An Exploratory Study of Building Information Modelling Maturity in the Construction Industry

Nor’Aini; Siti Salwa Mohd; Rahma

doi:https://doi.org/10.54216/IJBES.010101

Journal of Cybersecurity and Information Management | 2019

Design of Optimal Machine Learning based Cybersecurity Intrusion Detection Systems

Cybersecurity is the process of protecting critical systems and confidential data from digital attacks. With the advent of machine learning, cybersecurity systems can examine the patterns and learns them from preventing similar attacks and responds to fluctuating behavior. Cybersecurity intrusion detection system helps to detect the existence of intrusions in the network and achieves security in confidential data storage and transmission. In this view, this study designs an efficient cockroach optimization (CSO) with kernel extreme learning machine (KELM) model for cybersecurity intrusion detection. The proposed CSO-KELM model can accomplish cybersecurity by the detection and classification of intrusions. The proposed CSO-KELM technique encompasses a three-level process, namely preprocessing, classification, and parameter tuning. The design of the CSO algorithm for the appropriate selection of KELM parameters results in improved classification performance. For examining the betterment of the CSO-KELM technique, a series of experiments were performed on benchmark datasets. The experimental results pointed out the superiority of the CSO-KELM technique concerning several measures.

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Andino Maseleno mail

link https://doi.org/10.54216/JCIM.000103

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Journal of Cybersecurity and Information Management | 2019

Performance Analysis of Machine Learning based Botnet Detection and Classification Models for Information Security

Botnet detection becomes a challenging issue in several domains like cybersecurity, finance, healthcare, law, order, etc. The botnet represents a set of cooperated Internet-linked devices managed by cyber criminals to start coordinated attacks and carry out different malicious events. As the botnets are seamlessly dynamic with the developing countermeasures presented by network and host-based detection schemes, conventional methods have failed to achieve enough safety for botnet threats. Therefore, machine learning (ML) models have been developed to detect and classify botnets for cybersecurity. In this view, this paper performs a comprehensive evaluation of different ML-based botnet detection and classification models. The botnet detection model involves a three-stage process, namely preprocessing, feature extraction, and classification. In this study, four ML models such as C4.5 Decision Tree, bagging, boosting, and Adaboost are employed for classification purposes. To highlight the performance of the four ML models, an extensive set of simulations was performed. The obtained results pointed out that the ML models can attain enhanced botnet detection performance. 

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Salah-ddine KRIT mail

link https://doi.org/10.54216/JCIM.000104

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Journal of Cybersecurity and Information Management | 2019

A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval

From the last decades, a massive quantity of images gets generated and continues to rise to a maximum extent in the forthcoming data. The process of retrieving images based on a query image (QI) is a proficient method of accessing the visual properties from large datasets. Content-based image retrieval (CBIR) provides a way of effectively retrieving images from large databases. At the same time, image encryption techniques can be integrated into the CBIR model to retrieve the images securely. Therefore, this paper presents new image encryption with a deep learning-based secure CBIR model called IEDL-SCBIR. The proposed IEDL-SCBIR technique intends to encrypt the images as well as securely retrieve them. The proposed IEDL-SCBIR technique follows a two-stage process: optimal elliptic curve cryptography (ECC) based encryption and DL based image retrieval. The proposed model derives a cuckoo search optimization (CSO) with the ECC technique for the image encryption process in which the CSO algorithm is applied for optimal key generation. In addition, VGG based feature extraction with Euclidean distance-based similarity measurement is applied for the retrieval process. To validate the enhanced performance of the IEDL-SCBIR technique, a comprehensive results analysis takes place, and the obtained results demonstrate the betterment over the other methods.

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Mohamed Elsharkawy mail -

Ahmed N. Al Masri mail

link https://doi.org/10.54216/JCIM.000105

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Journal of Intelligent Systems and Internet of Things | 2019

Intelligent Neighborhood Indexing Sequence Model for Healthcare Data Encoding

Recently, information security in the healthcare sector has become essential to ensure confidentiality in medical data. At the same time, automated disease diagnosis using deep learning (DL) models also gained considerable attention to accomplish enhanced classification performance.  This paper designs an intelligent neighborhood indexing sequence based on encoding with a classification model for healthcare information security (INISEC-HIS). The proposed INISEC-HIS technique aims to accomplish security in medical data transmission and diagnosis. The neighborhood indexing sequence (NIS) technique is applied to securely transmit the data, which transforms the medical data into an encoded format. Besides, a novel artificial fish swarm algorithm (AFSA) with deep neural networks (DNN) model is used for the classification process. The design of AFSA to optimally adjust the hyperparameters of the DNN model shows the study's novelty. An extensive simulation analysis takes place to examine the improved outcomes of the INISEC-HIS technique, and the obtained results highlighted the supremacy over the other techniques.

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Ibrahim M. EL-Hasnony mail -

Mohamed Elhoseny mail -

Mohammed K. Hassan mail

link https://doi.org/10.54216/JISIoT.000102

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Neutrosophic and Information Fusion | 2024

On the Classification of the Group of Units for Some Symbolic m-Plithogenic Rings

This paper is dedicated to study and to find the symbolic m-plithogenic units in many symbolic plithogenic rings for some special values of m, where we present a full classification of many different symbolic n-plithogenic group of units as direct products of well-known groups by building suitable and well-defined algebraic isomorphisms.

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Lee Xu mail

link https://doi.org/10.54216/NIF.040103

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Vol. Volume 4 / Iss. Issue 1

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American Journal of Business and Operations Research | 2019

Study on reasons of Failure of Small and Medium Enterprises: Looking into Egypt case

Globally, Small and Medium Size Enterprises (SMEs) are considered the main contributors to economic activities. In the European Union, SMEs account for around 67 percent of the overall employment by the private sector and were considered the cushion that protected the economy during the recent financial crisis in 2008 [2]. While in the USA, and according to the Small Business Administration and Small Business House, SMEs are responsible for more than half of the private sector non-farm GDP of the nation.  In the Middle East and North Africa (MENA) region, a recent study by the World Bank revealed that SMEs employ around 40 percent of the workforce in the formal sector (non-agriculture). This number would increase if the informal sector were included. Generally, SMEs are seen as the potential for economic development and a significant source for jobs creation, especially when looking into developing countries. In Egypt, with the declining role of the government being the primary employer until the nineties of last century, and the private sector taking over this role, and the fact that SMEs are the significant portion of the private sector, it is significantly essential to support SMEs for the creation of new jobs, and overall social stability.  Constrains facing SMEs are many and are usually different from those facing large businesses. It is also observed that rates of business failure within SMEs are generally higher than with large corporates. This paper aims to seek to identify the reasons behind the failure of SMEs, with a look into the Egyptian and Middle East situations. 

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Hazem Hanbal mail -

Saad Metawa mail

link https://doi.org/10.54216/AJBOR.000103

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Journal of Cybersecurity and Information Management | 2019

Mitigating DDoS Attacks in Wireless Sensor Networks using Heuristic Feature Selection with Deep Learning Model

A wireless sensor network (WSN) encompasses a massive set of sensors with limited abilities for gathering sensitive data. Since security is a significant issue in WSN, there is a possibility of different types of attacks. In Distributed Denial of Service (DDOS) attack, the malicious node can adapt to several attacks, namely flooding, black hole, warm hole, etc., to interrupt the working of the WSN. The recently developed deep learning (DL) models can effectively detect DDoS attacks in the network. Therefore, this article proposes a heuristic feature selection with a Deep Learning-based DDoS (HFSDL-DDoS) attack detection model in WSN. The proposed HFSDL-DDoS technique intends to identify and categorize the occurrence of DDoS attacks in WSN. In addition, the HFSDL-DDoS technique involves the immune clonal genetic algorithm (ICGA) based feature selection (FS) approach to improve the detection performance. Moreover, a fruit fly algorithm (FFA) with bidirectional long, short-term memory (BiLSTM) based classification model is employed. The experimental analysis of the HFSDL-DDoS technique is performed, and the results are examined interms of several performance measures. The resultant experimental results pointed out the betterment of the HFSDL-DDoS technique over the other techniques.

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Abdul Rahaman Wahab Sait mail -

Irina Pustokhina mail -

M. Ilayaraja mail

link https://doi.org/10.54216/JCIM.000106

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Journal of Intelligent Systems and Internet of Things | 2019

Modeling of Multiple Share Creation with Optimal Signcryption Technique for Digital Image Security

Digital image security plays an essential role in the shared communication model. Encryption and decryption process is commonly applied to securely transmit the images in various real-time applications. In addition, the generation of encryption/decryption keys is also essential to achieve enhanced image security. This study presents a multiple share creation scheme with an optimal signcryption (MSS-OSC) technique for digital image security. The MSS-OSC technique primarily generates a set of various shares for every digital image that needs to be transmitted. In addition, the encryption of generated shares takes place via the optimal signcryption (OSC) technique. Moreover, genetic programming (GP) is employed to optimally choose the keys involved in the encryption and decryption process. The detailed experimental validation of the MSS-OSC technique is investigated using a set of benchmark test images. The results analysis demonstrated that the MSS-OSC technique had a superior performance by accomplishing maximum digital image security.

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Abdul Rahaman Wahab Sait mail -

Irina Pustokhina mail -

M. Ilayaraja mail

link https://doi.org/10.54216/JISIoT.000103

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Journal of Intelligent Systems and Internet of Things | 2019

MSJEP Classifier: “Modified Strong Jumping Emerging Patterns” for Fast Efficient Mining and for handling attributes whose values are associated with taxonomies

Modified Strong Jumping Emerging Patterns (MSJEPs) are those itemsets whose support increases from zero in one data set to non-zero in the other dataset with support constraints greater than the minimum support threshold (ζ). The support constraint of MSJEP removes potentially less useful JEPs while retaining those with high discriminating power. Contrast Pattern (CP)-tree-based discovery algorithm used for SJEP mining is a main-memory-based method. When the data set is large, it is unrealistic to assume that the CP-tree can fit in the main memory. The main idea to handle this problem is to first partition the data set into a set of projected data sets and then for each projected data set, we construct and mine its corresponding CP-tree. Trees of the projected data sets are called Separated Contrast Pattern Tree “SCP-trees”  and Patterns generated from it are Called MSJEPs” Modified Strong Jumping Emerging Patterns”.  Our proposal also investigates the weakness of emerging patterns in handling attributes whose values are associated with taxonomies and proposes using an MSJEP classifier to achieve better accuracy, better speed, and also handling attributes in taxonomy.

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Mohammed K. Hassan mail -

Ahmed K. Hassan mail -

Ali I. Eldesouky mail

link https://doi.org/10.54216/JISIoT.000201

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Vol. Volume 0 / Iss. Issue 2

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International Journal of BIM and Engineering Science | 2018

An Exploratory Study of Building Information Modelling Maturity in the Construction Industry

Despite the benefits of Building Information Modelling (BIM), the adoption level of BIM remains much lower than expected. Construction companies should appraise the existing condition in the BIM implementation to ascertain the applicable progress avenues that fit the user’s traits. To achieve this aim, the objectives of this paper are i) to identify the trends of BIM maturity studies ii) to conceptualise what is BIM maturity; iii) to identify the existing models of BIM maturity iv) to identify the indicators for measuring BIM maturity in the company, the project and the industry. A systematic review was conducted on BIM maturity articles, published in the Scopus database from 2008 to April 2018. The results reveal that most BIM maturity studies are dominated by authors from the United Kingdom and the United States, but the top three authors highly-cited were from Australia, Canada and the United Kingdom. The results highlight four aspects in the conceptualisation of BIM maturity: quality of use, the extent of use, the context of use and stages of the processes. The four most frequently quoted BIM maturity models are the National BIM Standard Capability Maturity Model, BIM maturity, BIM proficiency matrix and BIM implementation models. The results revealed seven major indicators for assessing BIM maturity namely information, people, policy, process, technology, organisation and BIM output. The findings advance the practitioners’ understanding of important indicators that must be considered to initiate or increase the BIM maturity levels in their respective companies or projects.

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Nor’Aini Yusof mail -

Siti Salwa Mohd Ishak mail -

Rahma Doheim mail

link https://doi.org/10.54216/IJBES.010101

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Vol. Volume 1 / Iss. Issue 1

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