The interpretation of large data streams necessitates high-performance repeated transfers, which overload Microprocessor System on Chips (SoC). The effective direct memory access (DMA) controller performs bulk data transfers without the CPU's involvement. The Direct Memory Controller (DMAC) solves this by facilitating bulk data transfer and execution. In this work, we created an intelligent DMAC (I-DMAC) for accessing video processing data without using CPUs. The model includes Bus selection Module, User control signal, Status Register, DMA supported Address, and AXI-PCI subsystems for improved video frame analysis. These modules are experimentally verified in Xilinx FPGA SoC architecture using VHDL code simulation and results compared to the E-DMAC model.
{"title":"I-DMAC: An Intelligent DMA Controller for Utilization - Aware Video Streaming used in AI Applications","authors":"P. Shukla, P. Shukla","doi":"10.54216/jcim.080203","DOIUrl":"https://doi.org/10.54216/jcim.080203","url":null,"abstract":"The interpretation of large data streams necessitates high-performance repeated transfers, which overload Microprocessor System on Chips (SoC). The effective direct memory access (DMA) controller performs bulk data transfers without the CPU's involvement. The Direct Memory Controller (DMAC) solves this by facilitating bulk data transfer and execution. In this work, we created an intelligent DMAC (I-DMAC) for accessing video processing data without using CPUs. The model includes Bus selection Module, User control signal, Status Register, DMA supported Address, and AXI-PCI subsystems for improved video frame analysis. These modules are experimentally verified in Xilinx FPGA SoC architecture using VHDL code simulation and results compared to the E-DMAC model.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"28 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":"127901436","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}
Our economy, infrastructure and societies rely to a large extent on information technology and computer networks solutions. Increasing dependency on information technologies has also multiplied the potential hazards of cyber-attacks. The prime goal of this study is to critically examine how the sufficient knowledge of cyber security threats plays a vital role in detection of any intrusion in simple networks and preventing the attacks. The study has evaluated various literatures and peer reviewed articles to examine the findings obtained by consolidating the outcomes of different studies and present the final findings into a simplified solution.
{"title":"An investigation into the effect of cybersecurity on attack prevention strategies","authors":"Mohammed I. Alghamdi","doi":"10.54216/jcim.030203","DOIUrl":"https://doi.org/10.54216/jcim.030203","url":null,"abstract":"Our economy, infrastructure and societies rely to a large extent on information technology and computer networks solutions. Increasing dependency on information technologies has also multiplied the potential hazards of cyber-attacks. The prime goal of this study is to critically examine how the sufficient knowledge of cyber security threats plays a vital role in detection of any intrusion in simple networks and preventing the attacks. The study has evaluated various literatures and peer reviewed articles to examine the findings obtained by consolidating the outcomes of different studies and present the final findings into a simplified solution.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"25 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":"121277770","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}
Energy efficiency is a significant challenge in mobile ad hoc networks (MANETs) design where the nodes move randomly with limited energy, leading to acceptable topology modifications. Clustering is a widely applied technique to accomplish energy efficiency in MANET. Therefore, this paper designs a new energy-efficient clustering protocol using an enhanced rain optimization algorithm (EECP-EROA) for MANET. The EROA technique is derived by integrating the Levy flight concept to the ROA to enhance global exploration abilities. In addition, the EECP-EROA technique intends to proficiently select CHs and the nearby nodes linked to the CH to generate clusters. Moreover, the EECP-EROA technique has derived an objective function with different input parameters. To showcase the superior performance of the EECP-EROA technique, a brief set of simulations takes place, and the results are inspected under varying aspects. The experimental values pointed out the betterment of the EECP-EROA technique over the other methods.
{"title":"An Energy Efficient Clustering Protocol using Enhanced Rain Optimization Algorithm in Mobile Adhoc Networks","authors":"M. Elhoseny, X. Yuan","doi":"10.54216/jcim.070201","DOIUrl":"https://doi.org/10.54216/jcim.070201","url":null,"abstract":"Energy efficiency is a significant challenge in mobile ad hoc networks (MANETs) design where the nodes move randomly with limited energy, leading to acceptable topology modifications. Clustering is a widely applied technique to accomplish energy efficiency in MANET. Therefore, this paper designs a new energy-efficient clustering protocol using an enhanced rain optimization algorithm (EECP-EROA) for MANET. The EROA technique is derived by integrating the Levy flight concept to the ROA to enhance global exploration abilities. In addition, the EECP-EROA technique intends to proficiently select CHs and the nearby nodes linked to the CH to generate clusters. Moreover, the EECP-EROA technique has derived an objective function with different input parameters. To showcase the superior performance of the EECP-EROA technique, a brief set of simulations takes place, and the results are inspected under varying aspects. The experimental values pointed out the betterment of the EECP-EROA technique over the other methods.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"112 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":"115480337","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}
An intrusion detection system is a critical security feature that analyses network traffic in order to avoid serious unauthorized access to network resources. For securing networks against potential breaches, effective intrusion detection is critical. In this paper, a novel Intrusion Detection Framework (IDF) is proposed. The three modules that comprise the suggested IDF are: (i) Data Pre-processing Module (DPM), (ii) Feature Selection Module (FSM), and Classification Module (CM). DPM collects and processes network traffic in order to prepare data for training and testing. The FSM seeks to identify the key elements for recognizing DPM intrusion attempts. An Improved Particle Swarm Optimization is used (IPSO). IPSO is a hybrid method that uses both filter and wrapper approaches to generate accurate and relevant information for the classification step that follows. Primary Selection Phase (PSP) and Completed Selection Phase (CSP) are the two consecutive feature selection phases in IPSO. PSP employs a filtering approaches to quickly identify the most significant features for detecting intrusion threats while eliminating those that are redundant or ineffective. In CSP, the next level of IPSO, this behavior reduces the computing cost. For accurate feature selection, CSP uses Binary Particle Swarm Optimization (Bi-PSO) as a wrapper approach. Based on the most effective features identified by FSM, The CM aims to identify intrusion attempts with the minimal processing time. Therefore, a K-Nearest Neighbor KNN classifier has been deployed. As a result, based on the significant features identified by the IPSO technique, KNN can accurately detect intrusion attacks with the least amount of processing time. The experimental results have shown that the proposed IDF outperforms other recent techniques using UNSW_NB-15 dataset. The accuracy, precision, recall, F1score, and processing time of the experimental outcomes of our findings were assessed. Our results were competitive with an accuracy of 99.8%, precision of 99.94%, recall of 99.85%, F1-score of 99.89%, and excursion time of 59.15s when compared to the findings of the current works.
{"title":"A Novel Intrusion Detection Framework (IDF) using Machine Learning Methods","authors":"Shereen H. Ali","doi":"10.54216/jcim.100103","DOIUrl":"https://doi.org/10.54216/jcim.100103","url":null,"abstract":"An intrusion detection system is a critical security feature that analyses network traffic in order to avoid serious unauthorized access to network resources. For securing networks against potential breaches, effective intrusion detection is critical. In this paper, a novel Intrusion Detection Framework (IDF) is proposed. The three modules that comprise the suggested IDF are: (i) Data Pre-processing Module (DPM), (ii) Feature Selection Module (FSM), and Classification Module (CM). DPM collects and processes network traffic in order to prepare data for training and testing. The FSM seeks to identify the key elements for recognizing DPM intrusion attempts. An Improved Particle Swarm Optimization is used (IPSO). IPSO is a hybrid method that uses both filter and wrapper approaches to generate accurate and relevant information for the classification step that follows. Primary Selection Phase (PSP) and Completed Selection Phase (CSP) are the two consecutive feature selection phases in IPSO. PSP employs a filtering approaches to quickly identify the most significant features for detecting intrusion threats while eliminating those that are redundant or ineffective. In CSP, the next level of IPSO, this behavior reduces the computing cost. For accurate feature selection, CSP uses Binary Particle Swarm Optimization (Bi-PSO) as a wrapper approach. Based on the most effective features identified by FSM, The CM aims to identify intrusion attempts with the minimal processing time. Therefore, a K-Nearest Neighbor KNN classifier has been deployed. As a result, based on the significant features identified by the IPSO technique, KNN can accurately detect intrusion attacks with the least amount of processing time. The experimental results have shown that the proposed IDF outperforms other recent techniques using UNSW_NB-15 dataset. The accuracy, precision, recall, F1score, and processing time of the experimental outcomes of our findings were assessed. Our results were competitive with an accuracy of 99.8%, precision of 99.94%, recall of 99.85%, F1-score of 99.89%, and excursion time of 59.15s when compared to the findings of the current works.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"106 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":"129562929","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}
Due to the drastic rise in multimedia content, digital images have become a major carrier of data. Generally, images are communicated or archived via wireless communication changes, and the significance of data security gets increased. In order to accomplish security, encryption is an effective technique which is used to encrypt the images using secret keys in such a way that it is not readable by the hacker. In this view, this study focuses on the design of Teaching and Learning based Optimization (TLBO) with Multi-Key Homomorphic Encryption (MHE) technique, called MHE-TLBO algorithm. The goal of the MHE-TLBO algorithm is to optimally select multiple keys using TLBO algorithm for encryption and decryption processes. In addition, the MHE-TLBO algorithm has derived a fitness function involving peak signal to noise ratio (PSNR) and thereby ensures the superior quality of the reconstructed image. For validating the security performance of the MHE-TLBO algorithm, a comprehensive result analysis is made and the simulation results ensured the betterment of the MHE-TLBO algorithm interms of different aspects.
{"title":"An Optimal Teaching and Learning based Optimization with Multi-Key Homomorphic Encryption for Image Security","authors":"M. Khalifa, A. N. Al-Masri","doi":"10.54216/jcim.070203","DOIUrl":"https://doi.org/10.54216/jcim.070203","url":null,"abstract":"Due to the drastic rise in multimedia content, digital images have become a major carrier of data. Generally, images are communicated or archived via wireless communication changes, and the significance of data security gets increased. In order to accomplish security, encryption is an effective technique which is used to encrypt the images using secret keys in such a way that it is not readable by the hacker. In this view, this study focuses on the design of Teaching and Learning based Optimization (TLBO) with Multi-Key Homomorphic Encryption (MHE) technique, called MHE-TLBO algorithm. The goal of the MHE-TLBO algorithm is to optimally select multiple keys using TLBO algorithm for encryption and decryption processes. In addition, the MHE-TLBO algorithm has derived a fitness function involving peak signal to noise ratio (PSNR) and thereby ensures the superior quality of the reconstructed image. For validating the security performance of the MHE-TLBO algorithm, a comprehensive result analysis is made and the simulation results ensured the betterment of the MHE-TLBO algorithm interms of different aspects.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"48 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":"117040278","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}
Big data has become an integral part of modern businesses, but its management and protection present numerous challenges, such as securing sensitive information from unauthorized access, preventing data breaches, and ensuring data integrity. This work investigated applying a machine learning (ML) approach to tackling the challenges of information security and management in big data environments. We present an ML framework that leverages a supervised learning strategy to detect anomalies, classify big data, and predict potential security threats. We also investigate the implementation of this framework and its potential benefits, such as reducing false positives and improving detection rates. Our experimental analysis in public datasets demonstrates the effectiveness of our approach in improving information security and management in big data environments.
{"title":"Machine Learning framework for Information Security Management in Big Data Applications","authors":"Othman Al Basheer, Murat Ozcek","doi":"10.54216/jcim.110106","DOIUrl":"https://doi.org/10.54216/jcim.110106","url":null,"abstract":"Big data has become an integral part of modern businesses, but its management and protection present numerous challenges, such as securing sensitive information from unauthorized access, preventing data breaches, and ensuring data integrity. This work investigated applying a machine learning (ML) approach to tackling the challenges of information security and management in big data environments. We present an ML framework that leverages a supervised learning strategy to detect anomalies, classify big data, and predict potential security threats. We also investigate the implementation of this framework and its potential benefits, such as reducing false positives and improving detection rates. Our experimental analysis in public datasets demonstrates the effectiveness of our approach in improving information security and management in big data environments.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"34 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":"134565304","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}
Parth Rustagi, Rohit Sroa, Priyanshu Sinha, Ashish Sharma4, S. Tayal
As useful as it gets to connect devices to the internet to make life easier and more comfortable, it also opens the gates to various cyber threats. The connection of Smart Home devices to the internet makes them vulnerable to malicious hackers that infiltrate the system. Hackers can penetrate these systems and have full control over devices. This can lead to denial of service, data leakage, invasion of privacy, etc. Thus security is a major aspect of Smart home devices. However, many companies manufacturing these Smart Home devices have little to no security protocols in their devices. In the process of making the IoT devices cheaper, various cost-cutting is done on the security protocols in IoT devices. In some way, many manufactures of the devices don’t even consider this as a factor to build upon. This leaves the devices vulnerable to attacks. Various authorities have worked upon to standardize the security aspects for the IoT and listed out guidelines for manufactures to follow, but many fail to abide by them. This paper introduces and talks about the various threats, various Security threats to Smart Home devices. It takes a deep dive into the solutions for the discussed threats. It also discusses their prevention. Lastly, it discusses various preventive measures and good practices to be incorporated to protect devices from any future attacks.
{"title":"HomeTec Software for Security Aspects of Smart Home Devices Based on IoT","authors":"Parth Rustagi, Rohit Sroa, Priyanshu Sinha, Ashish Sharma4, S. Tayal","doi":"10.54216/jcim.050101","DOIUrl":"https://doi.org/10.54216/jcim.050101","url":null,"abstract":"As useful as it gets to connect devices to the internet to make life easier and more comfortable, it also opens the gates to various cyber threats. The connection of Smart Home devices to the internet makes them vulnerable to malicious hackers that infiltrate the system. Hackers can penetrate these systems and have full control over devices. This can lead to denial of service, data leakage, invasion of privacy, etc. Thus security is a major aspect of Smart home devices. However, many companies manufacturing these Smart Home devices have little to no security protocols in their devices. In the process of making the IoT devices cheaper, various cost-cutting is done on the security protocols in IoT devices. In some way, many manufactures of the devices don’t even consider this as a factor to build upon. This leaves the devices vulnerable to attacks. Various authorities have worked upon to standardize the security aspects for the IoT and listed out guidelines for manufactures to follow, but many fail to abide by them. This paper introduces and talks about the various threats, various Security threats to Smart Home devices. It takes a deep dive into the solutions for the discussed threats. It also discusses their prevention. Lastly, it discusses various preventive measures and good practices to be incorporated to protect devices from any future attacks.","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":"124459178","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}
Safety and security risks to critical infrastructure organizations are well known, and incidents in both fields have taken place. To help critical infrastructure organizations manage these areas, safety and security standards have been created. The main aim of this paper is to present a framework that has been created to manage both safety and security by providing guidance on how to create a Safety and Security Management System (SSMS). The framework identifies and remediates conflicts and issues between IT, OT, safety, and security. While also creating processes that can combine safety and security compliance to standards to reduce duplication of work and allow one process to manage both areas. A survey was carried out to understand if the framework would be of use to organizations and to better understand the issues users have with managing safety and security and how they manage conflicts that can occur. The survey showed key areas of concern for organizations and how the framework can be of use to them. It identified six themes from the research and identified improvements opportunities for the framework that can be implemented.
{"title":"A Framework for creating a Safety and Security Management System (SSMS)","authors":"R. Kemp, Richard Smith","doi":"10.54216/jcim.090201","DOIUrl":"https://doi.org/10.54216/jcim.090201","url":null,"abstract":"Safety and security risks to critical infrastructure organizations are well known, and incidents in both fields have taken place. To help critical infrastructure organizations manage these areas, safety and security standards have been created. The main aim of this paper is to present a framework that has been created to manage both safety and security by providing guidance on how to create a Safety and Security Management System (SSMS). The framework identifies and remediates conflicts and issues between IT, OT, safety, and security. While also creating processes that can combine safety and security compliance to standards to reduce duplication of work and allow one process to manage both areas. A survey was carried out to understand if the framework would be of use to organizations and to better understand the issues users have with managing safety and security and how they manage conflicts that can occur. The survey showed key areas of concern for organizations and how the framework can be of use to them. It identified six themes from the research and identified improvements opportunities for the framework that can be implemented.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"67 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":"114451856","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 proliferation of Internet of Things (IoT) devices has led to an increase in the number of malware attacks targeting these devices. Traditional security mechanisms such as firewalls and antivirus software are often inadequate in protecting IoT devices from malware attacks due to their limited resources and the heterogeneity of IoT networks. In this paper, we propose DeepSecureIoT, a deep learning-based framework for securing IoT against malware attacks. Our proposed framework uses a deep convolutional neural network (CNN) to extract features from network traffic and classify it as normal or malicious. The CNN is trained using a large dataset of network traffic to accurately identify malware attacks and reduce false positives. We evaluate the performance of DeepSecureIoT using a benchmark dataset of real-world IoT malware attacks. The results show that our proposed framework achieves an accuracy of 0.961 in detecting and classifying malware attacks, outperforming state-of-the-art intrusion detection systems. Moreover, DeepSecureIoT has low computational overhead and can be deployed on resource-constrained IoT devices.
{"title":"A Deep Learning Framework for Securing IoT Against Malwares","authors":"Mustafa El .., Aaras Y Y.kraidi","doi":"10.54216/jcim.110104","DOIUrl":"https://doi.org/10.54216/jcim.110104","url":null,"abstract":"The proliferation of Internet of Things (IoT) devices has led to an increase in the number of malware attacks targeting these devices. Traditional security mechanisms such as firewalls and antivirus software are often inadequate in protecting IoT devices from malware attacks due to their limited resources and the heterogeneity of IoT networks. In this paper, we propose DeepSecureIoT, a deep learning-based framework for securing IoT against malware attacks. Our proposed framework uses a deep convolutional neural network (CNN) to extract features from network traffic and classify it as normal or malicious. The CNN is trained using a large dataset of network traffic to accurately identify malware attacks and reduce false positives. We evaluate the performance of DeepSecureIoT using a benchmark dataset of real-world IoT malware attacks. The results show that our proposed framework achieves an accuracy of 0.961 in detecting and classifying malware attacks, outperforming state-of-the-art intrusion detection systems. Moreover, DeepSecureIoT has low computational overhead and can be deployed on resource-constrained IoT devices.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"16 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":"126451157","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 advent of the Internet of Things (IoT) has led to the proliferation of connected devices, creating numerous security challenges. With billions of devices generating vast amounts of data, managing information security risks in the age of IoT has become increasingly complex. Traditional security approaches are not sufficient to mitigate the risks posed by IoT devices. Machine learning (ML) provides a promising approach to enhance the security of IoT systems. This paper proposes a machine learning approach for managing information security risks in the age of IoT. The proposed approach utilizes ML algorithms to identify and mitigate security threats in IoT systems. The approach involves collecting and analyzing data from IoT devices, and applying ML algorithms to detect patterns and anomalies that may indicate security threats. The ML algorithms are trained using both supervised and unsupervised learning techniques to enable them to identify known and unknown threats. The paper describes a case study in which the proposed approach is applied to an IoT system for home security. The results demonstrate that the ML approach can effectively detect security threats in the IoT system and mitigate them in real-time.
{"title":"Managing Information Security Risks in the Age of IoT","authors":"A. .., R. Almajed","doi":"10.54216/jcim.110103","DOIUrl":"https://doi.org/10.54216/jcim.110103","url":null,"abstract":"The advent of the Internet of Things (IoT) has led to the proliferation of connected devices, creating numerous security challenges. With billions of devices generating vast amounts of data, managing information security risks in the age of IoT has become increasingly complex. Traditional security approaches are not sufficient to mitigate the risks posed by IoT devices. Machine learning (ML) provides a promising approach to enhance the security of IoT systems. This paper proposes a machine learning approach for managing information security risks in the age of IoT. The proposed approach utilizes ML algorithms to identify and mitigate security threats in IoT systems. The approach involves collecting and analyzing data from IoT devices, and applying ML algorithms to detect patterns and anomalies that may indicate security threats. The ML algorithms are trained using both supervised and unsupervised learning techniques to enable them to identify known and unknown threats. The paper describes a case study in which the proposed approach is applied to an IoT system for home security. The results demonstrate that the ML approach can effectively detect security threats in the IoT system and mitigate them in real-time.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"47 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":"124875677","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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