Now security is considered as a major issue in networks, since the network has extended dramatically. Therefore, intrusion detection systems have attracted attention, as it has an ability to detect intrusion accesses effectively. These systems identify attacks and react by generating alerts or by blocking the unwanted data/traffic. The proposed system includes fuzzy logic with a data mining method which is a class-association rule mining method based on genetic algorithm. Due to the use of fuzzy logic, the proposed system can deal with mixed type of attributes and also avoid the sharp boundary problem. Genetic algorithm is used to extract many rules which are required for anomaly detection systems. An association-rulemining method is used to extract a sufficient number of important rules for the user's purpose rather than to extract all the rules meeting the criteria which are useful for misuse detection. Experimental results with KDD99 Cup database from MIT Lincoln Laboratory show that the proposed method provides competitively high detection rates compared with crisp data mining.

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Due to the tremendous growth of the Internet and Network based services, the severity of network based computer attacks have significantly increased. Thus, IDS play a vital role in network security. Intrusion detection system tries to detect computer attacks by examining various data records, log audits etc. Many existing IDS such as Snort are signature based system. The problem with such a system is that it cannot detect novel attacks whose signature is not available and hence generates a high rate of alerts. In this paper Multilayer Perceptron (MLP) with Back-Propagation algorithm is used to classify attacks. We train and test MLP with KDD99 training dataset. We use KDD99 dataset which is a subset of the DARPA dataset. It is a preprocessed dataset and is most suitable for our system. We analyze the working ofMLP by performing various experiments. We observed that MLP Neural network requires large training time. Once it trained, detects known as well as unknown attacks and also reduces false alerts.

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Over the past years music has been continually evolving through the its tempos and beats as well as it’s melody. Traditionally, music is produced by a group of musicians with different instruments combining together to create a final synchronised product. In recent years, harmonies and beats were always considered to be generated manually. However, with the advent of digital technologies and software, it has become possible for machines to generate music automatically at an alarming pace. The purpose of this research is to propose a method for creating musical notes using Recurrent Neural Networks (RNN), specifically Long Short-Term Memory (LSTM)networks. To implement this algorithm, a model is created and the data is represented in the form of a percussive instrument digital interface (MIDI) file for easy access and interpretation. The process of preparing the data for input into the model isalso discussed, as well as techniques for receiving, processing, and storing MIDI files for use as input.To enhance its learning capabilities, the model should be able to remember previous details of a musical sequence and its structure. This paper discusses the use of a layered architecture in the LSTM model and how its connections interweave to create a neural network.