Spotting and Mitigating DDoS Attacks Using Deep Learning for Online Traffic Analysis
Volume 17, Issue 2, July 2025, Pages 209-221
https://doi.org/10.22042/isecure.2025.217461
Mojtaba Shirinjani, Mojtaba Amiri, Amirhosein Salehi, Pouria Arefi Jamal, Rasoul Khazaei Laki, Seyed Hatef Sadegh Esfahani, Siavash Ahmadi, Masoumeh Koochak Shooshtari, Mohammad Reza Aref
Abstract Distributed Denial of Service (DDoS) attacks threaten server and network availability with minimal resources. These attacks mimic legitimate traffic, evading Intrusion Detection Systems (IDS) and Intrusion Prevention Systems(IPS). The primary challenge in countering DDoS attacks is achieving early detection as close to their origin. In addition, the persistence of malicious traffic hidden within legitimate traffic remains a common challenge for various mitigation techniques. This paper introduces a modular approach for identifying and mitigating DDoS attacks in both online and offline settings, using deep learning and rule-based techniques. We train the IDS with VGG16, GoogLeNet, Support Vector Machines (SVM), and Random Forest (RF) and evaluate them using the CICDDoS2019 dataset. Our experiments show a detection accuracy of 99.87% offline and 99.67% online. Our methodology outperforms state-of-the-art approaches in offline detection, particularly with VGG16 and GoogLeNet. In our online setup, the mitigation module successfully addresses all attacks detected by our anti-DDoS solution.
Private Federated Learning: An Adversarial Sanitizing Perspective
Volume 15, Issue 3, October 2023, Pages 67-76
https://doi.org/10.22042/isecure.2023.182211
Mojtaba Shirinjani, Siavash Ahmadi, Taraneh Eghlidos, Mohammad Reza Aref
Abstract Large-scale data collection is challenging in alternative centralized learning as privacy concerns or prohibitive policies may rise. As a solution, Federated Learning (FL) is proposed wherein data owners, called participants, can train a common model collaboratively while their privacy is preserved. However, recent attacks, namely Membership Inference Attacks (MIA) or Poisoning Attacks (PA), can threaten the privacy and performance in FL systems. This paper develops an innovative Adversarial-Resilient Privacy-preserving Scheme (ARPS) for FL to cope with preceding threats using differential privacy and
cryptography. Our experiments display that ARPS can establish a private model with high accuracy out‌performing state-of-the-art approaches. To the best of our knowledge, this work is the only scheme providing privacy protection beyond any output models in conjunction with Byzantine resiliency without sacrificing accuracy and efficiency.