Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods.
Hybrid wireless networks (i.e., multi-hop cellular networks) have been proven to be a better network structure for the next generation wireless networks and can help to tackle the stringent end-to-end QoS requirements of different applications. Hybrid networks synergistically combine infrastructure networks and MANETs to leverage each other. Specifically, infrastructure networks improve the scalability of MANETs, while MANETs automatically establish self-organizing networks, extending the coverage of the infrastructure networks. In a vehicle opportunistic access network (an instance of hybrid networks), people in vehicles need to upload or download videos from remote Internet servers through access points (APs) (i.e., base stations) spreading out in a city. Since it is unlikely that the base stations cover the entire city to maintain sufficiently strong signal everywhere to support an application requiring high link rates, the vehicles themselves can form a MANET to extend the coverage of the base stations, providing continuous network connections.
DISADVANTAGES OF EXISTING SYSTEM:
- Difficult to guarantee QoS in MANETs due to their unique features including user mobility, channel variance errors, and limited bandwidth.
- Although these protocols can increase the QoS of the MANETs to a certain extent, they suffer from invalid reservation and race condition problems.
In order to enhance the QoS support capability of hybrid networks, in this paper, we propose a QoS-Oriented Distributed routing protocol (QOD). Usually, a hybrid network has widespread base stations. The data transmission in hybrid networks has two features. First, an AP can be a source or a destination to any mobile node. Second, the number of transmission hops between a mobile node and an AP is small. The first feature allows a stream to have any cast transmission along multiple transmission paths to its destination through base stations, and the second feature enables a source node to connect to an AP through an intermediate node.
ADVANTAGES OF PROPOSED SYSTEM:
- The source node schedules the packet streams to neighbours based on their queuing condition, channel condition, and mobility, aiming to reduce transmission time and increase network capacity.
- Taking full advantage of the two features, QOD transforms the packet routing problem into a dynamic resource scheduling problem.
- System : Pentium IV 2.4 GHz.
- Hard Disk : 40 GB.
- Floppy Drive : 44 Mb.
- Monitor : 15 VGA Colour.
- Mouse :
- Ram : 512 Mb.
- Operating system : Windows XP/7/LINUX.
- Implementation : NS2
- NS2 Version : 2.28
- Front End : OTCL (Object Oriented Tool Command Language)
- Tool : Cygwin (To simulate in Windows OS)
Ze Li, Student Member, IEEE, and Haiying Shen, Member, IEEE, “A QoS-Oriented Distributed Routing Protocol for Hybrid Wireless Networks”, IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 13, NO. 3, MARCH 2014.
Building an intrusion detection system using a filter-based feature selection algorithm Building an intrusion detection system using a filter-based feature selection algorithm Building an intrusion detection system using a filter-based feature selection algorithm Building an intrusion detection system using a filter-based feature selection algorithm Building an intrusion detection system using a filter-based feature selection algorithm PPT BASEPAPERS