We organize a conference for collecting IMSc points in the context of the IT Security BSc course in the spring semester of the 2023/24 academic year at BME. Beyond IMSc point collection, the goal of the conference is to encourage students to deep-dive into some hot topics of IT security, to get familiar with the challenges and recent research results, and to share knowledge with other students in the form of short presentations. We do hope that the conference will shed light on the beauty of the field of IT security and some of its exciting research areas, and it will stimulate both the active participants of the conference and all other students enrolled in the IT security course to engage in further studies in the domain of IT security.
The Call for Papers (CfP) for the conference is available here.
all, uav, cyber-physical-system, vehicle, network-security, power grid, machine-learning, data-evaluation, privacy, economics, malware, binary-similarity, cryptography, machine-learning-security, LLM-security, LLM, copilot, federated-learning, poisoning, password-manager, AAA, OAuth, web-security, Kerberos
Learning systems that require all the data to be fed into a learning model running on a central server pose serious privacy concerns. For example, the transmission of health data across certain organizational boundaries may violate security and privacy rules. Federated Learning (FL) was proposed by Google to mitigate this issue by enabling a group of clients (e.g, different stakeholders) to jointly learn a model while keeping their private data at their local devices. However, FL has been shown vulnerable to model and data poisoning attacks, where one or more malicious clients try to poison the global model by sending carefully crafted local model updates to the central parameter server. These attacks may cause the central model to underperform, more costly to train, or misclassify certain testing samples. There are several schemes that attempt to detect and eliminate such misbehaving clients.
Tags: machine-learning, machine-learning-security, federated-learning, poisoning
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