Ensuring that the appropriate level of security is available in wireless networks is absolutely essential. To aid in the defense and detection of potential threats, WLANs should employ security solutions that include an anomaly-based intrusion detection system (ADS) that identify wireless network intrusions by gathering and analyzing the data that is available on the system. This paper is to pr...

C security has become an increasingly vital field in computer science in response to the proliferation of private sensitive information. The term “Intrusion” refers to any unauthorized access which attempts to compromise confidentiality, integrity and availability of information resources [1] [14] [32]. Traditional intrusion prevention techniques such as firewalls, access control and encryption...

In the field of network intrusion detection, both the signature-based intrusion detection system and the machine learning-based intrusion detection system possess advantages and disadvantages. When the two discrepant systems are combined in a way that the former is used as the main system and the latter as a supporting system, the machine learning-based intrusion detection system measures the v...

High level security is an essentially required in the communication and information sharing on the network clouds. Intrusion detection system (IDS) is being used to detect violations and malicious behavior over networks and hosts. Purpose of our paper is to provide an Intrusion detection system to detect and prevent the malicious behavior on the cloud computing. We propose an intrusion detectio...

Recently data mining methods have gained importance in addressing network security issues, including network intrusion detection—a challenging task in network security. Intrusion detection systems aim to identify attacks with a high detection rate and a low false alarm rate. Classification-based data mining models for intrusion detection are often ineffective in dealing with dynamic changes in ...

Designers of intrusion detection systems are often faced with the problem that their design fails to meet the specification because the actual implementation is not able to detect attacks as required. This work aims at addressing such shortcomings at an early stage of the design process. The proposed method provides guidance to intrusion detection systems designers by predicting whether or not ...

energy consumption is considered as a critical issue in wireless sensor networks (wsns). batteries of sensor nodes have limited power supply which in turn limits services and applications that can be supported by them. an efcient solution to improve energy consumption and even trafc in wsns is data aggregation (da) that can reduce the number of transmissions. two main challenges for da are: (i)...

This report explores integrating fuzzy logic with two data mining methods (association rules and frequency episodes) for intrusion detection. Data mining methods are capable of extracting patterns automatically from a large amount of data. The integration with fuzzy logic can produce more abstract and flexible patterns for intrusion detection, since many quantitative features are involved in in...

Recent reports show that the loss from the malicious intrusion by insiders is more serious than by outsiders. Despite that the various attacks are occurred by insiders and outsiders, most work has been focused on the intrusion detection against outsider attacks. In this paper, we improve the Wang et al.’s insider predection model [15] and propose the combined model with access control for the e...

In The extensive growth of computing networks and tools and tricks for intruding into and attacking networks has underscored the importance of intrusion detection in network security. Yet, contemporary intrusion detection systems (IDS) are limiting in that they typically employ a misuse detection strategy, with searches for patterns of program or user behavior that match known intrusion scenari...