QUIC Flow Traffic AnLyzer ()

We released the first version of the tool as a Python open-source project to extract 180 features from QUIC network traffic flows.

Detecting vulnerability in network systems through AI

91亚色 researchers are exploring how to better secure a digital world increasingly shaped by the Internet of Things (IoT) by understanding how malicious bots operate and developing stronger defences against them.

IoT devices are everyday objects that connect to the internet so they can send, receive and act on data. They range from home thermostats and baby monitors to traffic sensors, medical equipment and industrial controls. Many operate quietly in the background and are rarely updated or closely monitored, making them especially attractive targets for cybercriminals.

How 91亚色 researchers are strengthening cybersecurity

91亚色 researchers are exploring how to better secure a digital world increasingly shaped by the Internet of Things (IoT) by understanding how malicious bots operate and developing stronger defences against them.

IoT devices are everyday objects that connect to the internet so they can send, receive and act on data. They range from home thermostats and baby monitors to traffic sensors, medical equipment and industrial controls. Many operate quietly in the background and are rarely updated or closely monitored, making them especially attractive targets for cybercriminals.

With the inevitable growth of information digitization, Portable Document Format (PDF) has become one of the most popular exploited file formats for document exchange among various applications and platforms. Consequently, PDF files have become an attractive target for attackers to infect and deliver malicious codes to users. Despite the efficacy and success of machine learning classifiers in detecting malicious PDF files, they require tedious feature engineering and have some limitations. Additionally, one of the main reasons for resistance to using deep learning models is their lack of interpretability. To address these challenges, this study proposes using the TabNet model for malicious PDF detection, offering global and local interpretability while maintaining high or competitive detection performance. The Optuna optimization framework is employed to further enhance the model鈥檚 capabilities. The proposed approach is evaluated on the real-world Evasive-PDFMal2022 dataset and demonstrates state-of-the-art performance compared to baseline methods.

The rapid proliferation of Internet of Things (IoT) devices has improved connectivity but introduced new cybersecurity risks, particularly from botnets. Detecting and identifying malicious botnet activities is crucial for early attack mitigation, understanding attack patterns, and deploying effective countermeasures. However, state-of-the-art IoT botnet detection models often struggle to handle imbalanced data, capture temporal patterns, and provide interpretable, explainable insights. This work proposes an IoT botnet detection and profiling model that leverages Explainable Artificial Intelligence (XAI) methods, including eXtreme Gradient Boosting (XGBoost) for feature selection, a Long Short-Term Memory (LSTM) neural network model for botnet detection and classification, and Shapley Additive Explanations (SHAP) for interpretability. This model integrates a feature selection approach that combines correlation analysis with the XGBoost algorithm to improve efficiency. The LSTM model is optimized and fine-tuned using Bayesian optimization to achieve accurate botnet detection and classification. The SHAP method provides interpretable insights into individual and collective botnet behaviors for profiling. Finally, the performance of the proposed model was evaluated with the augmented BCCC-Aposemat-IoT-Bot-2024 dataset and compared with state-of-the-art models. The results demonstrate that our proposed model achieves competitive performance while offering key advantages, including effective handling of sequential and imbalanced data, improved computational efficiency, and enhanced explainability.