Sharing the electronic health data helps to increase the accuracy of the diagnoses and to improve the quality of health services. This shared data can also be used in medical research and can reduce medical costs. However, health data are fragmented across decentralized hospitals, this prevents data sharing and puts patients’ privacy at risks. In recent years, blockchain has revealed solutions that make life easier in many areas thanks to its distributed, safe and immutable structure. There are many blockchain-based studies in the literature on providing data privacy and sharing in different areas. In some studies, blockchain has been used with technologies such as cloud computing and cryptology. In the field of healthcare blockchain-based solutions are offered for the management and sharing of Electronic health records. In these solutions, private and consortium blockchain types are generally preferred and Public Key Infrastructure (PKI) and encryption are used for data privacy. Within the scope of this study, blockchain-based studies on the privacy preserving data sharing of health data were examined. In this paper, information about the studies in the literature and potential issues that can be studied in the future were discussed. In addition, information about current blockchain technologies such as smart contracts and PKI is also given.
{"title":"A Systematic Review of Privacy Preserving Healthcare Data Sharing on Blockchain","authors":"Mustafa Tanrıverdi","doi":"10.54216/jcim.040203","DOIUrl":"https://doi.org/10.54216/jcim.040203","url":null,"abstract":"Sharing the electronic health data helps to increase the accuracy of the diagnoses and to improve the quality of health services. This shared data can also be used in medical research and can reduce medical costs. However, health data are fragmented across decentralized hospitals, this prevents data sharing and puts patients’ privacy at risks. In recent years, blockchain has revealed solutions that make life easier in many areas thanks to its distributed, safe and immutable structure. There are many blockchain-based studies in the literature on providing data privacy and sharing in different areas. In some studies, blockchain has been used with technologies such as cloud computing and cryptology. In the field of healthcare blockchain-based solutions are offered for the management and sharing of Electronic health records. In these solutions, private and consortium blockchain types are generally preferred and Public Key Infrastructure (PKI) and encryption are used for data privacy. Within the scope of this study, blockchain-based studies on the privacy preserving data sharing of health data were examined. In this paper, information about the studies in the literature and potential issues that can be studied in the future were discussed. In addition, information about current blockchain technologies such as smart contracts and PKI is also given.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121126481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aldin Justin Sundararaj, K. Sagayam, Ahmed A. Elngar, A. Subash, B. Pillai
In this work, a kinetic heating simulation (KHS) facility has been designed, developed and the performance estimation is carried out in Propulsion and High enthalpy lab held at Karunya Institute of Technology and Sciences. The main objective of developing a KHS facility is to study the material characteristics high temperature paints at elevated temperatures. The Kinetic heating simulation facility is developed for 110 kW power rating. The current facility is designed to hold maximum of 105 Infrared lamps with each lamp having a power rating of 1kW. Ceramic lamps are used for heating the specimen. 15 lamps are placed in a bank and each bank can be controlled individually with the help of controlling unit. A total of 7 banks are used in operation of the kinetic heating simulation facility. To estimate the performance of the KHS facility K-type thermocouple are used for feedback as well as to measure temperature. The KHS also has provision for heat flux measurement. Preliminary studies are carried out to estimate the performance of KHS facility for various ranges
{"title":"Design, development and performance estimation of 110 kW kinetic heating simulation facilities for material studies–Phase I","authors":"Aldin Justin Sundararaj, K. Sagayam, Ahmed A. Elngar, A. Subash, B. Pillai","doi":"10.54216/jcim.050102","DOIUrl":"https://doi.org/10.54216/jcim.050102","url":null,"abstract":"In this work, a kinetic heating simulation (KHS) facility has been designed, developed and the performance estimation is carried out in Propulsion and High enthalpy lab held at Karunya Institute of Technology and Sciences. The main objective of developing a KHS facility is to study the material characteristics high temperature paints at elevated temperatures. The Kinetic heating simulation facility is developed for 110 kW power rating. The current facility is designed to hold maximum of 105 Infrared lamps with each lamp having a power rating of 1kW. Ceramic lamps are used for heating the specimen. 15 lamps are placed in a bank and each bank can be controlled individually with the help of controlling unit. A total of 7 banks are used in operation of the kinetic heating simulation facility. To estimate the performance of the KHS facility K-type thermocouple are used for feedback as well as to measure temperature. The KHS also has provision for heat flux measurement. Preliminary studies are carried out to estimate the performance of KHS facility for various ranges","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125548388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Saudi Aramco is the world’s leading oil producer based in Saudi Arabia. Around 1/10th of oil is exported from this organization to the world. Oil production is the major source of revenue for Saudi Arabia and its economy relies completely on it. The Shamoon virus attacked Saudi Aramco in August 2012. The country receives over 80% to 90% of total revenues from the exports of oil and contributes over 40% of the GDP [8]. Shamoon spread from the company's network and removed all of the hard drives. The company was limited only to office workstations and the software was not affected by the virus, due to which all technical operations could have been affected. It was the most disastrous cyber attack in the history of Saudi Arabia. Around 30,000 workstations had been infected by the virus. This paper also discusses the effects of Ransomware which recently attacked Aramco. Apart from that, we will also discuss some suggestions and security measures to prevent those attacks.
{"title":"Impact of Cyber Attack on Saudi Aramco","authors":"Mohammed. I I.alghamdi","doi":"10.5281/ZENODO.5172091","DOIUrl":"https://doi.org/10.5281/ZENODO.5172091","url":null,"abstract":"Saudi Aramco is the world’s leading oil producer based in Saudi Arabia. Around 1/10th of oil is exported from this organization to the world. Oil production is the major source of revenue for Saudi Arabia and its economy relies completely on it. The Shamoon virus attacked Saudi Aramco in August 2012. The country receives over 80% to 90% of total revenues from the exports of oil and contributes over 40% of the GDP [8]. Shamoon spread from the company's network and removed all of the hard drives. The company was limited only to office workstations and the software was not affected by the virus, due to which all technical operations could have been affected. It was the most disastrous cyber attack in the history of Saudi Arabia. Around 30,000 workstations had been infected by the virus. This paper also discusses the effects of Ransomware which recently attacked Aramco. Apart from that, we will also discuss some suggestions and security measures to prevent those attacks.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123039240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahmed A. Elngar, Artificial Intelligenc, Mohamed Arafa, Amar Fathy, Basma Moustafa, Omar Mahmoud, M. Shaban, Nehal Fawzy
Computer vision is one of the fields of computer science that is one of the most powerful and persuasive types of artificial intelligence. It is similar to the human vision system, as it enables computers to recognize and process objects in pictures and videos in the same way as humans do. Computer vision technology has rapidly evolved in many fields and contributed to solving many problems, as computer vision contributed to self-driving cars, and cars were able to understand their surroundings. The cameras record video from different angles around the car, then a computer vision system gets images from the video, and then processes the images in real-time to find roadside ends, detect other cars, and read traffic lights, pedestrians, and objects. Computer vision also contributed to facial recognition; this technology enables computers to match images of people’s faces to their identities. which these algorithms detect facial features in images and then compare them with databases. Computer vision also play important role in Healthcare, in which algorithms can help automate tasks such as detecting Breast cancer, finding symptoms in x-ray, cancerous moles in skin images, and MRI scans. Computer vision also contributed to many fields such as image classification, object discovery, motion recognition, subject tracking, and medicine. The rapid development of artificial intelligence is making machine learning more important in his field of research. Use algorithms to find out every bit of data and predict the outcome. This has become an important key to unlocking the door to AI. If we had looked to deep learning concept, we find deep learning is a subset of machine learning, algorithms inspired by structure and function of the human brain called artificial neural networks, learn from large amounts of data. Deep learning algorithm perform a task repeatedly, each time tweak it a little to improve the outcome. So, the development of computer vision was due to deep learning. Now we'll take a tour around the convolution neural networks, let us say that convolutional neural networks are one of the most powerful supervised deep learning models (abbreviated as CNN or ConvNet). This name ;convolutional ; is a token from a mathematical linear operation between matrixes called convolution. CNN structure can be used in a variety of real-world problems including, computer vision, image recognition, natural language processing (NLP), anomaly detection, video analysis, drug discovery, recommender systems, health risk assessment, and time-series forecasting. If we look at convolutional neural networks, we see that CNN are similar to normal neural networks, the only difference between CNN and ANN is that CNNs are used in the field of pattern recognition within images mainly. This allows us to encode the features of an image into the structure, making the network more suitable for image-focused tasks, with reducing the parameters required to set-up the model. One of th
{"title":"Image Classification Based On CNN: A Survey","authors":"Ahmed A. Elngar, Artificial Intelligenc, Mohamed Arafa, Amar Fathy, Basma Moustafa, Omar Mahmoud, M. Shaban, Nehal Fawzy","doi":"10.54216/jcim.060102","DOIUrl":"https://doi.org/10.54216/jcim.060102","url":null,"abstract":"Computer vision is one of the fields of computer science that is one of the most powerful and persuasive types of artificial intelligence. It is similar to the human vision system, as it enables computers to recognize and process objects in pictures and videos in the same way as humans do. Computer vision technology has rapidly evolved in many fields and contributed to solving many problems, as computer vision contributed to self-driving cars, and cars were able to understand their surroundings. The cameras record video from different angles around the car, then a computer vision system gets images from the video, and then processes the images in real-time to find roadside ends, detect other cars, and read traffic lights, pedestrians, and objects. Computer vision also contributed to facial recognition; this technology enables computers to match images of people’s faces to their identities. which these algorithms detect facial features in images and then compare them with databases. Computer vision also play important role in Healthcare, in which algorithms can help automate tasks such as detecting Breast cancer, finding symptoms in x-ray, cancerous moles in skin images, and MRI scans. Computer vision also contributed to many fields such as image classification, object discovery, motion recognition, subject tracking, and medicine. The rapid development of artificial intelligence is making machine learning more important in his field of research. Use algorithms to find out every bit of data and predict the outcome. This has become an important key to unlocking the door to AI. If we had looked to deep learning concept, we find deep learning is a subset of machine learning, algorithms inspired by structure and function of the human brain called artificial neural networks, learn from large amounts of data. Deep learning algorithm perform a task repeatedly, each time tweak it a little to improve the outcome. So, the development of computer vision was due to deep learning. Now we'll take a tour around the convolution neural networks, let us say that convolutional neural networks are one of the most powerful supervised deep learning models (abbreviated as CNN or ConvNet). This name ;convolutional ; is a token from a mathematical linear operation between matrixes called convolution. CNN structure can be used in a variety of real-world problems including, computer vision, image recognition, natural language processing (NLP), anomaly detection, video analysis, drug discovery, recommender systems, health risk assessment, and time-series forecasting. If we look at convolutional neural networks, we see that CNN are similar to normal neural networks, the only difference between CNN and ANN is that CNNs are used in the field of pattern recognition within images mainly. This allows us to encode the features of an image into the structure, making the network more suitable for image-focused tasks, with reducing the parameters required to set-up the model. One of th","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131718403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intrusion detection systems have been used in many systems to avoid malicious attacks. Traditionally, these intrusion detection systems use signature-based classification to detect predefined attacks and monitor the network's overall traffic. These intrusion detection systems often fail when an unseen attack occurs, which does not match with predefined attack signatures, leaving the system hopeless and vulnerable. In addition, as new attacks emerge, we need to update the database of attack signatures, which contains the attack information. This raises concerns because it is almost impossible to define every attack in the database and make the process costly also. Recently, research in conjunction with artificial intelligence and network security has evolved. As a result, it created many possibilities to enable machine learning approaches to detect the new attacks in network traffic. Machine learning has already shown successful results in the domain of recommendation systems, speech recognition, and medical systems. So, in this paper, we utilize machine learning approaches to detect attacks and classify them. This paper uses the CSE-CIC-IDS dataset, which contains normal and malicious attacks samples. Multiple steps are performed to train the network traffic classifier. Finally, the model is deployed for testing on sample data.
{"title":"An Artificial Intelligence-based Intrusion Detection System","authors":"Thani A. Almuhairi, Ahmad Almarri, Khalid Hokal","doi":"10.54216/jcim.07.02.04","DOIUrl":"https://doi.org/10.54216/jcim.07.02.04","url":null,"abstract":"Intrusion detection systems have been used in many systems to avoid malicious attacks. Traditionally, these intrusion detection systems use signature-based classification to detect predefined attacks and monitor the network's overall traffic. These intrusion detection systems often fail when an unseen attack occurs, which does not match with predefined attack signatures, leaving the system hopeless and vulnerable. In addition, as new attacks emerge, we need to update the database of attack signatures, which contains the attack information. This raises concerns because it is almost impossible to define every attack in the database and make the process costly also. Recently, research in conjunction with artificial intelligence and network security has evolved. As a result, it created many possibilities to enable machine learning approaches to detect the new attacks in network traffic. Machine learning has already shown successful results in the domain of recommendation systems, speech recognition, and medical systems. So, in this paper, we utilize machine learning approaches to detect attacks and classify them. This paper uses the CSE-CIC-IDS dataset, which contains normal and malicious attacks samples. Multiple steps are performed to train the network traffic classifier. Finally, the model is deployed for testing on sample data.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130725116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Health Insurance Portability and Accountability Act of 1996 was brought in to serve as a legislation that could essentially assist in reorganizing the flow of healthcare information, prescribing how sensitive medical data stored with healthcare/insurance firms should be protected from stealing and tampering. It has served as a pioneer in the world of privacy in healthcare and set one of the earliest benchmarks for any legal instruments regarding the storing and dissemination of medical information in the form of electronic health records. The HITECH act of 2009 and the HIPAA omnibus rule of 2013 further cemented the use of standardized frameworks which can help control, reduce and track any possible breaches of confidentiality and integrity of such personal information. This paper explores the content, reasoning, and timeline of the HIPAA act and the impact it creates on the health information technology sector. It also explains the challenges that are faced in the implementation of the policy and gives a holistic perspective of the rights and responsibilities of each stakeholder involved.
1996年的《健康保险流通与责任法案》(Health Insurance Portability and Accountability Act)是一项可以从根本上帮助重组医疗保健信息流的立法，规定了如何保护存储在医疗保健/保险公司的敏感医疗数据不被窃取和篡改。它是保健领域隐私领域的先驱，并为以电子健康记录形式存储和传播医疗信息的任何法律文书设定了最早的基准之一。2009年的HITECH法案和2013年的HIPAA综合规则进一步巩固了标准化框架的使用，这些框架可以帮助控制、减少和跟踪任何可能违反此类个人信息机密性和完整性的行为。本文探讨了HIPAA法案的内容、推理和时间表，以及它对卫生信息技术部门的影响。它还解释了在政策实施过程中所面临的挑战，并对所涉及的每个利益相关者的权利和责任进行了全面的分析。
{"title":"A review into the evolution of HIPAA in response to evolving technological environments","authors":"Abhishek P. Patil, Neelika Chakrabarti","doi":"10.5281/ZENODO.4014219","DOIUrl":"https://doi.org/10.5281/ZENODO.4014219","url":null,"abstract":"The Health Insurance Portability and Accountability Act of 1996 was brought in to serve as a legislation that could essentially assist in reorganizing the flow of healthcare information, prescribing how sensitive medical data stored with healthcare/insurance firms should be protected from stealing and tampering. It has served as a pioneer in the world of privacy in healthcare and set one of the earliest benchmarks for any legal instruments regarding the storing and dissemination of medical information in the form of electronic health records. The HITECH act of 2009 and the HIPAA omnibus rule of 2013 further cemented the use of standardized frameworks which can help control, reduce and track any possible breaches of confidentiality and integrity of such personal information. This paper explores the content, reasoning, and timeline of the HIPAA act and the impact it creates on the health information technology sector. It also explains the challenges that are faced in the implementation of the policy and gives a holistic perspective of the rights and responsibilities of each stakeholder involved.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115161768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data mining is a technique that is applied to mine valuable information from the rough data. A prediction analysis is an approach that has the potential for forecasting future possibilities based on the recent data. The CCFD is the challenge of prediction in which fraudulent transactions are predicted based on certain rules. There are several stages included in the detection of fraud in credit cards. Various classification algorithms are reviewed with respect to the performance analysis in order to detect fraud in the credit card. The performance is measured with regard to precision.
{"title":"Analysis of Various Credit Card Fraud Detection Techniques","authors":"A. Admin","doi":"10.54216/jcim.050202","DOIUrl":"https://doi.org/10.54216/jcim.050202","url":null,"abstract":"Data mining is a technique that is applied to mine valuable information from the rough data. A prediction analysis is an approach that has the potential for forecasting future possibilities based on the recent data. The CCFD is the challenge of prediction in which fraudulent transactions are predicted based on certain rules. There are several stages included in the detection of fraud in credit cards. Various classification algorithms are reviewed with respect to the performance analysis in order to detect fraud in the credit card. The performance is measured with regard to precision.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"875 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123026444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud computing has become a popular paradigm for delivering computing resources and services over the internet. However, the adoption of cloud computing also brings new security challenges and risks, including data breaches, insider attacks, and unauthorized access. Therefore, it is critical to have a comprehensive information security management framework to address these challenges and ensure the security and privacy of cloud computing environments. This paper proposes a machine learning (ML) based information security management (ISM) framework for cloud computing environments that integrates best practices and standards from various domains, including cloud computing, information security, and risk management. The proposed framework includes residual recurrent network to effectively discriminate different patterns of cloud security attacks. The proposed framework emphasizes the importance of threat detection, security controls, and continuous monitoring and improvement. The framework is designed to be flexible and scalable, allowing organizations to tailor it to their specific needs and requirements.
{"title":"Information Security Management Framework for Cloud Computing Environments","authors":"Manal M. .., S. M. Hebrisha","doi":"10.54216/jcim.110102","DOIUrl":"https://doi.org/10.54216/jcim.110102","url":null,"abstract":"Cloud computing has become a popular paradigm for delivering computing resources and services over the internet. However, the adoption of cloud computing also brings new security challenges and risks, including data breaches, insider attacks, and unauthorized access. Therefore, it is critical to have a comprehensive information security management framework to address these challenges and ensure the security and privacy of cloud computing environments. This paper proposes a machine learning (ML) based information security management (ISM) framework for cloud computing environments that integrates best practices and standards from various domains, including cloud computing, information security, and risk management. The proposed framework includes residual recurrent network to effectively discriminate different patterns of cloud security attacks. The proposed framework emphasizes the importance of threat detection, security controls, and continuous monitoring and improvement. The framework is designed to be flexible and scalable, allowing organizations to tailor it to their specific needs and requirements.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129729405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Fadhil, Noor Q. Makhool, Muna M. Hummady, Z. O. Dawood
Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet detection for information security. For effectual recognition of botnets, the proposed model involves data pre-processing at the initial stage. Besides, the model is utilized for the identification and classification of botnets that exist in the network. In order to optimally adjust the SVM parameters, the DFA is utilized and consequently resulting in enhanced outcomes. The presented model has the ability in accomplishing improved botnet detection performance. A wide-ranging experimental analysis is performed and the results are inspected under several aspects. The experimental results indicated the efficiency of our model over existing methods.
{"title":"Machine Learning-based Information Security Model for Botnet Detection","authors":"H. Fadhil, Noor Q. Makhool, Muna M. Hummady, Z. O. Dawood","doi":"10.54216/jcim.090106","DOIUrl":"https://doi.org/10.54216/jcim.090106","url":null,"abstract":"Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet detection for information security. For effectual recognition of botnets, the proposed model involves data pre-processing at the initial stage. Besides, the model is utilized for the identification and classification of botnets that exist in the network. In order to optimally adjust the SVM parameters, the DFA is utilized and consequently resulting in enhanced outcomes. The presented model has the ability in accomplishing improved botnet detection performance. A wide-ranging experimental analysis is performed and the results are inspected under several aspects. The experimental results indicated the efficiency of our model over existing methods.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129171641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Internet of Things (IoT) has become a hot popular topic for building a smart environment. At the same time, security and privacy are treated as significant problems in the real-time IoT platform. Therefore, it is highly needed to design intrusion detection techniques for accomplishing security in IoT. With this motivation, this study designs a novel flower pollination algorithm (FPA) based feature selection with a gated recurrent unit (GRU) model, named FPAFS-GRU technique for intrusion detection in the IoT platform. The proposed FPAFS-GRU technique is mainly designed to determine the presence of intrusions in the network. The FPAFS-GRU technique involves the design of the FPAFS technique to choose an optimal subset of features from the networking data. Besides, a deep learning based GRU model is applied as a classification tool to identify the network intrusions. An extensive experimental analysis takes place on KDDCup 1999 dataset, and the results are investigated under different dimensions. The resultant simulation values demonstrated the betterment of the FPAFS-GRU technique with a higher detection rate of 0.9976.
{"title":"Modeling of Deep Learning based Intrusion Detection System in Internet of Things Environment","authors":"M. Hammoudeh, Saeed M. Aljaberi","doi":"10.54216/jcim.080102","DOIUrl":"https://doi.org/10.54216/jcim.080102","url":null,"abstract":"The Internet of Things (IoT) has become a hot popular topic for building a smart environment. At the same time, security and privacy are treated as significant problems in the real-time IoT platform. Therefore, it is highly needed to design intrusion detection techniques for accomplishing security in IoT. With this motivation, this study designs a novel flower pollination algorithm (FPA) based feature selection with a gated recurrent unit (GRU) model, named FPAFS-GRU technique for intrusion detection in the IoT platform. The proposed FPAFS-GRU technique is mainly designed to determine the presence of intrusions in the network. The FPAFS-GRU technique involves the design of the FPAFS technique to choose an optimal subset of features from the networking data. Besides, a deep learning based GRU model is applied as a classification tool to identify the network intrusions. An extensive experimental analysis takes place on KDDCup 1999 dataset, and the results are investigated under different dimensions. The resultant simulation values demonstrated the betterment of the FPAFS-GRU technique with a higher detection rate of 0.9976.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123930895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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