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Network security risks grow with increase in the network size. In recent past, the attacks on computer networks have increased tremendously and require efficient network intrusion detection mechanisms. Data mining and machine-learning techniques have been used for network intrusion detection during the past few years and have gained much popularity. In this paper, we propose an intrusion detection mechanism based on binary particle swarm optimization (PSO) and random forests (RF) algorithms called PSO-RF and investigate the performance of various dimension reduction techniques along with a set of different classifiers including the proposed approach. Binary PSO is used to find more appropriate set of attributes for classifying network intrusions, and RF is used as a classifier. In the preprocessing step, we reduce the dimensions of the dataset by using different state-of-the-art dimension reduction techniques, and then this reduced dataset is presented to the proposed PSO-RF approach that further optimizes the dimensions of the data and finds an optimal set of features. PSO is an optimization method that has a strong global search capability and is used here for dimension optimization. We perform extensive experimentation to prove the worth of the proposed approach by using different performance metrics. The standard benchmark, that is, KDD99Cup dataset, is used that contains the information about various kinds of network intrusions. The experimental results indicate that the proposed approach performs better than the other approaches for the detection of all kinds of attacks present in the dataset.

Feature subset selection is one of the important problems in a number of fields namely data mining, machine learning, pattern recognition. It refers to the problem of opting for useful features that are neither irrelevant nor redundant. Since most of the data acquired through different sources are not in a proper shape to mine useful patterns from it therefore feature selection is applied over this data to filter out useless features. But since feature selection is a combinatorial optimization problem therefore exhaustively generating and evaluating all possible subsets is intractable in terms of computational cost, memory usage and processing time. Hence such a mechanism is required that intelligently searches for useful set of features in a polynomial time. In this study a feature subset selection algorithm based on conditional mutual information and ant colony optimization is proposed. The proposed method is a pure filter based feature subset selection technique that incurs less computational cost and proficient in terms of classification accuracy. Moreover, along with high accuracy it opts for less number of features. Extensive experimentation is performed based on thirteen benchmark datasets over a number of well known classification algorithms. Empirical results endorse efficiency and effectiveness of the proposed method.

Control of active prosthetic hands using surface electromyography (sEMG) signals is an active research area; despite the advances in sEMG pattern recognition and classification techniques, none of the commercially available prosthetic hands provide the user with an intuitive control. One of the major reasons for this disparity between academia and industry is the variation of sEMG signals in a dynamic environment as opposed to the controlled laboratory conditions. This research investigated the effects of sEMG signal variation on the performance of a hand motion classifier due to arm position variation and also explored the effect of static position and dynamic movement strategies for classifier training. A wearable system is used to measure the electrical activity of the muscles and the position of the forearm while performing six classes of hand motions. The system is made position aware (POS) using inertial measurement units (IMUs) for different arm movement gestures. The hand gestures are decoded under both static and dynamic forearm movements. Four time domain (TD) features are extracted from the sEMG signals along with IMU-based arm position information. The features are trained and tested using linear discriminant analysis (LDA) and support vector machine (SVM) for both TD and TD-POS features. The results for the SVM show a significant difference between the static and dynamic approaches, while the TD-POS features show enhanced classification performance in comparison to the TD-based classification. Results have shown the effectiveness of the dynamic training approach and sensor fusion techniques to improve the performance of existing stand-alone sEMG-based prosthetic control systems.

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