The Snort intrusion detection system is a widely used and well-regarded open sourcesystem used for the detection of malicious activity in conventional wired networks. Recently, software patches to enable 802.11 wireless intrusion detection capability in Snort have been released. This paper focuses on the lessons learned from a live deployment of these wireless extensions to the Snort IDS. Gener...

A novel hybrid artificial intelligent system for Intrusion Detection, called MOVIH-IDS, is presented in this study. A hybrid model built by means of a multiagent system that incorporates an unsupervised connectionist Intrusion Detection System (IDS) has been defined to guaranty an efficient computer network security architecture. This hybrid IDS facilitates the intrusion detection in dynamic ne...

Since the advent of intrusion detection system (IDS) in the early 1980s, IDS has been suffering many problems until now. The detection of novel attacks and lower rate of false alarms must be realized in successful IDS. Misuse detection compares data against predefined patterns usually collected by an IDS signature database. It is hard for misuse detection to detect even slightly variation of kn...

With the rapid development and comprehensive application of network technology, network security problems gradually appear serious. Traditional firewall technologies can’t provide sufficient security protection against various attacks and intrusions (Anderson, 1980), while intrusion detection systems (IDS) are faced with compromise between false alarms and false positives (Denning, 1987). In th...

A fundamental problem in intrusion detection is what metric(s) can be used to objectively evaluate an intrusion detection system (IDS) in terms of its ability to correctly classify events as normal or intrusion. In this paper, we provide an in-depth analysis of existing metrics. We argue that the lack of a single unified metric makes it difficult to fine tune and evaluate an IDS. The intrusion ...

This paper presents a brief review of the application of various Data Mining techniques and their advances in the design, development and application of Intrusion Detection Systems (IDS) for protecting computer and communication networks from intruders. Data mining techniques are used to monitor and analyze large amount of network data & classify these network data into anomalous and normal dat...

Security of networking systems has been an issue since computer networks became prevalent, most especially now that Internet is changing the facie computing. Intrusions pose significant threats to the integrity, confidentiality and availability of information for the internet users. In this paper, a new approach to real-time network anomaly intrusion detection via Fuzzy-Bayesian is proposed to ...

Most existing intrusion detection systems take a passive approach to observing attacks or noticing exploits. We suggest that active intrusion detection (AID) techniques provide value, particularly in scenarios where an administrator attempts to recover a network infrastructure from a compromise. In such cases, an attacker may have corrupted fundamental services (e.g., ARP, DHCP, DNS, NTP), and ...

because firewalls and Intrusion Detection Systems usually only defend against value to the whole system Data mining techniques can be applied to gain. detection system (IDS) based on data mining techniques.

the Intrusion Detection System (IDS) is tool which detects an unauthorised, misuse of computer system and provides information security. An intrusion detection system (IDS) is combined with hardware and software elements that work together to find unexpected events which may indicate an attack will happen, is happening, or has happened. Network intrusion detection based on anomaly detection pro...