One of the most significant uses of the Internet of Things is military infiltration detection (IoT). Autonomous drones play a major role in IoT-based vulnerability scanning (UVs). By relocating UVs remotely, this work introduces a new algorithm called the Moth-Flame Optimization Algorithm (MFO). In particular, MFO is used to proactively manage UVs under various scenarios and under different intrusion-covering situations. According to actual studies, the suggested algorithm is both profitable and efficient.
{"title":"An Intelligent Spatial Military Intrusion Detection using Reactive Mobility Unmanned Vehicles Based on IoT and metaheuristic Optimization Algorithm","authors":"Lobna Osman","doi":"10.54216/jcim.090203","DOIUrl":"https://doi.org/10.54216/jcim.090203","url":null,"abstract":"One of the most significant uses of the Internet of Things is military infiltration detection (IoT). Autonomous drones play a major role in IoT-based vulnerability scanning (UVs). By relocating UVs remotely, this work introduces a new algorithm called the Moth-Flame Optimization Algorithm (MFO). In particular, MFO is used to proactively manage UVs under various scenarios and under different intrusion-covering situations. According to actual studies, the suggested algorithm is both profitable and efficient.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"2015 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":"121442137","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}
Wireless sensor networks (WSNs) are increasingly used in a wide variety of settings, including defence, industry, healthcare, and education. Hundreds or even thousands of sensor nodes are spread out across a given area and linked to a central Base Station (BS) in order to keep tabs on the environment. The BS then sends the data out to the users over the internet. The sensor network's adaptability, portability, dependability, and quickness are driving its widespread use across industries. The suggested SHS evaluates the efficiency of well-established symmetric algorithms to see where it stands in the spectrum of security. The Blowfish encryption algorithm was proven to require the least amount of processing power after extensive benchmarking. Therefore, the Blowfish algorithm is selected to protect sensitive medical information. The medical database receives the encrypted health records. Only those with proper permissions should be able to access them. Therefore, the CP-ABE is implemented to regulate access to patient records. The SHS's results on the dataset are compared to those of other existing systems. With SHS, health data may be transmitted to doctors rapidly and securely because it requires less computing time and energy. In addition to these benefits, SHS also offers privacy, authentication, and authorization.
{"title":"A Concentrated Energy Consumption Wireless Sensor Network by Symmetric Encryption and Attribute Based Encryption Technique","authors":"A. Soni","doi":"10.54216/jcim.120101","DOIUrl":"https://doi.org/10.54216/jcim.120101","url":null,"abstract":"Wireless sensor networks (WSNs) are increasingly used in a wide variety of settings, including defence, industry, healthcare, and education. Hundreds or even thousands of sensor nodes are spread out across a given area and linked to a central Base Station (BS) in order to keep tabs on the environment. The BS then sends the data out to the users over the internet. The sensor network's adaptability, portability, dependability, and quickness are driving its widespread use across industries. The suggested SHS evaluates the efficiency of well-established symmetric algorithms to see where it stands in the spectrum of security. The Blowfish encryption algorithm was proven to require the least amount of processing power after extensive benchmarking. Therefore, the Blowfish algorithm is selected to protect sensitive medical information. The medical database receives the encrypted health records. Only those with proper permissions should be able to access them. Therefore, the CP-ABE is implemented to regulate access to patient records. The SHS's results on the dataset are compared to those of other existing systems. With SHS, health data may be transmitted to doctors rapidly and securely because it requires less computing time and energy. In addition to these benefits, SHS also offers privacy, authentication, and authorization.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"36 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":"130572595","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}
Cybersecurity is training defensive arrangements, systems, and plans to save the information from cyber outbreaks. These virtual outbreaks are typically intended to retrieve, alter, or otherwise extinguish delicate data, extracting currency from manipulators, or disturb usual commercial procedures. System Security defends one’s system and information from breaks, interruptions also other intimidations. Network Security contains admission controller, computer virus and defiant computer virus software program, system safety, system analytics, system-connected protection categories, firewalls, and VPN encoding. System substructure strategies stand the mechanisms of a net that conveyance transportations desired intended for information, submissions, facilities, and multimedia. In this paper, we reflect on Cybersecurity in Networking Devices.
{"title":"Cybersecurity in Networking Devices","authors":"Afroj Jahan Badhon, S. Aggarwal","doi":"10.54216/jcim.080104","DOIUrl":"https://doi.org/10.54216/jcim.080104","url":null,"abstract":"Cybersecurity is training defensive arrangements, systems, and plans to save the information from cyber outbreaks. These virtual outbreaks are typically intended to retrieve, alter, or otherwise extinguish delicate data, extracting currency from manipulators, or disturb usual commercial procedures. System Security defends one’s system and information from breaks, interruptions also other intimidations. Network Security contains admission controller, computer virus and defiant computer virus software program, system safety, system analytics, system-connected protection categories, firewalls, and VPN encoding. System substructure strategies stand the mechanisms of a net that conveyance transportations desired intended for information, submissions, facilities, and multimedia. In this paper, we reflect on Cybersecurity in Networking Devices.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"135 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":"133040471","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}
Malware is software that is designed to cause damage to computer systems. Locating malicious software is a crucial task in the cybersecurity industry. Malware authors and security experts are locked in a never-ending conflict. In order to combat modern malware, which often exhibits polymorphic behavior and a wide range of characteristics, novel countermeasures have had to be created. Here, we present a hybrid learning approach to malware detection and classification. In this scenario, we have merged the machine learning techniques of Random Forest and K-Nearest Neighbor Classifier to develop a hybrid learning model. We used current malware and an updated dataset of 10,000 examples of malicious and benign files, with 78 feature values and 6 different malware classes to deal with. We compared the model's results with those of current approaches after training it for both binary and multi-class classification. The suggested methodology may be utilized to create an anti-malware application that is capable of detecting malware on newly collected data.
{"title":"Detection and Classification of Malware Using Guided Whale Optimization Algorithm for Voting Ensemble","authors":"M. Eid, M. I. F. Allah","doi":"10.54216/jcim.100102","DOIUrl":"https://doi.org/10.54216/jcim.100102","url":null,"abstract":"Malware is software that is designed to cause damage to computer systems. Locating malicious software is a crucial task in the cybersecurity industry. Malware authors and security experts are locked in a never-ending conflict. In order to combat modern malware, which often exhibits polymorphic behavior and a wide range of characteristics, novel countermeasures have had to be created. Here, we present a hybrid learning approach to malware detection and classification. In this scenario, we have merged the machine learning techniques of Random Forest and K-Nearest Neighbor Classifier to develop a hybrid learning model. We used current malware and an updated dataset of 10,000 examples of malicious and benign files, with 78 feature values and 6 different malware classes to deal with. We compared the model's results with those of current approaches after training it for both binary and multi-class classification. The suggested methodology may be utilized to create an anti-malware application that is capable of detecting malware on newly collected data.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"108 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":"134214251","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, Mohamed Arafa, Mustafa Marouf, M. Ahmed, Nehal Fawzy
Feature detection, description and matching are essential components of various computer vision applications; thus, they have received a considerable attention in the last decades. Several feature detectors and descriptors have been proposed in the literature with a variety of definitions for what kind of points in an image is potentially interesting (i.e., a distinctive attribute). This chapter introduces basic notation and mathematical concepts for detecting and describing image features. Then, it discusses properties of perfect features and gives an overview of various existing detection and description methods. Furthermore, it explains some approaches to feature matching. Finally, the chapter discusses the most used techniques for performance evaluation of detection algorithms.
{"title":"Explaining feature detection Mechanisms: A Survey","authors":"Ahmed A. Elngar, Mohamed Arafa, Mustafa Marouf, M. Ahmed, Nehal Fawzy","doi":"10.54216/jcim.060103","DOIUrl":"https://doi.org/10.54216/jcim.060103","url":null,"abstract":"Feature detection, description and matching are essential components of various computer vision applications; thus, they have received a considerable attention in the last decades. Several feature detectors and descriptors have been proposed in the literature with a variety of definitions for what kind of points in an image is potentially interesting (i.e., a distinctive attribute). This chapter introduces basic notation and mathematical concepts for detecting and describing image features. Then, it discusses properties of perfect features and gives an overview of various existing detection and description methods. Furthermore, it explains some approaches to feature matching. Finally, the chapter discusses the most used techniques for performance evaluation of detection algorithms.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"27 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132363873","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}
Deepfake videos are a growing concern today as they can be used to spread misinformation and manipulate public opinion. In this paper, we investigate the use of different feature extraction techniques for detecting deepfake videos using machine learning algorithms. We explore three feature extraction techniques, including facial landmarks detection, optical flow, and frequency analysis, and evaluate their effectiveness in detecting deepfake videos. We compare the performance of different machine learning algorithms and analyze their ability to detect deepfakes using the extracted features. Our experimental results show that the combination of facial landmarks detection and frequency analysis provides the best performance in detecting deepfake videos, with an accuracy of over 95%. Our findings suggest that machine learning algorithms can be a powerful tool in detecting deepfake videos, and feature extraction techniques play a crucial role in achieving high accuracy.
{"title":"A Machine Learning Approach to Detecting Deepfake Videos: An Investigation of Feature Extraction Techniques","authors":"Preeti Singh, Khyati Chaudhary, Gopal Chaudhary, Manju Khari, Bharat Rawal","doi":"10.54216/jcim.090204","DOIUrl":"https://doi.org/10.54216/jcim.090204","url":null,"abstract":"Deepfake videos are a growing concern today as they can be used to spread misinformation and manipulate public opinion. In this paper, we investigate the use of different feature extraction techniques for detecting deepfake videos using machine learning algorithms. We explore three feature extraction techniques, including facial landmarks detection, optical flow, and frequency analysis, and evaluate their effectiveness in detecting deepfake videos. We compare the performance of different machine learning algorithms and analyze their ability to detect deepfakes using the extracted features. Our experimental results show that the combination of facial landmarks detection and frequency analysis provides the best performance in detecting deepfake videos, with an accuracy of over 95%. Our findings suggest that machine learning algorithms can be a powerful tool in detecting deepfake videos, and feature extraction techniques play a crucial role in achieving high accuracy.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"4 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":"128244321","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}
Wireless Sensor Network (WSN) encompasses a set of wirelessly connected sensor nodes in the network for tracking and data gathering applications. The sensors in WSN are constrained in energy, memory, and processing capabilities. Despite the benefits of WSN, the sensors closer to the base station (BS) expels their energy faster. It suffers from hot spot issues and can be resolved by the use of unequal clustering techniques. In this aspect, this paper presents a political optimizer-based unequal clustering scheme (POUCS) for mitigating hot spot problems in WSN. The goal of the POUCS technique is to choose cluster heads (CHs) and determine unequal cluster sizes. The POUCS technique derives a fitness function involving different input parameters to minimize energy consumption and maximize the lifetime of the network. To showcase the enhanced performance of the POUCS technique, a comprehensive experimental analysis takes place, and the detailed comparison study reported the better performance of the POUCS technique over the recent techniques.
{"title":"Mitigating Hot Spot Problem in Wireless Sensor Networks using Political Optimizer Based Unequal Clustering Technique","authors":"Sahil Verm, Sanjukta Gain","doi":"10.54216/jcim.080201","DOIUrl":"https://doi.org/10.54216/jcim.080201","url":null,"abstract":"Wireless Sensor Network (WSN) encompasses a set of wirelessly connected sensor nodes in the network for tracking and data gathering applications. The sensors in WSN are constrained in energy, memory, and processing capabilities. Despite the benefits of WSN, the sensors closer to the base station (BS) expels their energy faster. It suffers from hot spot issues and can be resolved by the use of unequal clustering techniques. In this aspect, this paper presents a political optimizer-based unequal clustering scheme (POUCS) for mitigating hot spot problems in WSN. The goal of the POUCS technique is to choose cluster heads (CHs) and determine unequal cluster sizes. The POUCS technique derives a fitness function involving different input parameters to minimize energy consumption and maximize the lifetime of the network. To showcase the enhanced performance of the POUCS technique, a comprehensive experimental analysis takes place, and the detailed comparison study reported the better performance of the POUCS technique over the recent techniques.","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":"130735333","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 use of machine learning methods in cybersecurity is only one of many examples of how this once-emerging innovation has entered the mainstream. Anomaly-based identification of common assaults on vital infrastructures is only one instance of the various applications of malware analysis. Scholars are using machine learning-based identification in numerous cybersecurity solutions since signature-based approaches are inadequate at identifying zero-day threats or even modest modifications of established assaults. In this work, we introduce the machine-learning models-based security framework to detect cyber-attacks. This paper used three machine learning models Logistic Regression, Random Forest, and K-Nearest Neighbor This framework not only reduces the computational difficulty of the framework by minimizing the feature parameters, but it also performs well in terms of accuracy in forecasting unknown scenarios in the tests. Finally, we ran trials using cybersecurity datasets to measure the machine learning model's performance using metrics including precision, recall, and accuracy.
{"title":"Cybersecurity Detection Model using Machine Learning Techniques","authors":"Mustafa El .., Aaras Y Y.kraidi","doi":"10.54216/jcim.120104","DOIUrl":"https://doi.org/10.54216/jcim.120104","url":null,"abstract":"The use of machine learning methods in cybersecurity is only one of many examples of how this once-emerging innovation has entered the mainstream. Anomaly-based identification of common assaults on vital infrastructures is only one instance of the various applications of malware analysis. Scholars are using machine learning-based identification in numerous cybersecurity solutions since signature-based approaches are inadequate at identifying zero-day threats or even modest modifications of established assaults. In this work, we introduce the machine-learning models-based security framework to detect cyber-attacks. This paper used three machine learning models Logistic Regression, Random Forest, and K-Nearest Neighbor This framework not only reduces the computational difficulty of the framework by minimizing the feature parameters, but it also performs well in terms of accuracy in forecasting unknown scenarios in the tests. Finally, we ran trials using cybersecurity datasets to measure the machine learning model's performance using metrics including precision, recall, and accuracy.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"47 3 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":"129958623","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}
Existing cloud based security procedures are insufficient to manage the ever-increasing assaults in IoT due to the volume of data generated and the processing latency. IoT applications are vulnerable to cyberattacks, and some of these assaults might have catastrophic results if not stopped or mitigated quickly enough. As a result, IoT calls for self-protect security systems that can automatically interpret attacks in IoT traffic and efficiently handle the attack situation by activating the proper response quickly. Fog computing satisfies this need because it can embed the intelligent self-protection mechanism in the distributed fog nodes, allowing them to swiftly deal with the assault scenario and safeguard the IoT application with little in the way of human interaction. At the fog nodes, the forecasting method employs distributed Gaussian process regression. The cyber-attack may be predicted more quickly and with less mistake for both low- and high-rate attacks thanks to the local forecasting about the IoT traffic characteristics at fog node. One of the fundamental necessities of an IoT security mechanism is the ability to forecast attacks in a timely manner with a high degree of accuracy, and the simulation results highlight this fact.
{"title":"An Effective FOG Computing Based Distributed Forecasting of Cyber-Attacks in Internet of Things","authors":"Vandana Roy","doi":"10.54216/jcim.120201","DOIUrl":"https://doi.org/10.54216/jcim.120201","url":null,"abstract":"Existing cloud based security procedures are insufficient to manage the ever-increasing assaults in IoT due to the volume of data generated and the processing latency. IoT applications are vulnerable to cyberattacks, and some of these assaults might have catastrophic results if not stopped or mitigated quickly enough. As a result, IoT calls for self-protect security systems that can automatically interpret attacks in IoT traffic and efficiently handle the attack situation by activating the proper response quickly. Fog computing satisfies this need because it can embed the intelligent self-protection mechanism in the distributed fog nodes, allowing them to swiftly deal with the assault scenario and safeguard the IoT application with little in the way of human interaction. At the fog nodes, the forecasting method employs distributed Gaussian process regression. The cyber-attack may be predicted more quickly and with less mistake for both low- and high-rate attacks thanks to the local forecasting about the IoT traffic characteristics at fog node. One of the fundamental necessities of an IoT security mechanism is the ability to forecast attacks in a timely manner with a high degree of accuracy, and the simulation results highlight this fact.","PeriodicalId":169383,"journal":{"name":"Journal of Cybersecurity and Information Management","volume":"2 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":"128561278","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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