang National Unviersity, Yong-Kee Jun Gyeongsang National University, Ok-Kyoon Ha Kyungwoon University
12:00
30m
Talk
Using Assurance Cases to assure the fulfillment of non-functional requirements of AI-based systems - Lessons learned
ITEQS
Marc Hauer TU Kaiserslautern - Algorithm Accountability Lab , Lena Müller-Kress winnovation consulting gmbh, Gertraud Leimüller winnovation consulting gmbh & leiwand.ai gmbh, Katharina Zweig TU Kaiserslautern - Algorithm Accountability Lab