Intrusion Detection Systems (IDSs) play an important part in securing computer networks from different malicious threats and attacks. Modern IDSs leverage machine learning algorithms for effective intrusion detection. However, network traffic flows contain a large number of redundant features in a high-dimensional feature space which decreases the performance of such data-driven approaches drastically. Existing feature reduction methods lack to effectively remove redundant features as well as to retain features with additional information (if there is any). In this paper, we propose a Redundancy based Hierarchical Clustering (RHC) method that groups redundant features in the same cluster based on mutual information. We use both feature selection and extraction for finding the final feature set. We conduct rigorous experiments on three benchmark security datasets and our results demonstrate that the proposed methods outperform the state-of-the-art methods in terms of accuracy, f-score and false positive rate. We show the superiority of our proposed methods in both binary class (normal vs attack) and multi-class classification.

Feature selection plays a vital role in the field of data mining and machine learning for analyzing high-dimensional data. A popular criteria for feature selection is Mutual Information (MI) as it can capture both the linear and non-linear relationship among different features and class variable. Existing MI based feature selection methods use different approximation techniques to capture the joint performance of features, their relationship with the classes and eliminate the redundant features. However, these approximations may fail to select the optimal set of features, especially when the feature dimension is high. Besides, due to the absence of an appropriate searching strategy, these MI based approximations may select unnecessary features. To address these issues, we propose a method namely Feature Selection based on Redundancy maximized Clusters (FSRC) that creates the clusters of redundant features and then selects a subset of representative features from each cluster. We also propose to use bias corrected normalized MI in this regard. Rigorous experiments performed on thirty benchmark datasets demonstrate that FSRC outperforms the existing state-of-the-art methods in most of the cases. Moreover, FSRC is applied to three gene expression datasets which are high-dimensional but small sample datasets. The result shows that FSRC can select the features (genes) that are not only discriminating but also biologically relevant.

Human Activity Recognition from body-worn sensor data poses an inherent challenge in capturing spatial and temporal dependencies of time-series signals. In this regard, the existing recurrent or convolutional or their hybrid models for activity recognition struggle to capture spatio-temporal context from the feature space of sensor reading sequence. To address this complex problem, we propose a self-attention based neural network model that foregoes recurrent architectures and utilizes different types of attention mechanisms to generate higher dimensional feature representation used for classification. We performed extensive experiments on four popular publicly available HAR datasets: PAMAP2, Opportunity, Skoda and USC-HAD. Our model achieve significant performance improvement over recent state-of-the-art models in both benchmark test subjects and Leave-one-subject-out evaluation. We also observe that the sensor attention maps produced by our model is able capture the importance of the modality and placement of the sensors in predicting the different activity classes.

This paper introduces a new splitting criterion called Inter-node Hellinger Distance (iHD) and a weighted version of it (iHDw) for constructing decision trees. iHD measures the distance between the parent and each of the child nodes in a split using Hellinger distance. We prove that this ensures the mutual exclusiveness between the child nodes. The weight term in iHDw is concerned with the purity of individual child node considering the class imbalance problem. The combination of the distance and weight term in iHDw thus favors a partition where child nodes are purer and mutually exclusive, and skew insensitive. We perform an experiment over twenty balanced and twenty imbalanced datasets. The results show that decision trees based on iHD win against six other state-of-the-art methods on at least 14 balanced and 10 imbalanced datasets. We also observe that adding the weight to iHD improves the performance of decision trees on imbalanced datasets. Moreover, according to the result of the Friedman test, this improvement is statistically significant compared to other methods.

An appropriate aging description from face image is the prime influential factor in human age recognition, but still there is an absence of a specially engineered aging descriptor, which can characterize discernible facial aging cues (e.g., craniofacial growth, skin aging) from a detailed and more finer point of view. To address this issue, we propose a local face descriptor, directional age-primitive pattern (DAPP), which inherits discernible aging cue information and is functionally more robust and discriminative than existing local descriptors. We introduce three attributes for coding the DAPP description. First, we introduce Age-Primitives encoding aging related to the most crucial texture primitives, yielding a reasonable and clear aging definition. Second, we introduce an encoding concept dubbed as Latent Secondary Direction, which preserves compact structural information in the code avoiding uncertain codes. Third, a globally adaptive thresholding mechanism is initiated to facilitate more discrimination in a flat and textured region. We apply DAPP on separate age group recognition and age estimation tasks. Applying the same approach to both of these tasks is seldom explored in the literature. Carefully conducted experiments show that the proposed DAPP description outperforms the existing approaches by an acceptable margin.

Various methods have been proposed for enhancing the images. Some of those perform well in some specific application areas but most of the techniques suffer from artifacts due to over enhancement. To overcome this problem, we have introduced a new image enhancement technique namely Bilateral Histogram Equalization with Pre-processing (BHEP) which uses Harmonic mean to divide the histogram of the image. We have performed both qualitative and quantitative measurements for experiments and the results show that BHEP creates less artifacts in several standard images than the existing state-of-the-art image enhancement techniques.

BACKGROUND: Eukaryotic promoter prediction using computational analysis techniques is one of the most difficult jobs in computational genomics that is essential for constructing and understanding genetic regulatory networks. The increased availability of sequence data for various eukaryotic organisms in recent years has necessitated for better tools and techniques for the prediction and analysis of promoters in eukaryotic sequences. Many promoter prediction methods and tools have been developed to date but they have yet to provide acceptable predictive performance. One obvious criteria to improve on current methods is to devise a better system for selecting appropriate features of promoters that distinguish them from non-promoters. Secondly improved performance can be achieved by enhancing the predictive ability of the machine learning algorithms used. RESULTS: In this paper, a novel approach is presented in which 128 4-mer motifs in conjunction with a non-linear machine-learning algorithm utilising a Support Vector Machine (SVM) are used to distinguish between promoter and non-promoter DNA sequences. By applying this approach to plant, Drosophila, human, mouse and rat sequences, the classification model has showed 7-fold cross-validation percentage accuracies of 83.81%, 94.82%, 91.25%, 90.77% and 82.35% respectively. The high sensitivity and specificity value of 0.86 and 0.90 for plant; 0.96 and 0.92 for Drosophila; 0.88 and 0.92 for human; 0.78 and 0.84 for mouse and 0.82 and 0.80 for rat demonstrate that this technique is less prone to false positive results and exhibits better performance than many other tools. Moreover, this model successfully identifies location of promoter using TATA weight matrix. CONCLUSION: The high sensitivity and specificity indicate that 4-mer frequencies in conjunction with supervised machine-learning methods can be beneficial in the identification of RNA pol II promoters comparative to other methods. This approach can be extended to identify promoters in sequences for other eukaryotic genomes.