Rise of big data – issues and challenges

The recent rapid rise in the availability of big data due to Internet-based technologies such as social media platforms and mobile devices has left many market leaders unprepared for handling very large, random and high velocity data. Conventionally, technologies are initially developed and tested in labs and appear to the public through media such as press releases and advertisements. These technologies are then adopted by the general public. In the case of big data technology, fast development and ready acceptance of big data by the user community has left little time to be scrutinized by the academic community. Although many books and electronic media articles are published by professionals and authors for their work on big data, there is still a lack of fundamental work in academic literature. Through survey methods, this paper discusses challenges in different aspects of big data, such as data sources, content format, data staging, data processing, and prevalent data stores. Issues and challenges related to big data, specifically privacy attacks and counter-techniques such as k-anonymity, t-closeness, l-diversity and differential privacy are discussed. Tools and techniques adopted by various organizations to store different types of big data are also highlighted. This study identifies different research areas to address such as a lack of anonymization techniques for unstructured big data, data traffic pattern determination for developing scalable data storage solutions and controlling mechanisms for high velocity data

E‐ART: a new encryption algorithm based on the reflection of binary search tree

Data security has become crucial to most enterprise and government applications due to the increasing amount of data generated, collected, and analyzed. Many algorithms have been developed to secure data storage and transmission. However, most existing solutions require multi-round functions to prevent differential and linear attacks. This results in longer execution times and greater memory consumption, which are not suitable for large datasets or delay-sensitive systems. To address these issues, this work proposes a novel algorithm that uses, on one hand, the reflection property of a balanced binary search tree data structure to minimize the overhead, and on the other hand, a dynamic offset to achieve a high security level. The performance and security of the proposed algorithm were compared to Advanced Encryption Standard and Data Encryption Standard symmetric encryption algorithms. The proposed algorithm achieved the lowest running time with comparable memory usage and satisfied the avalanche effect criterion with 50.1%. Furthermore, the randomness of the dynamic offset passed a series of National Institute of Standards and Technology (NIST) statistical tests

E-ART: A New Encryption Algorithm Based on the Reflection of Binary Search Tree

Data security has become crucial to most enterprise and government applications due to the increasing amount of data generated, collected, and analyzed. Many algorithms have been developed to secure data storage and transmission. However, most existing solutions require multi-round functions to prevent differential and linear attacks. This results in longer execution times and greater memory consumption, which are not suitable for large datasets or delay-sensitive systems. To address these issues, this work proposes a novel algorithm that uses, on one hand, the reflection property of a balanced binary search tree data structure to minimize the overhead, and on the other hand, a dynamic offset to achieve a high security level. The performance and security of the proposed algorithm were compared to Advanced Encryption Standard and Data Encryption Standard symmetric encryption algorithms. The proposed algorithm achieved the lowest running time with comparable memory usage and satisfied the avalanche effect criterion with 50.1%. Furthermore, the randomness of the dynamic offset passed a series of National Institute of Standards and Technology (NIST) statistical tests

ENTERPRISE RESOURCE PLANNING FOR COMPETITVE ADVANTAGE

Enterprise Resource Planning (ERP) software vendor says that ERP has become a source of the competitive advantages for companies around the world and dynamic force driving the process of global integration through information. However, many argue that ERP provide base commonalty across the industries as it is available for many and its benefits have been realized by many firms. This paper asks who is right.&nbsp

Blockchain-Assisted Machine Learning with Hybrid Metaheuristics-Empowered Cyber Attack Detection and Classification Model

Cyber attack detection is the process of detecting and responding to malicious or unauthorized activities in networks, computer systems, and digital environments. The objective is to identify these attacks early, safeguard sensitive data, and minimize the potential damage. An intrusion detection system (IDS) is a cybersecurity tool mainly designed to monitor system activities or network traffic to detect and respond to malicious or suspicious behaviors that may indicate a cyber attack. IDSs that use machine learning (ML) and deep learning (DL) have played a pivotal role in helping organizations identify and respond to security risks in a prompt manner. ML and DL techniques can analyze large amounts of information and detect patterns that may indicate the presence of malicious or cyber attack activities. Therefore, this study focuses on the design of blockchain-assisted hybrid metaheuristics with a machine learning-based cyber attack detection and classification (BHMML-CADC) algorithm. The BHMML-CADC method focuses on the accurate recognition and classification of cyber attacks. Moreover, the BHMML-CADC technique applies Ethereum BC for attack detection. In addition, a hybrid enhanced glowworm swarm optimization (HEGSO) system is utilized for feature selection (FS). Moreover, cyber attacks can be identified with the design of a quasi-recurrent neural network (QRNN) model. Finally, hunter–prey optimization (HPO) algorithm is used for the optimal selection of the QRNN parameters. The experimental outcomes of the BHMML-CADC system were validated on the benchmark BoT-IoT dataset. The wide-ranging simulation analysis illustrates the superior performance of the BHMML-CADC method over other algorithms, with a maximum accuracy of 99.74%

ARTPHIL: reversible de-identification of free-text using an integrated model

Organisations that collect and maintain individual data face the challenge of preserving privacy and security when using, archiving, or sharing these data. De-identification tools are essential for minimising the privacy risk. However, current data de-identification and anonymisation methods are widely used to alter the original data in a way that cannot be recovered. This results in data distortion and, hence, the substantial loss of knowledge within the data. To address this issue, this paper introduces the concept of reversible data de-identification methods to de-identify unstructured health data under the Health Insurance Portability and Accountability Act (HIPAA) guidelines. The model integrates Philter [9], the state-of-the-art tool for extracting personal identifiers from free-text, to detect confidential information and encrypt them with E-ART, lightweight encryption algorithm E-ART [10]. The performance of the proposed model ARTPHIL is evaluated using i2b2 data corpus in terms of recall, precision, F-measure and execution time. The results of the experiment are consistent with the recent de-identification method with recall of 96.93%. More importantly, the original data can be recovered, if needed, and authenticated

Human-Based Interaction Analysis via Automated Key Point Detection and Neural Network Model

The human interaction with an object is one of the most challenging domains in real-life applications, such as smart homes, surveillance, medical, education, safety-based application of computer vision, and artificial intelligence. In this research article, we have proposed a framework for human and object interaction in real-life examples such as sports and other activities. Initially, we reviewed video-based data by considering the three state-of-the-art data sets. Preprocessing steps have been followed to avoid extra costs, such as video-to-frame conversion, noise reduction and background subtraction. Human silhouette extraction has been performed via the Gaussian mixture model (GMM) and supper pixel model. Next, human body points and object location detection were performed. Finally, human and object-based features have been extracted. To minimize the features replication and to achieve optimized results, we have applied stochastic gradient descent and Restricted Boltzmann Machine; As a result, we have achieved an accuracy of 88.46%, 82.00%, and 88.30% on human body parts recognition over the MPII dataset, UCF_aerial dataset, and wild Dataset respectively. The classification accuracy for the MPII dataset is 92.71%, for the UCF_aerial dataset is 90.60%, and for sports video in the wild Dataset is 92.42%. We have achieved a high accuracy rate compared to other state-of-the-art methods and frameworks due to the complex feature extraction and optimization approach

Hand Gesture Recognition for Characters Understanding Using Convex Hull Landmarks and Geometric Features

With the latest advancements, hand gesture recognition is becoming an effective way of communication and gaining popularity from a research point of view. Hearing impaired people around the world need assistance, while sign language is only understood by a few people around the globe. It becomes challenging for untrained people to communicate easily, research community has tried to train systems with a variety of models to facilitate communication with hearing impaired people and also human-computer interaction. Researchers have detected gestures with numerous recognition rates; however, the recognition rate still needs improvement. As the images captured via cameras possess multiple issues, the light intensity variation makes it a challenging task to extract gestures from such images, extra information in captured images, such as noise hinders the computation time, and complex backgrounds make the extraction of gestures difficult. A novel approach is proposed in this paper for character detection and recognition. The proposed system is divided into five steps for hand gesture recognition. Firstly, images are pre-processed to reduce noise and intensity is adjusted. The pre-processed images region of interest is detected via directional images. After hand extraction, landmarks are extracted via a convex hull. Each gesture is used to extract geometric features for the proposed hand gesture recognition (HGR) system. The extracted features helped in gesture detection and recognition via the Convolutional Neural Network (CNN) classifier. The proposed approach experimentation result demonstrated over the MNIST dataset achieved a gesture recognition rate of 93.2% and 90.2% with one-third and two-third training validation systems, respectively. Also, the proposed system performance is validated on the ASL dataset, giving accuracy of 91.6% and 88.14% with one-third and two-third training validation systems, respectively. The proposed system is also compared with other conventional systems. Different emerging domains such as human-computer interaction (HCI), human-robot interaction (HRI), and virtual reality (VR) are applicable to the proposed system to fill the communication gap