Set Of Advanced Features Research Articles

Design and Development of Cybersecurity Suite

Protecting personal, corporate and government data in the digital age requires cybersecurity. Traditional cybersecurity methods sometimes lack comprehensive and dynamic protection mechanisms as cyber-attacks change and become more sophisticated. A new cybersecurity suite is proposed in this study. It integrates various advanced security technologies into a single user-friendly platform interaction. This paper specifically combines techniques for real-time phishing detection, strong password management, safe encryption and decryption services and an interactive module to promote user awareness and behaviours to improve digital security for individuals and organization. Rapid prototyping is used in iterative and incremental development phases to adapt to changing security needs and user input. This method allows the platform to be constantly improved to satisfy the current cybersecurity standards and react to new threats. The prototyped developed provides proactive digital defence, a complete cybersecurity platform that equips users to defend their digital presence. With the intuitive design and advanced feature set, the prototype aspires to democratize cybersecurity. Empirical testing of the prototype revealed that makes an effective protection of a wider audience without the complexities of security software. This paper contributes in the advances cybersecurity technology and shaping the digital protection measures.

A Heterogeneous Machine Learning Ensemble Framework for Malicious Webpage Detection

The growing dependence on digital systems has heightened the risks posed by cybersecurity threats. This paper proposes a new method for detecting malicious webpages among several adversary activities. As shown in previous studies, malicious URL detection performance is significantly affected by the learning dataset features. The overall performance of different machine learning models varies depending on the data features, and using a particular model alone is not always desirable in any given environment. To address these limitations, we propose an ensemble approach using different machine learning models. Our proposed method outperforms the existing single model by 6%, allowing for the detection of an additional 141 malicious URLs. In this study, repetitive tasks are automated, improving the performance of different machine learning models. In addition, the proposed framework builds an advanced feature set based on URL and web content and includes the most optimized detection model structure. The proposed technology can contribute to define an advanced feature set based on URL and web content and includes the most optimized detection model structure and research on automated technology for the detection of malicious websites, such as phishing websites and malicious code distribution.

Simple descriptor based machine learning model development for synergy prediction of different metal loadings and solvent swellings on coal pyrolysis

The co-effect of solvent swelling and metal loading on coal pyrolysis was investigated through statistical analysis and machine learning. The distributions and properties of pyrolysis products, and the pyrolysis parameters were all considered. 22 targets were screened out by analysis of variance (ANOVA). Both linear and non-linear regression models aiming to predict these values were constructed, in which the swelling ratio and atomic descriptors collected from handbooks were taken as inputs. The symbolic transformation (ST) algorithm was involved to assemble a new feature and the model built on the advanced feature set displays higher prediction accuracy for all targets. The result of leave-one-out cross validation shows acceptable performance(R2 > 0.8) for most targets (19 of 22), and good performance (R2 > 0.9) for half of them (12 of 22). The importance of ST feature was verified, and the contribution of each single feature was clearly reflected in the formula of ST.

IoMT-based computational approach for detecting brain tumor

Internet of medical things (IoMT) is gaining enormous attraction from the healthcare research community. In IoMT, vital health-related information is gathered by medical devices with the help of Internet. Profound supportive data is provided to patients to muddle through their recuperations. However, because of numerous medical equipment, the addresses of devices may be changed by intruders, which is a life threat to serious patients, e.g., the ones having brain tumor-like serious diseases. The mass of irregular cells inside the brain results in brain tumor, which can damage the brain and is life-threatening. Identifying brain tumors in initial stages is quite crucial for its diagnosis, prognosis, and treatment. Conventional methods used for the detection is biopsy and examination of CT scans or magnetic resonance imaging (MRI) by a human, which is a tedious job, impractical for large number of data, and requires the time of radiologist for making inferences. To cope with these challenges, various automated techniques have been proposed. However, still there is a great need for the development of a method that may detect a brain tumor with significant accuracy in less time. Moreover, the selection of features set for making prediction is also very important to achieve remarkable accuracy. In this study, Partial Tree (PART) — an association rule learner, with advanced feature set is adopted to detect brain tumors with respect to its grade, i.e., grade I through grade IV. The proposed model is validated through 10-fold cross validation and is compared with the existing methods, i.e., CART, Random Forest, Naive Bayes, and Random Tree. The obtained results reveal that partial tree with advanced feature set supersedes the aforementioned techniques. Precision, recall, and F-measure are the utilized performance measures for evaluation.

Analysis for Disease Gene Association Using Machine Learning

To recognize the basis of disease, it is essential to determine its underlying genes. Understanding the association between underlying genes and genetic disease is a fundamental problem regarding human health. Identification and association of genes with the disease require time consuming and expensive experimentations of a great number of potential candidate genes. Therefore, the alternative inexpensive and rapid computational methods have been proposed that can identify the candidate gene associated with a disease. Most of these methods use phenotypic similarities due to the fact that genes causing same or similar diseases have less variation in their sequence or network properties of protein-protein interactions based on-premises that genes lie closer in protein interaction network that causes the similar or same disease. However, these methods use only basic network properties or topological features and gene sequence information or biological features as a prior knowledge for identification of gene-disease association, which restricts the identification process to a single gene-disease association. In this study, we propose and analyze some novel computational methods for the identification of genes associated with diseases. Some advance topological and biological features that are overlooked currently are introducing for identifying candidate genes. We evaluate different computational methods on disease-gene association data from DisGeNET in a 10-fold cross-validation mode based on TP rate, FP rate, precision, recall, F-measure, and ROC curve evaluation parameters. The results reveal that various computational methods with advanced feature set outperform previous state-of-the-art techniques by achieving precision up to 93.8%, recall up to 93.1%, and F- measure up to 92.9%. Significantly, we apply our methods to study four major diseases: Thalassemia, Diabetes, Malaria, and Asthma. Simulation results show that the proposed Deep Extreme Learning Machine (DELM) gives more accurate results as compared to previously published approaches.

Enabling Digital Grid for Industrial Revolution: Self-Healing Cyber Resilient Platform

Driving digital grid development to provide sustainable, reliable and secure network systems requires deploying a variety of applications against any potential cyber attacks. Therefore, there is an urgent demand to accelerate the development of an intelligent Software-Defined Networking (SDN) platform that can address the tremendous challenges of data protection for digital resiliency. Modern grid technology tends to adopt distributed SDN controllers for further slicing power grid domain and protect the boundaries of electric data at network edges. To facilitate these techniques, this article proposes an intelligent secure SDN controller for supporting digital grid resiliency, considering management coordination capability, self-healing features and recovery of network traffic forwarding during service interruptions. A set of advanced features are employed in grid controllers to configure the network elements in response to possible disasters or link failures. In addition, various SDN topology scenarios are introduced for efficient coordination and configurations of network domains. Finally, to justify the potential advantages of an intelligent secure SDN system, a case study is presented to evaluate the requirements of secure digital modern grid networks and pave the path toward the next phase of industry revolution.

Classifying running speed conditions using a single wearable sensor: Optimal segmentation and feature extraction methods

Accelerometers have been used to classify running patterns, but classification accuracy and computational load depends on signal segmentation and feature extraction. Stride-based segmentation relies on identifying gait events, a step avoided by using window-based segmentation. For each segment, discrete points can be extracted from the accelerometer signal, or advanced features can be computed. Therefore, the purpose of this study was to examine how different segmentation and feature extraction methods influence the accuracy and computational load of classifying running conditions. Forty-four runners ran at their preferred speed and 25% faster than preferred while an accelerometer at the lower back recorded 3D accelerations. Computational load was determined as the accelerometer signal was segmented into single and five strides, and corresponding small and large windows, with discrete points extracted from the single stride segments and advanced features computed from all four segment types. Each feature set was used to classify speed conditions and classification accuracy was recorded. Computational load and classification accuracy were compared across all feature sets using a repeated-measures MANOVA, with follow-up t-tests to compare feature type (discrete vs. advanced), segmentation method (stride- vs. window-based), and segment size (small vs. large), using a Bonferroni-adjusted α = 0.003. The five-stride (97.49 (±4.57)%) and large-window advanced (97.23 (±5.51)%) feature sets produced the greatest classification accuracy, but the large-window advanced feature set had a lower computational load (0.0041 (±0.0002)s) than the stride-based feature sets. Therefore, using a few advanced features and large overlapping window sizes yields the best performance of both classification accuracy and computational load.

Stream control transmission protocol: Past, current, and future standardization activities

The Stream Control Transmission Protocol (SCTP) is a general-purpose transport layer protocol providing a service similar to TCP - plus a set of advanced features to utilize the enhanced capabilities of modern IP networks and to support increased application requirements. Nowadays, there are SCTP implementations for all major operating systems. While SCTP was standardized as an RFC several years ago, there is still significant ongoing work within the IETF to discuss and standardize further features in the form of protocol extensions. In this article, we first introduce the SCTP base protocol and already standardized extensions. After that, we focus on the ongoing SCTP standardization progress in the IETF and give an overview of activities and challenges in the areas of security and concurrent multipath transport.

The multimedia set-top-box based on PC platform

A comprehensive research on key issues of the multimedia set-top-box used in active TV is conducted. Meanwhile a practical architecture for the multimedia set-top-box based on PC platform is designed. The architecture, with its advanced feature set, high integration and PCI interface is well suited to bring the set-top-box experience to consumers at an affordable price, and makes it very attractive for the consumer.

CIM-OSA Part 1: total enterprise modelling and function view

Abstract The objective of Computer-Integrated Manufacturing (CIM) is the appropriate integration of enterprise operations by means of efficient information exchange within the enterprise with the help of Information Technology (IT). Integration includes the physical and logical connection of processes by means of data communications technology operating to specified standards, but also the integration of enterprise functions as welt as enterprise information. Generalized models and an open systems architecture are required to reduce the system complexity to a manageable level. They are used to identify the principal components, processes, constraints and information sources used to describe a manufacturing enterprise progressing towards CIM. In this paper, the basic concepts of an open-systems architecture for CIM called CIM-OSA are presented. The function view of the CIM-OSA modelling framework is discussed. CIM-OSA provides a unique set of advanced features to model functionality and behaviour of CIM systems at three distinct levels (requirements definition, design specification and implementation description)