Attribute Learning for Network Intrusion Detection
Network intrusion detection is one of the most visible uses for Big Data analytics. One of the main problems in this application is the constant rise of new attacks. This scenario, characterized by the fact that not enough labeled examples are available for the new classes of attacks is hardly addressed by traditional machine learning approaches. New findings on the capabilities of Zero-Shot learning (ZSL) approach makes it an interesting solution for this problem because it has the ability to classify instances of unseen classes. ZSL has inherently two stages: the attribute learning and the inference stage. In this paper we propose a new algorithm for the attribute learning stage of ZSL. The idea is to learn new values for the attributes based on decision trees (DT). Our results show that based on the rules extracted from the DT a better distribution for the attribute values can be found. We also propose an experimental setup for the evaluation of ZSL on network intrusion detection (NID).
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeIntrusion DetectionNetwork Intrusion DetectionZero-Shot LearningSimilar Papers 제목 키워드 기반
A Grassmannian Approach to Zero-Shot Learning for Network Intrusion Detection
One of the main problems in Network Intrusion Detection comes from constant rise of new attacks, so that not enough labeled examples are available for the new classes of attacks. Traditional Machine Learning approaches h…
AttributeIntrusion DetectionNetwork Intrusion DetectionZero-Shot LearningIndividual Packet Features are a Risk to Model Generalisation in ML-Based Intrusion Detection
Machine learning is increasingly used for intrusion detection in IoT networks. This paper explores the effectiveness of using individual packet features (IPF), which are attributes extracted from a single network packet,…
Intrusion DetectionCyberCScope: Mining Skewed Tensor Streams and Online Anomaly Detection in Cybersecurity Systems
Cybersecurity systems are continuously producing a huge number of time-stamped events in the form of high-order tensors, such as {count; time, port, flow duration, packet size, . . . }, and so how can we detect anomalies…
Anomaly DetectionSTATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learning
In-vehicle network (IVN) is facing complex external cyber-attacks, especially the emerging masquerade attacks with extremely high difficulty of detection while serious damaging effects. In this paper, we propose the STAT…
Graph LearningIntrusion DetectionXAI-based Feature Selection for Improved Network Intrusion Detection Systems
Explainability and evaluation of AI models are crucial parts of the security of modern intrusion detection systems (IDS) in the network security field, yet they are lacking. Accordingly, feature selection is essential fo…
AttributeDecision Makingfeature selectionIntrusion Detection+1