Mohammed I. Alghamdi, Abeer. Y. Salawi, Salwa Alghamdi
Software defined networks (SDN) remain a hot research field as it provides controllable networking operations. The SDN controller can be treated as the operating system of the SDN model and it holds the responsibility of performing different networking applications. Despite the benefits of SDN, security remains a challenging problem. At the same time, distributed denial of services (DDoS) is a typical attack on SDN owing to centralized architecture, especially at the control layer of the SDN. This article develops a new Cat Swarm Optimization with Fuzzy Rule Base Classification (CSO-FRBCC) model for cybersecurity in SDN. The presented CSO-FRBCC model intends to effectually categorize the occurrence of DDoS attacks in SDN. To achieve this, the CSO-FRBCC model primarily pre-processes the input data to transform it to a uniform format. Besides, the CSO-FRBCC model employs FRBCC classifier for the recognition and classification of intrusions. Moreover, the parameter optimization of the FRBCC classification model is adjusted by the use of cat swarm optimization (CSO) algorithm which results in improved performance. A comprehensive set of simulations were carried out on benchmark dataset and the results highlighted the enhanced outcomes of the CSO-FRBCC model over the other recent approaches.
软件定义网络(SDN)由于能够提供可控的网络操作，一直是研究的热点。SDN控制器可以看作是SDN模型的操作系统，它负责执行不同的网络应用程序。尽管SDN有很多好处，但安全性仍然是一个具有挑战性的问题。同时，分布式拒绝服务(DDoS, distributed denial of services)是针对SDN的一种典型攻击方式，其集中式架构尤其体现在SDN的控制层。本文提出了一种新的基于模糊规则基分类的Cat群优化(CSO-FRBCC)网络安全模型。本文提出的CSO-FRBCC模型旨在对SDN中DDoS攻击的发生进行有效的分类。为了实现这一点，CSO-FRBCC模型主要对输入数据进行预处理，将其转换为统一的格式。此外，CSO-FRBCC模型采用FRBCC分类器对入侵进行识别和分类。此外，采用猫群优化(CSO)算法对FRBCC分类模型的参数优化进行调整，提高了分类性能。在基准数据集上进行了一组全面的模拟，结果突出了CSO-FRBCC模型优于其他最新方法的结果。
{"title":"Smart Model for Securing Software Defined Networks","authors":"Mohammed I. Alghamdi, Abeer. Y. Salawi, Salwa Alghamdi","doi":"10.54216/jcim.0100101","DOIUrl":"https://doi.org/10.54216/jcim.0100101","url":null,"abstract":"Software defined networks (SDN) remain a hot research field as it provides controllable networking operations. The SDN controller can be treated as the operating system of the SDN model and it holds the responsibility of performing different networking applications. Despite the benefits of SDN, security remains a challenging problem. At the same time, distributed denial of services (DDoS) is a typical attack on SDN owing to centralized architecture, especially at the control layer of the SDN. This article develops a new Cat Swarm Optimization with Fuzzy Rule Base Classification (CSO-FRBCC) model for cybersecurity in SDN. The presented CSO-FRBCC model intends to effectually categorize the occurrence of DDoS attacks in SDN. To achieve this, the CSO-FRBCC model primarily pre-processes the input data to transform it to a uniform format. Besides, the CSO-FRBCC model employs FRBCC classifier for the recognition and classification of intrusions. Moreover, the parameter optimization of the FRBCC classification model is adjusted by the use of cat swarm optimization (CSO) algorithm which results in improved performance. A comprehensive set of simulations were carried out on benchmark dataset and the results highlighted the enhanced outcomes of the CSO-FRBCC model over the other recent approaches.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"30 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":"122786785","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 secure transmission of medical data is crucial for the protection of patients' privacy and confidentiality. With the advent of IoT in healthcare, medical data is being transmitted over networks that are vulnerable to cyberattacks. Therefore, there is an urgent need for lightweight yet secure encryption algorithms that can protect medical data in transit. In this paper, we propose an integrated Chaotic-GIFT algorithm for lightweight and robust encryption of medical data transmitted over IoT networks. The proposed algorithm combines the chaos theory with a lightweight block cipher to provide secure and efficient encryption of medical data. The Chaotic-GIFT algorithm employs bit-level shuffling and substitution of medical images to provide encryption, while the chaotic sequence generated by the logistic map is used as the cryptographic key for added security. The proposed Chaotic-GIFT algorithm provides a lightweight and efficient solution for the secure transmission of medical data over IoT networks. Evaluation of the algorithm's effectiveness was conducted using multiple metrics including encryption and decryption time, throughput, avalanche effect, non-linearity analysis, and correlation coefficient.
{"title":"Protecting Medical Data on the Internet of Things with an Integrated Chaotic-GIFT Lightweight Encryption Algorithm","authors":"H. Fadhil, M. Elhoseny, B. M. Mushgil","doi":"10.54216/jcim.120105","DOIUrl":"https://doi.org/10.54216/jcim.120105","url":null,"abstract":"The secure transmission of medical data is crucial for the protection of patients' privacy and confidentiality. With the advent of IoT in healthcare, medical data is being transmitted over networks that are vulnerable to cyberattacks. Therefore, there is an urgent need for lightweight yet secure encryption algorithms that can protect medical data in transit. In this paper, we propose an integrated Chaotic-GIFT algorithm for lightweight and robust encryption of medical data transmitted over IoT networks. The proposed algorithm combines the chaos theory with a lightweight block cipher to provide secure and efficient encryption of medical data. The Chaotic-GIFT algorithm employs bit-level shuffling and substitution of medical images to provide encryption, while the chaotic sequence generated by the logistic map is used as the cryptographic key for added security. The proposed Chaotic-GIFT algorithm provides a lightweight and efficient solution for the secure transmission of medical data over IoT networks. Evaluation of the algorithm's effectiveness was conducted using multiple metrics including encryption and decryption time, throughput, avalanche effect, non-linearity analysis, and correlation coefficient.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"8 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":"114226236","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 growing ubiquity of IoT-enabled devices in recent years emphasizes the critical need to strengthen transportation network safety and dependability. Intrusion detection systems (IDS) are crucial in preventing attacks on transport networks that rely on the Internet of Things (IoT). However, understanding the rationale behind deep learning-based IDS models may be challenging because they do not explain their findings. We offer an interpretable deep learning system that may be used to improve transportation network safety using IoT. To develop naturally accessible explanations for IDS projections, we integrate deep learning models with the Shapley Additive Reasons (SHAP) approach. By adding weight to distinct elements of the input data needed to develop the model, we increase the readability of so-called black box processes. We use the ToN_IoT dataset, which provides statistics on the volume of network traffic created by IoT-enabled transport systems, to assess the success of our strategy. We use a tool called CICFlowMeter to create network flows and collect data. The regularity of the flows, as well as their correlation with specific assaults, has been documented, allowing us to train and evaluate the IDS model. The experiment findings show that our explainable deep learning system is extremely accurate at detecting and categorising intrusions in IoT-enabled transportation networks. By examining data using the SHAP approach, cybersecurity specialists may learn more about the IDS's decision-making process. This enables the development of robust solutions, which improves the overall security of the Internet of Things. Aside from simplifying IDS predictions, the proposed technique provides useful recommendations for strengthening the resilience of IoT-enabled transportation systems against cyberattacks. The usefulness of IDS in defending mission critical IoT infrastructure has been questioned by security experts in the Internet of Vehicles (IoV) industry. The IoV is the primary research object in this case. Deep learning algorithms' versatility in processing many forms of data has contributed to their growing prominence in the field of anomaly detection in intrusion detection systems. Although machine learning models may be highly useful, they frequently yield false positives, and the path they follow to their conclusions is not always obvious to humans. Cybersecurity experts who want to evaluate the performance of a system or design more secure solutions need to understand the thinking process behind an IDS's results. The SHAP approach is employed in our proposed framework to give greater insight into the decisions made by IDSs that depend on deep learning. As a result, IoT network security is strengthened, and more cyber-resilient systems are developed. We demonstrate the effectiveness of our technique by comparing it to other credible methods and utilising the ToN_IoT dataset. Our framework has the best success rate when compared to other framew
{"title":"Unraveling the Decision-making Process Interpretable Deep Learning IDS for Transportation Network Security","authors":"Rajit Nair","doi":"10.54216/jcim.120205","DOIUrl":"https://doi.org/10.54216/jcim.120205","url":null,"abstract":"The growing ubiquity of IoT-enabled devices in recent years emphasizes the critical need to strengthen transportation network safety and dependability. Intrusion detection systems (IDS) are crucial in preventing attacks on transport networks that rely on the Internet of Things (IoT). However, understanding the rationale behind deep learning-based IDS models may be challenging because they do not explain their findings. We offer an interpretable deep learning system that may be used to improve transportation network safety using IoT. To develop naturally accessible explanations for IDS projections, we integrate deep learning models with the Shapley Additive Reasons (SHAP) approach. By adding weight to distinct elements of the input data needed to develop the model, we increase the readability of so-called black box processes. We use the ToN_IoT dataset, which provides statistics on the volume of network traffic created by IoT-enabled transport systems, to assess the success of our strategy. We use a tool called CICFlowMeter to create network flows and collect data. The regularity of the flows, as well as their correlation with specific assaults, has been documented, allowing us to train and evaluate the IDS model. The experiment findings show that our explainable deep learning system is extremely accurate at detecting and categorising intrusions in IoT-enabled transportation networks. By examining data using the SHAP approach, cybersecurity specialists may learn more about the IDS's decision-making process. This enables the development of robust solutions, which improves the overall security of the Internet of Things. Aside from simplifying IDS predictions, the proposed technique provides useful recommendations for strengthening the resilience of IoT-enabled transportation systems against cyberattacks. The usefulness of IDS in defending mission critical IoT infrastructure has been questioned by security experts in the Internet of Vehicles (IoV) industry. The IoV is the primary research object in this case. Deep learning algorithms' versatility in processing many forms of data has contributed to their growing prominence in the field of anomaly detection in intrusion detection systems. Although machine learning models may be highly useful, they frequently yield false positives, and the path they follow to their conclusions is not always obvious to humans. Cybersecurity experts who want to evaluate the performance of a system or design more secure solutions need to understand the thinking process behind an IDS's results. The SHAP approach is employed in our proposed framework to give greater insight into the decisions made by IDSs that depend on deep learning. As a result, IoT network security is strengthened, and more cyber-resilient systems are developed. We demonstrate the effectiveness of our technique by comparing it to other credible methods and utilising the ToN_IoT dataset. Our framework has the best success rate when compared to other framew","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"14 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":"116265223","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}
Chaotic encryptions offered various advantages over traditional encryption methods, like high security, speed, reasonable computational overheads. This paper introduces novel perturbation techniques for data encryption based on double chaotic systems. A new technique for image encryption utilizing mixed the proposed chaotic maps is presented. The proposed hybrid system parallels and combines two chaotic maps as part of a new chaotification method. It based on permutation, diffusion and system parameters, which are then involved in pixel shuffling and substitution operations, respectively. Many statistical test and security analysis indicate the validity of the results, e.g., the average values for NPCR and UACI are 99.67145% and 33.63288%, respectively. The proposed technique can achieve low residual intelligibility, high sensitivity and quality of recovered data, high security performance, and it show that the encrypted image has good resistance against attacks.
{"title":"A New Chaos-based Approach for Robust Image Encryption","authors":"F. Khalifa, A. Khalil, M. A. Mohamed","doi":"10.54216/jcim.070104","DOIUrl":"https://doi.org/10.54216/jcim.070104","url":null,"abstract":"Chaotic encryptions offered various advantages over traditional encryption methods, like high security, speed, reasonable computational overheads. This paper introduces novel perturbation techniques for data encryption based on double chaotic systems. A new technique for image encryption utilizing mixed the proposed chaotic maps is presented. The proposed hybrid system parallels and combines two chaotic maps as part of a new chaotification method. It based on permutation, diffusion and system parameters, which are then involved in pixel shuffling and substitution operations, respectively. Many statistical test and security analysis indicate the validity of the results, e.g., the average values for NPCR and UACI are 99.67145% and 33.63288%, respectively. The proposed technique can achieve low residual intelligibility, high sensitivity and quality of recovered data, high security performance, and it show that the encrypted image has good resistance against attacks.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"152 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":"114638897","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}
Presently, a precise localization and tracking process becomes significant to enable smartphone-assisted navigation to maximize accuracy in the real-time environment. Fingerprint-based localization is the commonly available model for accomplishing effective outcomes. With this motivation, this study focuses on designing efficient smartphone-assisted indoor localization and tracking models using the glowworm swarm optimization (ILT-GSO) algorithm. The ILT-GSO algorithm involves creating a GSO algorithm based on the light-emissive characteristics of glowworms to determine the location. In addition, the Kalman filter is applied to mitigate the estimation process and update the initial position of the glowworms. A wide range of experiments was carried out, and the results are investigated in terms of distinct evaluation metrics. The simulation outcome demonstrated considerable enhancement in the real-time environment and reduced the computational complexity. The ILT-GSO algorithm has resulted in an increased localization performance with minimal error over the recent techniques.
{"title":"An Efficient Smartphone Assisted Indoor Localization with Tracking Approach using Glowworm Swarm Optimization Algorithm","authors":"Mohammad Alshehri","doi":"10.54216/jcim.060203","DOIUrl":"https://doi.org/10.54216/jcim.060203","url":null,"abstract":"Presently, a precise localization and tracking process becomes significant to enable smartphone-assisted navigation to maximize accuracy in the real-time environment. Fingerprint-based localization is the commonly available model for accomplishing effective outcomes. With this motivation, this study focuses on designing efficient smartphone-assisted indoor localization and tracking models using the glowworm swarm optimization (ILT-GSO) algorithm. The ILT-GSO algorithm involves creating a GSO algorithm based on the light-emissive characteristics of glowworms to determine the location. In addition, the Kalman filter is applied to mitigate the estimation process and update the initial position of the glowworms. A wide range of experiments was carried out, and the results are investigated in terms of distinct evaluation metrics. The simulation outcome demonstrated considerable enhancement in the real-time environment and reduced the computational complexity. The ILT-GSO algorithm has resulted in an increased localization performance with minimal error over the recent techniques.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"39 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":"132271092","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}
Mathematical programming can express competency concepts in a well-defined mathematical model for a particular Any system that runs is always be expected to experience faults in different ways. Any change in the physical state of numerous components, control machinery, as well as environmental factors, might result in these problems. In process industries, where prompt detection is crucial in maintaining high product quality, dependability, and safety under various operating situations, finding these flaws is one of the most difficult tasks. The goal of this project is to implement several machine learning techniques for fault identification and classification in a binary distillation column. A pilot binary distillation unit (UOP3CC) is utilized for this purpose. The set up is run under normal operating conditions and the real time data is collected. Three common faults namely reboiler fault, feed pump fault and sensor fault are introduced one at a time and the faulty data is collected. These data are then introduced in to different machine learning algorithms like Logistic Regression, KNN, Naive Bayes, Decision Tree, Gradient Boosting, X Gradient Boosting, SVC and Light Gradient Boosting for model development. 70% of the data samples used for training and 30% of data samples are used for testing. It is found the Decision tree algorithm gives the best accuracy possible with 99.9%. Using decision tree algorithm, fault classification is performed for different datasets and is found that the algorithm was able to classify accurately even for new untrained datasets.
{"title":"Data Driven Machine Learning For Fault Detection And Classification In Binary Distillation Column","authors":"S. -, M. Mythily, D. ., D. Manamalli","doi":"10.54216/jcim.110105","DOIUrl":"https://doi.org/10.54216/jcim.110105","url":null,"abstract":"Mathematical programming can express competency concepts in a well-defined mathematical model for a particular Any system that runs is always be expected to experience faults in different ways. Any change in the physical state of numerous components, control machinery, as well as environmental factors, might result in these problems. In process industries, where prompt detection is crucial in maintaining high product quality, dependability, and safety under various operating situations, finding these flaws is one of the most difficult tasks. The goal of this project is to implement several machine learning techniques for fault identification and classification in a binary distillation column. A pilot binary distillation unit (UOP3CC) is utilized for this purpose. The set up is run under normal operating conditions and the real time data is collected. Three common faults namely reboiler fault, feed pump fault and sensor fault are introduced one at a time and the faulty data is collected. These data are then introduced in to different machine learning algorithms like Logistic Regression, KNN, Naive Bayes, Decision Tree, Gradient Boosting, X Gradient Boosting, SVC and Light Gradient Boosting for model development. 70% of the data samples used for training and 30% of data samples are used for testing. It is found the Decision tree algorithm gives the best accuracy possible with 99.9%. Using decision tree algorithm, fault classification is performed for different datasets and is found that the algorithm was able to classify accurately even for new untrained datasets.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"61 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":"128557368","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 advancements of information technologies and wireless networks have created open online communication channels. Inappropriately, trolls have abused the technologies to impose cyberattacks and threats. Automated cybersecurity solutions are essential to avoid the threats and security issues in social media. This paper presents an efficient dragonfly algorithm (DFA) with gated recurrent unit (GRU) for cybersecurity in social networking. The proposed DFA-GRU model aims to determine the social networking data into neural statements or insult (cyberbullying) statements. Besides, the DFA-GRU model primarily undergoes preprocessing to get rid of unwanted data and TF-IDF vectorizer is used. In addition, the GRU model is employed for the classification process in which the hyperparameters are optimally adjusted by the use of DFA, and thereby the overall classification results get improved. The performance validation of the DFA-GRU model is carried out using benchmark dataset and the results are examined under varying aspects. The experimental outcome highlighted the enhanced performance of the DFA-GRU model interms of distinct measures.
{"title":"Dragonfly Algorithm with Gated Recurrent Unit for Cybersecurity in Social Networking","authors":"Yutao Han, I. M. El-Hasnony, Wenbo Cai","doi":"10.54216/jcim.000107","DOIUrl":"https://doi.org/10.54216/jcim.000107","url":null,"abstract":"The advancements of information technologies and wireless networks have created open online communication channels. Inappropriately, trolls have abused the technologies to impose cyberattacks and threats. Automated cybersecurity solutions are essential to avoid the threats and security issues in social media. This paper presents an efficient dragonfly algorithm (DFA) with gated recurrent unit (GRU) for cybersecurity in social networking. The proposed DFA-GRU model aims to determine the social networking data into neural statements or insult (cyberbullying) statements. Besides, the DFA-GRU model primarily undergoes preprocessing to get rid of unwanted data and TF-IDF vectorizer is used. In addition, the GRU model is employed for the classification process in which the hyperparameters are optimally adjusted by the use of DFA, and thereby the overall classification results get improved. The performance validation of the DFA-GRU model is carried out using benchmark dataset and the results are examined under varying aspects. The experimental outcome highlighted the enhanced performance of the DFA-GRU model interms of distinct measures.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"30 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":"132730504","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}
Phishing is a familiar kind of cyberattack in the present digital world. Phishing detection with maximum performance accuracy and minimum computational complexity is continuously a topic of much interest. A novel technology was established for improving the phishing detection rate and decreasing computational constraints recently. But, one solution has inadequate for addressing every problem due to attackers from cyberspace. Thus, the initial objective of this work is for analysing the performance of different deep learning (DL) techniques from detection phishing activity. This study introduces a novel Brain Storm Optimization with Long Short Term Memory Enabled Phishing Webpage Classification (BSOLSTM-PWC) for Cybersecurity. The proposed BSOLSTM-PWC technique enables to accomplish cybersecurity by the identification and classification of phishing webpages. To accomplish this, the BSOLSTM-PWC technique initially employs data pre-processing technique to transform the data into actual format. Besides, the BSOLSTM-PWC technique employs LSTM classifier for the identification and categorization of phishing webpages. Moreover, the BSO algorithm is utilized to appropriately adjust the hyperparameters involved in the LSTM model. For reporting the improved outcomes of the BSOLSTM-PWC method, a wide-ranging simulation analysis is made using benchmark dataset. The experimental outcomes reported the enhanced outcomes of the BSOLSTM-PWC method on existing methods.
{"title":"Brain Storm Optimization with Long Short Term Memory Enabled Phishing Webpage Classification for Cybersecurity","authors":"M. Zaher, N. M. Eldakhly","doi":"10.54216/jcim.090202","DOIUrl":"https://doi.org/10.54216/jcim.090202","url":null,"abstract":"Phishing is a familiar kind of cyberattack in the present digital world. Phishing detection with maximum performance accuracy and minimum computational complexity is continuously a topic of much interest. A novel technology was established for improving the phishing detection rate and decreasing computational constraints recently. But, one solution has inadequate for addressing every problem due to attackers from cyberspace. Thus, the initial objective of this work is for analysing the performance of different deep learning (DL) techniques from detection phishing activity. This study introduces a novel Brain Storm Optimization with Long Short Term Memory Enabled Phishing Webpage Classification (BSOLSTM-PWC) for Cybersecurity. The proposed BSOLSTM-PWC technique enables to accomplish cybersecurity by the identification and classification of phishing webpages. To accomplish this, the BSOLSTM-PWC technique initially employs data pre-processing technique to transform the data into actual format. Besides, the BSOLSTM-PWC technique employs LSTM classifier for the identification and categorization of phishing webpages. Moreover, the BSO algorithm is utilized to appropriately adjust the hyperparameters involved in the LSTM model. For reporting the improved outcomes of the BSOLSTM-PWC method, a wide-ranging simulation analysis is made using benchmark dataset. The experimental outcomes reported the enhanced outcomes of the BSOLSTM-PWC method on existing methods.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"32 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":"132838427","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}
Intensive studies have been done to get robust encryption algorithms. Due to the importance of image information, image encryption has become played a vital rule in information security. Many image encryption schemes have been proposed but most of them suffer from poor robustness against severe types of attacks. In this paper two proposed techniques will be presented for color image encryption to be robust to severe attacks: composite attack. One of these approaches is represented by hybrid use of both steganography and Discrete Wavelet Transform (DWT) based encryption and the other one in which Fractional Fast Fourier Transform (FRFFT) has been used with DWT. Not only new techniques will be presented but also a new chaotic map has been used as random keys for both algorithms. After extensive comparative study with some traditional techniques, it has been found that the proposed algorithms have achieved better performance.
{"title":"Chaos Based Stego Color Image Encryption","authors":"M. I. F. Allah","doi":"10.54216/jcim.100201","DOIUrl":"https://doi.org/10.54216/jcim.100201","url":null,"abstract":"Intensive studies have been done to get robust encryption algorithms. Due to the importance of image information, image encryption has become played a vital rule in information security. Many image encryption schemes have been proposed but most of them suffer from poor robustness against severe types of attacks. In this paper two proposed techniques will be presented for color image encryption to be robust to severe attacks: composite attack. One of these approaches is represented by hybrid use of both steganography and Discrete Wavelet Transform (DWT) based encryption and the other one in which Fractional Fast Fourier Transform (FRFFT) has been used with DWT. Not only new techniques will be presented but also a new chaotic map has been used as random keys for both algorithms. After extensive comparative study with some traditional techniques, it has been found that the proposed algorithms have achieved better performance.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"24 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":"123854549","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}
In the modern internet-connected society, technologies underpin almost every action in society. Although there have been positive effects of technologies in the organization, there have been forensic specialists indicating the issues and challenges with cyber security threats. The real-time conditions provide the capability of the organization in detecting, analyzing, and defending individuals against such threats. In this research project, the focus is on understanding the cyber security threats and the protection approaches to be utilized in safeguarding threats from financial institutions. With the Covid-19 pandemic, most of the financial firms, including Al Rajhi Bank, are utilizing technologies in their operations, and this has exposed them to cyber security threats. From the literature review conducted, the financial firms need to consider cyber security approaches including implementing triple DES, RSA, and blowfish algorithms in improving the security measures of the organizations.
{"title":"A Comprehensive Analysis of Cyber Security Protection Approaches for Financial Firms: A Case of Al Rajhi Bank, Saudi Arabia","authors":"Mohammed I. Alghamdi","doi":"10.54216/jcim.090101","DOIUrl":"https://doi.org/10.54216/jcim.090101","url":null,"abstract":"In the modern internet-connected society, technologies underpin almost every action in society. Although there have been positive effects of technologies in the organization, there have been forensic specialists indicating the issues and challenges with cyber security threats. The real-time conditions provide the capability of the organization in detecting, analyzing, and defending individuals against such threats. In this research project, the focus is on understanding the cyber security threats and the protection approaches to be utilized in safeguarding threats from financial institutions. With the Covid-19 pandemic, most of the financial firms, including Al Rajhi Bank, are utilizing technologies in their operations, and this has exposed them to cyber security threats. From the literature review conducted, the financial firms need to consider cyber security approaches including implementing triple DES, RSA, and blowfish algorithms in improving the security measures of the organizations.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"75 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":"127552221","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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