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Next generation cyberspace intrusion detection (ID) systems will require the fusion of data from myriad heterogeneous distributed network sensors to effectively create cyberspace situational awareness. This article provides a functional overview of how the art and science of multisensor data fusion can enhance the performance and reliability of ID systems. The article also discusses the data fusion inference process and data mining operations, outlines design challenges, and suggests areas for further research and development

Misuse Intrusion Detection has traditionally been understood in the literature as the detection of specific, precisely representable techniques of computer system abuse. Pattern matching is well disposed to the representation and detection of such abuse. Each specific method of abuse can be represented as a pattern and many of these can be matched simultaneously against the audit logs generated by the OS kernel. Using relatively high level patterns to specify computer system abuse relieves the pattern writer from having to understand and encode the intricacies of pattern matching into a misuse detector. Patterns represent a declarative way of specifying what needs to be detected, instead of specifying how it should be detected. We have devised a model of matching based on Colored Petri Nets specifically targeted for misuse intrusion detection. In this paper we present a software architecture for structuring a pattern matching solution to misuse intrusion detection. In the context of an object oriented prototype implementation we describe the abstract classes encapsulating generic functionality and the inter-relationships between the classes.

This report examines and classifies the characteristics of signatures used in misuse intrusion detection. Efficient algorithms to match patterns in some of these classes are described. A generalized model for matching intrusion signatures based on Colored Petri Nets is presented, and some of its properties are derived.

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Next generation cyberspace intrusion detection systems will fuse data from heterogeneous distributed network sensors to create cyberspace situational awareness. This paper provides a few first steps toward developing the engineering requirements using the art and science of multisensor data fusion as the underlying model. Current generation internet-based intrusion detection systems and basic multisensor data fusion constructs are summarized. The TCP/IP model is used to develop framework sensor and database models. The SNMP ASN.1 MIB construct is recommended for the representation of context-dependent threat

Communication networks scope is growing at a faster rate as new services and new users are being added. Management of such networks becomes important to provide the assured quality of service. Finding the skilled managers is often a problem. To alleviate that and also to provide assistance to the network managers, network management task has to be automated. Many attempts have been made in this direction and it is a promising area of interest to researchers in both academics and industry. In this paper, a critical review of the network management and artificial intelligence approaches to network management are presented.

Abstract—One main drawback of intrusion detection system is the inability of detecting new attacks which do not have known signatures. In this paper we discuss an intrusion detection method that proposes independent component analysis (ICA) based feature selection heuristics and using rough fuzzy for clustering data. ICA is to separate these independent components (ICs) from the monitored variables. Rough set has to decrease the amount of data and get rid of redundancy and Fuzzy methods allow objects to belong to several clusters simultaneously, with different degrees of membership. Our approach allows us to recognize not only known attacks but also to detect activity that may be the result of a new, unknown attack. The experimental results on Knowledge Discovery and Data Mining-(KDDCup 1999) dataset. Keywords—Network security, intrusion detection, rough set, ICA, anomaly detection, independent component analysis, rough fuzzy.

Cyberspace is a complex dimension of both enabling and inhibiting data flows in electronic data networks. Current generation intrusion detection (ID) systems are not technologically advanced enough to create the situational knowledge required to manage these networks. Next generation ID system will fuse data, combining both short-term sensor data with long-term knowledge databases, to create cyberspace situational awareness. This article offers a glimpse into the foggy crystal ball of future ID systems. Before diving into the technical discussion we ask the reader to keep in mind the generic model of a datagram traversing the Internet. Figure 1 illustrates an IP datagram moving in a store-and-forward environment from source to destination; routed based on a destination address with a uncertain source address decrementing the datagram time-to-live (TTL) at every router hop [1]. The datagram is routed through major Internets and IP transit providers. There is striking similarity between the transit of a datagram in the Internet and an airplane through airspace; future network management and air traffic control. At a very high abstract level, the concepts used to monitor objects in airspace apply to monitoring objects in networks. The Federal Aviation Administration (FAA) divides airspace management into two distinct entities. On the one hand, local controllers guide aircraft into and out of the air space surrounding an airport. Their job is to maintain awareness of the location of all aircraft in their vicinity, ensure proper separation, identify threats to aircraft, and manage the overall safety of passengers. Functionally, this is similar to the role of network controllers who must control the environment within their administrative domains. The network administrator must ensure the proper ports are open and the information is not delayed, the collisions are kept to a minimum, and the integrity of the delivery systems are not compromised. This is naturally similar to the situational awareness required in current generation air traffic control (ATC).

Threats to the network resources increase exponentially with the growth of the network /users and the technological developments. In this paper, we describe a security management framework for access control to the network resources. We deal this in three steps. In the first part, user access control to a given network is discussed. In the second part, access control to the network resources, both by self usage and delegation, revocation of delegation is presented. The third part explains the access control to the resources belonging to the other networks. A neural network based model is developed for intrusion detection that overcomes most of the limitations of the existing systems.