Black Hole attack Detection using fuzzy based IDS

In the past few years, an evolution in the wireless communication has been emerged, along with the evolution a new type with large potential application of wireless network appears, which is the Mobile Ad-Hoc Network (MANET). Black hole attack consider one of the most affected kind on MANET. Therefore, the use of intrusion detection system (IDS) has a major importance in the MANET protection. In this paper, an optimization of a fuzzy based intrusion detection system is proposed which automate the process of producing a fuzzy system by using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the initialization of the FIS and then optimize this initialized system by using Genetic Algorithm (GA). In addition, a normal estimated fuzzy based IDS is introduces to see the effect of the optimization on the system. From this study, it is proven that the optimized proposed IDS perform better that the normal estimated systems

Statistical and fuzzy approach for database security

A new type of database anomaly is described by addressing the concept of Cumulated Anomaly in this paper. Dubiety-Determining Model (DDM), which is a detection model basing on statistical and fuzzy set theories for Cumulated Anomaly, is proposed. DDM can measure the dubiety degree of each database transaction quantitatively. Software system architecture to support the DDM for monitoring database transactions is designed. We also implemented the system and tested it. Our experimental results show that the DDM method is feasible and effective

A dubiety-determining based model for database cumulated anomaly intrusion

The concept of Cumulated Anomaly (CA), which describes a new type of database anomalies, is addressed. A typical CA intrusion is that when a user who is authorized to modify data records under certain constraints deliberately hides his/her intentions to change data beyond constraints in different operations and different transactions. It happens when some appearing to be authorized and normal transactions lead to certain accumulated results out of given thresholds. The existing intrusion techniques are unable to deal with CAs. This paper proposes a detection model, Dubiety-Determining Model (DDM), for Cumulated Anomaly. This model is mainly based on statistical theories and fuzzy set theories. It measures the dubiety degree, which is presented by a real number between 0 and 1, for each database transaction, to show the likelihood of a transaction to be intrusive. The algorithms used in the DDM are introduced. A DDM-based software architecture has been designed and implemented for monitoring database transactions. The experimental results show that the DDM method is feasible and effective

Real valued negative selection for anomaly detection in wireless ad hoc networks

Wireless ad hoc network is one of the network technologies that have gained lots of attention from computer scientists for the future telecommunication applications. However it has inherits the major vulnerabilities from its ancestor (i.e., the fixed wired networks) but cannot inherit all the conventional intrusion detection capabilities due to its features and characteristics. Wireless ad hoc network has the potential to become the de facto standard for future wireless networking because of its open medium and dynamic features. Non-infrastructure network such as wireless ad hoc networks are expected to become an important part of 4G architecture in the future. In this paper, we study the use of an Artificial Immune System (AIS) as anomaly detector in a wireless ad hoc network. The main goal of our research is to build a system that can learn and detect new and unknown attacks. To achieve our goal, we studied how the real-valued negative selection algorithm can be applied in wireless ad hoc network network and finally we proposed the enhancements to real-valued negative selection algorithm for anomaly detection in wireless ad hoc network