Towards a taxonomy of intrusion-detection systems
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on computer network security
Intrusion detection using autonomous agents
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Indra: A peer-to-peer approach to network intrusion detection and prevention
WETICE '03 Proceedings of the Twelfth International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises
Snort - Lightweight Intrusion Detection for Networks
LISA '99 Proceedings of the 13th USENIX conference on System administration
Unsupervised anomaly detection in network intrusion detection using clusters
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Architecture for an Artificial Immune System
Evolutionary Computation
| Hi-index | 0.00 |
Today, a computer network is under constant assault from attacks. In Computer Science, NIDS are used in order to protect a computer network against these intrusions. These systems normally use stochastic approaches or a rule-based system to detect intrusions and to describe the known intrusions. These systems have some disadvantages which we solve with a new approach called ANIMA. ANIMA stores bad-signatures of intrusions in directed and weighted graphs as well as returns for each checked-packet a value how malicious the packet is. The primary advantages of ANIMA are the online-system, adaptation, easy administration and storage-saving. In this article, we discuss the approach ANIMA for intrusion detection, the advantages and disadvantages, the implementation as well as the results occurred out of the simulations that ANIMA for intrusion detection works well in bad-packet-identification as well as the implementation substantiates the theoretical advantages.