360◦ Anomaly Based Unsupervised Intrusion Detection
نویسنده
چکیده
This paper is meant as a reference to describe the research conducted at the Politecnico di Milano university on unsupervised learning for anomaly detection. We summarize our key results and our ongoing and future work, referencing our publications as well as the core literature of the field to give the interested reader a roadmap for exploring our research area.
منابع مشابه
Unsupervised Anomaly Detection in Network Intrusion Detection Using Clusters
Most current network intrusion detection systems employ signature-based methods or data mining-based methods which rely on labeled training data. This training data is 90 typically expensive to produce. Moreover, these methods have difficulty in detecting new types of attack. In this paper, we have discussed anomaly based instruction detection, pros and cons of anomaly detection, supervised and...
متن کاملUnsupervised Sequential Information Bottleneck Clustering For Building Anomaly Based Network Intrusion Detection Model
In this paper we present a novel approach to unsupervised clustering in building an efficient anomaly based network intrusion detection model. The method is based on a recently introduced sequential information bottleneck (sIB) principle. KDDCup 1999 intrusion detection benchmark dataset is used for the experimentation of our proposed technique. The experimental results demonstrate that the pro...
متن کاملAnomaly Intrusion Detection Design Using Hybrid of Unsupervised and Supervised Neural Network
This paper proposed a new approach to design the system using a hybrid of misuse and anomaly detection for training of normal and attack packets respectively. The utilized method for attack training is the combination of unsupervised and supervised Neural Network (NN) for Intrusion Detection System. By the unsupervised NN based on Self Organizing Map (SOM), attacks will be classified into small...
متن کاملImproving Self Organizing Map Performance for Network Intrusion Detection
The continuous evolution of the types of attacks against computer networks suggests a paradigmatic shift from misuse based intrusion detection system to anomaly based systems. Unsupervised learning algorithms are natural candidates for this task, but while they have been successfully applied in host-based intrusion detection, network-based applications are more difficult, for a variety of reaso...
متن کاملNetwork Anomaly Detection Using Unsupervised Model
Most existing network intrusion detection systems use signature-based methods which depend on labeled training data. This training data is usually expensive to produce due to cost of laboratory set up, experienced or knowledge person and non availability of ready software tool. Above all, these methods have difficulty in detecting new or unknown types of attacks. Using unsupervised anomaly dete...
متن کامل