Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM

Applied Intelligence - With the rapid advancement in network technologies, the need for cybersecurity has gained increasing momentum in recent years. As a primary defense mechanism, an intrusion...     

Organometallic L-Alanine Cadmium Iodide Crystals for Optical Device Fabrication

Single crystals of L-alanine cadmium iodide (LACI) were grown by the slow evaporation technique at room temperature. A single-crystal X-ray diffraction (SXRD) model was used to evaluate the crystal structure of the as-grown LACI crystal. The energy dispersive X-ray (EDX) analysis and ultraviolet-visible-near infrared (UV-vis-NIR) transmittance studies were carried out, and the results reveal the presence of elements in the title compound. From the transmittance data, the optical bandgap as a function of photon energy was estimated, and the different optical constants were calculated. A fluorescence study was performed for the LACI crystal. Thermogravimetric and differential thermal analyses have also been studied to investigate the thermal property of the LACI crystal. The efficiency of the second harmonic generation (SHG) of the title crystal was investigated. The magnetic and electrical properties were estimated by the vibrating sample magnetometer (VSM) analysis and impedance study, respectively.     

Evolution of supramolecular healable composites: a minireview

Efforts to further extend the range of applications of polymer based materials have resulted in the recent production of healable polymers that can regain their strength after damage. Within this field of healable materials, supramolecular polymers have been subject to extensive investigation. By virtue of their reversible non‐covalent interactions, cracks and fractures in such polymers can be readily and repeatedly healed in order to regain key physical properties. However, many supramolecular polymers are relatively weak and elastomeric in nature, which renders them unsuitable for high strength structural applications. To overcome these deficiencies, preliminary studies have shown that it is possible to reinforce supramolecular polymers with microscale and nanoscale fillers to afford composites that are not only stronger and stiffer compared with the polymers alone but also retain their healing abilities. In this minireview we discuss the evolution of these supramolecular composites and their advantages over more conventional, covalent polymeric materials. © 2014 Society of Chemical Industry     

Fabrication of Biogenic Silica Nanostructures from Sorghum bicolor Leaves for Food Industry Applications

Silicon - Due to the large production of sorghum, the generation of associated agricultural residues, which contain high contents of silica, is inevitable. Also, these agricultural residues are not...     

Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

The era of the Internet of things (IoT) has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before. However, the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate services. Thus, there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of service. Most of the existing techniques—including collaborative filtering (CF), which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems, preventing them from providing high quality recommendations. Inspired by the great success of deep learning in a wide range of fields, this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF recommendations. The proposed deep learning model is designed as a hybrid architecture with three key networks, namely autoencoder (AE), multilayered perceptron (MLP), and generalized matrix factorization (GMF). The model employs two AE networks to learn deep latent feature representations of users and items respectively and in parallel. Next, MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item features. Finally, the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP networks. We conducted extensive experiments on two benchmark datasets, MoiveLens100K and MovieLens1M, using four standard evaluation metrics. Ablation experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation performance. Comparative analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rating sparsity and cold-start problems.     

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization (IMFP-VMMO) model in cloud environment. The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction. To accomplish this, IMFP-VMMO model employs Gradient Boosting Decision Tree (GBDT) classification model at initial stage for effectual prediction of VM failures. At the same time, VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm (QO-AFSA) which in turn reduces the energy consumption. The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model. The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.     

Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-the-art performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder (DAE) for learning the robust feature representation and one-class support vector machine (OCSVM) for finding the more compact decision hyperplane for intrusion detection. Specially, the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously. This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection. Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model. First, the ablation evaluation on benchmark dataset, NSL-KDD validates the design decision of the proposed model. Next, the performance evaluation on recent intrusion dataset, UNSW-NB15 signifies the stable performance of the proposed model. Finally, the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.     

Biogenic silica–metal phosphate (metal = Ca,Fe or Zn) nanocomposites: fabrication from rice husk and their biomedical applications

In this investigation, we fabricated biogenic silica–metal phosphate nanocomposites (BSMPNs) using rice husk from agricultural waste as a silica source. The morphologies and dimensions of the synthesized nanocomposites were analyzed using transmission electron microscopy (TEM). Fourier-transform infrared spectroscopy results confirmed that metal phosphate crystals were formed with the biogenic silica. The X-ray diffraction patterns of the BSMPNs showed the presence of hexagonal calcium and iron phosphate and orthorhombic zinc phosphate nanoparticles embedded in the matrix of biogenic silica. The TEM images suggested that spherical and irregularly shaped tiny particles with dimensions between 50 and 100 nm were dispersed in the biogenic silica. The in vitro biological properties of the nanocomposites were studied by a cell viability assay and through the analysis of microscopy images. The cytocompatibility studies proved that the material was nontoxic and had excellent biocompatibility with human mesenchymal stem cells. The synthetic route for these nanocomposites is interesting and may be helpful in the fabrication of various novel silica-based composites and in the exploitation of eco-friendly agricultural biomass. Our results revealed that these nanocomposites can be used in bone tissue engineering.     

A Novel Hybrid Optimization for Cluster‐Based Routing Protocol in Information-Centric Wireless Sensor Networks for IoT Based Mobile Edge Computing

Wireless Personal Communications - In present days, the utilization of mobile edge computing (MEC) and Internet of Things (IoT) in mobile networks offers a bottleneck in the evolving technological...