In this paper, we analyze the potential of using Emergent Self-Organizing Maps (ESOMs) based on Kohonen Self -Organizing maps in order to detect intrusive behaviours. The proposed approach combines machine learning and information visualization techniques to analyze network traffic and is based on classifying "normal" versus "abnormal" traffic. The results are promising as they show the ability of eSOMs to classify normal against abnormal behaviour regarding false alarms and detection probabilities. \ua9 Springer-Verlag Berlin Heidelberg 2006
Network security monitoring using machine learning algorithms is a topic that has been well research...
Network intrusion detection is the problem of detecting unauthorised use of, or access to, computer ...
Purely based on a hierarchy of self-organizing feature maps (SOMs), an approach to network intrusion...
Abstract. In this paper, we analyze the potential of using Emergent Self-Organizing Maps (ESOMs) bas...
While many techniques have been explored for detecting intrusive or abnormal behavior on computer sy...
Denial of service attacks constitute one of the greatest problem in network security. Monitoring tra...
The growth of the Internet and consequently, the number of interconnected computers through a shared...
International audienceIt is a well-known problem that intrusion detection systems overload their hum...
Traditional intrusion detection systems (IDSs) focus on low-level attacks and anomalies, and raise a...
Internet became one of life’s basics in these days. More networks are connected to the Internet ever...
Model-based intrusion detection and knowledge discovery are combined to cluster and classify P2P bot...
The continuous evolution of the attacks against computer networks has given renewed strength to rese...
Model-based intrusion detection and knowledge discovery are combined to cluster and classify P2P bot...
Abstract. Anomaly detection attempts to recognize abnormal behavior to detect intrusions. We have co...
Abstract:- In this paper we propose a framework for using self-organizing networks for agent based i...
Network security monitoring using machine learning algorithms is a topic that has been well research...
Network intrusion detection is the problem of detecting unauthorised use of, or access to, computer ...
Purely based on a hierarchy of self-organizing feature maps (SOMs), an approach to network intrusion...
Abstract. In this paper, we analyze the potential of using Emergent Self-Organizing Maps (ESOMs) bas...
While many techniques have been explored for detecting intrusive or abnormal behavior on computer sy...
Denial of service attacks constitute one of the greatest problem in network security. Monitoring tra...
The growth of the Internet and consequently, the number of interconnected computers through a shared...
International audienceIt is a well-known problem that intrusion detection systems overload their hum...
Traditional intrusion detection systems (IDSs) focus on low-level attacks and anomalies, and raise a...
Internet became one of life’s basics in these days. More networks are connected to the Internet ever...
Model-based intrusion detection and knowledge discovery are combined to cluster and classify P2P bot...
The continuous evolution of the attacks against computer networks has given renewed strength to rese...
Model-based intrusion detection and knowledge discovery are combined to cluster and classify P2P bot...
Abstract. Anomaly detection attempts to recognize abnormal behavior to detect intrusions. We have co...
Abstract:- In this paper we propose a framework for using self-organizing networks for agent based i...
Network security monitoring using machine learning algorithms is a topic that has been well research...
Network intrusion detection is the problem of detecting unauthorised use of, or access to, computer ...
Purely based on a hierarchy of self-organizing feature maps (SOMs), an approach to network intrusion...