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The development of Internet protocols are greatly needed as the network security becomes one of the most important issues. This brings the need to develop IPv4 into IPv6 in order to proceed towards increasing the network capacity. Now Intruders are considered as one of the most serious threats to the internet security. Data mining techniques have been successfully utilized in many applications. Many research projects have applied data mining techniques to intrusion detection. Furthermore different types of data mining algorithms are very much useful to intrusion detection such as Classification, Link Analysis and Sequence Analysis. Moreover, one of the major challenges in securing fast networks is the online detection of suspicious anomalies in network traffic pattern. Most of the current security solutions failed to perform the security task in online mode because of the time needed to capture the packets and making decision about it. Practically, this study provides alliterative survey for the enhancement associated with IPv6 in terms of its security related functions. It is worthy mentioned that this study is concurred with the data mining approaches that have been used to detect intrusions.

This report describes the results of an experiment assessing 5 security configurations in order to increase the amount of security for an industrial control system (ICS). The first objective was to evaluate how network topology affects the information learned by an attacker to conduct passive reconnaissance of an ICS. The second objective was to identify useful methods to detect network intrusion. The testbed experiment demonstrated that network segregation and technical controls can reduce the attack surface of an ICS network. The experiment also revealed that whitelisting techniques can detect an attacker since ICS network hosts rarely change. In addition, we describe general methods for characterizing baseline Modbus traffic that could be used for detecting anomalous ICS traffic from an attacker.

With the rapid advancement of computer technology during the last couple of decades. Computer systems are commonly used in manufacturing, corporate, as well as other aspects of human living. As a result, constructing dependable infrastructures is a major challenge for IT managers. On the contrary side, this same rapid advancement of technology has created numerous difficulties in building reliable networks which are challenging tasks. There seem to be numerous varieties of attacks that affect the accessibility, authenticity, as well as secrecy of communications systems. In this paper, an in-depth and all-inclusive description of artificial intelligence methods used for the detection of network intrusions is discussed in detail.

With the rapid advancement of computer technology during the last couple of decades. Computer systems are commonly used in manufacturing, corporate, as well as other aspects of human living. As a result, constructing dependable infrastructures is a major challenge for IT managers. On the contrary side, this same rapid advancement of technology has created numerous difficulties in building reliable networks which are challenging tasks. There seem to be numerous varieties of attacks that affect the accessibility, authenticity, as well as secrecy of communications systems. In this paper, an in-depth and all-inclusive description of artificial intelligence methods used for the detection of network intrusions is discussed in detail.

It has become an important model in various applications such as education, business and others. So security of the data that is communicated through internet is necessary. Secure network is maintained by Intrusion Detection System (IDS). IDS observes the data traffic carefully and identifies it as normal or spam. Nowadays most of the applications depends on the advance network technologies namely wireless networks, wireless sensor networks and Bluetooth. In case of wireless sensor networks security mechanisms such as key-management protocols, authentication techniques and security protocols cannot be used because of resource constraints. Intrusion Detection System is the ideal security mechanism for wireless sensor networks. In this paper classification and predictive models for intrusion detection are built by using machine learning classification algorithms.