Amani Abou Rida

In Complex computational ecosystems 2023 (CCE’23)

Abstract The importance of machine learning (ML) in detecting cyberattacks lies in its ability to efficiently process and analyze large volumes of IoT data, which is critical in ensuring the security and privacy of sensitive information transmitted between connected devices. However, the lack of explainability of ML algorithms has become a significant concern in the cybersecurity community. Therefore, explainable techniques are developed to make ML algorithms more transparent, thereby improving trust in attack detection systems by its ability to allow cybersecurity analysts to understand the reasons for model predictions and to identify any limitation or error in the model.

Anomaly detection on static and dynamic graphs using graph convolutional neural networks

By Amani Abou Rida, Rabih Amhaz, Pierre Parrend

2022-03-01

In Robotics and AI for cybersecurity and critical infrastructure in smart cities

Abstract Anomalies represent rare observations that vary significantly from others. Anomaly detection intended to discover these rare observations has the power to prevent detrimental events, such as financial fraud, network intrusion, and social spam. However, conventional anomaly detection methods cannot handle this problem well because of the complexity of graph data (e.g., irregular structures, relational dependencies, node/edge types/attributes/directions/multiplicities/weights, large scale, etc.) [1]. Thanks to the rise of deep learning in solving these limitations, graph anomaly detection with deep learning has obtained an increasing attention from many scientists recently.

Metrics for evaluating interface explainability models for cyberattack detection in IoT data

Anomaly detection on static and dynamic graphs using graph convolutional neural networks

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