Abstract:Genetic algorithm (GA) has received significant attention for the design and implementation of intrusion detection systems. In this paper, it is proposed to use variable length chromosomes (VLCs) in a GA-based network intrusion detection system. Fewer chromosomes with relevant features are used for rule generation. An effective fitness function is used to define the fitness of each rule. Each chromosome will have one or more rules in it. As each chromosome is a complete solution to the problem, fewer chromosomes are sufficient for effective intrusion detection. This reduces the computational time. The proposed approach is tested using Defense Advanced Research Project Agency (DARPA) 1998 data. The experimental results show that the proposed approach is efficient in network intrusion detection.

In the last few years there has been a tremendous increase in connectivity between systems which has brought about limitless possibilities and opportunities. Unfortunately security related problems have also increased at the same rate. Computer systems are becoming increasingly vulnerable to attacks. These attacks or intrusions based on flaws in operating system or application programs usually read or modify confidential information or render the system useless. Different soft computing techniques are used for network intrusion detection (NID).This paper presents an effective GA based approach to generate the classification rules for network intrusion detection. While applying GA an, enumeration technique is used to select the gene values in a chromosome. This enumeration technique substantially reduces the computational time required for population generation and yields more appropriate rules. These rules are then used to detect the network intrusions. Experimental results show that this technique is more effective in detecting intrusions.

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PIC microcontroller & PC based gas sensing system is presented in this project. The analysis presented here depends on thin film metal oxide gas sensors TGS 822, TGS 813, TGS 2600, MQ6 and MQ7. The differences in the steady state performance among their sensors are used for improving their selectivity and sensitivity, while the combination of gas sensors permits success in gas classification problems. In the presented approach the gas sensors are embedded into a chamber with a heating system. Different types of gases are used, such as, Methane, Carbon monoxide and LPG to pass through this chamber with different concentrations, different operating temperatures and different load resistances. Sets of data collected to detect the gas sensitivity for each sensor depending on the output voltage in relation to temperatures, concentration of gases and variable resistances for each sensor. In this project, novel approach, based on the ANN technique, used for the gas identification. The identification is done directly from the data driven from the microcontroller by using ANN trained model. The results of the ANN are shown to provide gas identification according to variation in different parameters, such as gas concentrations, variation in sensor's resistance and output voltage of sensor at different temperatures and to indicate that the selection of different gases is possible, based on microcontroller, which improves sensitivity and selectivity with high accuracy and reliability.

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