Add to ORKGModel-based intrusion detection and knowledge discovery are combined to cluster and classify P2P botnet traffic and other malignant network activity by using a Self-Organizing Map (SOM) self-trained on denied Internet firewall log entries. The SOM analyzed new firewall log entries in a case study to classify similar network activity, and discovered previously unknown local P2P bot traffic and other security issues
The main purpose of this paper is to propose a novel soft computing inference engine model for intru...
Anomaly detection in user access patterns using artificial neural networks is a novel way of combati...
International audienceIt is a well-known problem that intrusion detection systems overload their hum...
Model-based intrusion detection and knowledge discovery are combined to cluster and classify P2P bot...
Abstract. In this paper, we analyze the potential of using Emergent Self-Organizing Maps (ESOMs) bas...
In this paper, we analyze the potential of using Emergent Self-Organizing Maps (ESOMs) based on Koho...
While many techniques have been explored for detecting intrusive or abnormal behavior on computer sy...
The growth of the Internet and consequently, the number of interconnected computers through a shared...
Abstract- Denial of Service attacks constitute one of the greatest problem in network security. Moni...
Network security monitoring using machine learning algorithms is a topic that has been well research...
The continuous evolution of the attacks against computer networks has given renewed strength to rese...
The growth of the Internet and, consequently, the number of interconnected computers, has exposed si...
Abstract. Anomaly detection attempts to recognize abnormal behavior to detect intrusions. We have co...
Abstract:- A new approach of anomaly intrusion detection (AID) is proposed in this paper. The Self-O...
The network is a highly vulnerable venture for any organization that needs to have a set of computer...
The main purpose of this paper is to propose a novel soft computing inference engine model for intru...
Anomaly detection in user access patterns using artificial neural networks is a novel way of combati...
International audienceIt is a well-known problem that intrusion detection systems overload their hum...
Model-based intrusion detection and knowledge discovery are combined to cluster and classify P2P bot...
Abstract. In this paper, we analyze the potential of using Emergent Self-Organizing Maps (ESOMs) bas...
In this paper, we analyze the potential of using Emergent Self-Organizing Maps (ESOMs) based on Koho...
While many techniques have been explored for detecting intrusive or abnormal behavior on computer sy...
The growth of the Internet and consequently, the number of interconnected computers through a shared...
Abstract- Denial of Service attacks constitute one of the greatest problem in network security. Moni...
Network security monitoring using machine learning algorithms is a topic that has been well research...
The continuous evolution of the attacks against computer networks has given renewed strength to rese...
The growth of the Internet and, consequently, the number of interconnected computers, has exposed si...
Abstract. Anomaly detection attempts to recognize abnormal behavior to detect intrusions. We have co...
Abstract:- A new approach of anomaly intrusion detection (AID) is proposed in this paper. The Self-O...
The network is a highly vulnerable venture for any organization that needs to have a set of computer...
The main purpose of this paper is to propose a novel soft computing inference engine model for intru...
Anomaly detection in user access patterns using artificial neural networks is a novel way of combati...
International audienceIt is a well-known problem that intrusion detection systems overload their hum...