Pub Date : 2020-07-01DOI: 10.4018/ijcwt.2020070104
Mansoor Al-Gharibi, M. Warren, W. Yeoh
The purpose of this article is to discuss the risks and reasons of adopting the cloud computing in the critical infrastructure within the government context of cloud computing adoption. The article will also present examples of cloud computing adoption in the critical infrastructure domain. The data used in the paper was gathered from different academic, governmental, and online sources. It was found that, although there are risks involved in the cloud computing adoption, governments are deploying cloud computing using different deployment models and reaching high level of deployment within the critical infrastructure. The findings of this study suggest that it is not a question of adopting or not anymore, but the question of how to mitigate the risks involved after the deployment.
{"title":"Risks of Critical Infrastructure Adoption of Cloud Computing by Government","authors":"Mansoor Al-Gharibi, M. Warren, W. Yeoh","doi":"10.4018/ijcwt.2020070104","DOIUrl":"https://doi.org/10.4018/ijcwt.2020070104","url":null,"abstract":"The purpose of this article is to discuss the risks and reasons of adopting the cloud computing in the critical infrastructure within the government context of cloud computing adoption. The article will also present examples of cloud computing adoption in the critical infrastructure domain. The data used in the paper was gathered from different academic, governmental, and online sources. It was found that, although there are risks involved in the cloud computing adoption, governments are deploying cloud computing using different deployment models and reaching high level of deployment within the critical infrastructure. The findings of this study suggest that it is not a question of adopting or not anymore, but the question of how to mitigate the risks involved after the deployment.","PeriodicalId":41462,"journal":{"name":"International Journal of Cyber Warfare and Terrorism","volume":"26 1","pages":"47-58"},"PeriodicalIF":0.5,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74321359","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}
Pub Date : 2020-07-01DOI: 10.4018/ijcwt.2020070105
R. Nagahawatta, M. Warren, W. Yeoh
Cybersecurity is focused on helping the community to make knowledgeable decisions on its adaptation and mitigation. This survey evaluated the level of cybersecurity awareness and discernment amongst university students in Sri Lanka. The study was based on primary data collected through a questionnaire on awareness and the perception of cybersecurity from respondents at universities in Sri Lanka. The results indicated that experience and the level of cybersecurity awareness among university students in Sri Lanka are not significantly low, but there are some knowledge gaps with new threats. Further, the results showed that university students in Sri Lanka were able to identify cybercrime as a threat. These findings necessitate building awareness and developing capacity to improve student's knowledge on the cybersecurity subject especially if universities are to be used as a key focal point in cybersecurity awareness campaigns in Sri Lanka.
{"title":"A Study of Cyber Security Issues in Sri Lanka","authors":"R. Nagahawatta, M. Warren, W. Yeoh","doi":"10.4018/ijcwt.2020070105","DOIUrl":"https://doi.org/10.4018/ijcwt.2020070105","url":null,"abstract":"Cybersecurity is focused on helping the community to make knowledgeable decisions on its adaptation and mitigation. This survey evaluated the level of cybersecurity awareness and discernment amongst university students in Sri Lanka. The study was based on primary data collected through a questionnaire on awareness and the perception of cybersecurity from respondents at universities in Sri Lanka. The results indicated that experience and the level of cybersecurity awareness among university students in Sri Lanka are not significantly low, but there are some knowledge gaps with new threats. Further, the results showed that university students in Sri Lanka were able to identify cybercrime as a threat. These findings necessitate building awareness and developing capacity to improve student's knowledge on the cybersecurity subject especially if universities are to be used as a key focal point in cybersecurity awareness campaigns in Sri Lanka.","PeriodicalId":41462,"journal":{"name":"International Journal of Cyber Warfare and Terrorism","volume":"7 1","pages":"59-72"},"PeriodicalIF":0.5,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78674999","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}
Pub Date : 2020-07-01DOI: 10.4018/ijcwt.2020070101
W. Hutchinson
This speculative article examines the concept of deceiving autonomous drones that are controlled by artificial intelligence (AI) and can work without operational input from humans. This article examines the potential of autonomous drones, their implications and how deception could possibly be a defence against them and /or a means of gaining advantage. It posits that officially, no truly autonomous drone is operational now, yet the development of AI and other technologies could expand the capabilities of these devices, which will inevitably confront society with a number of deep ethical, legal, and philosophical issues. The article also examines the impact of autonomous drones and their targets in terms of the power/deception nexus. The impact of surveillance and kinetic impacts on the target populations is investigated. The use of swarms can make deception more difficult although security can be breached. The Internet of Things can be considered as based on the same model as a swarm and its impact on human behaviour indicates that deception or perhaps counter-deception should be considered as a defence. Finally, the issues raised are outlined. However, this article does not provide definitive answers but, hopefully, exposes a number of issues that will stimulate further discussion and research in this general area.
{"title":"Deceiving Autonomous Drones","authors":"W. Hutchinson","doi":"10.4018/ijcwt.2020070101","DOIUrl":"https://doi.org/10.4018/ijcwt.2020070101","url":null,"abstract":"This speculative article examines the concept of deceiving autonomous drones that are controlled by artificial intelligence (AI) and can work without operational input from humans. This article examines the potential of autonomous drones, their implications and how deception could possibly be a defence against them and /or a means of gaining advantage. It posits that officially, no truly autonomous drone is operational now, yet the development of AI and other technologies could expand the capabilities of these devices, which will inevitably confront society with a number of deep ethical, legal, and philosophical issues. The article also examines the impact of autonomous drones and their targets in terms of the power/deception nexus. The impact of surveillance and kinetic impacts on the target populations is investigated. The use of swarms can make deception more difficult although security can be breached. The Internet of Things can be considered as based on the same model as a swarm and its impact on human behaviour indicates that deception or perhaps counter-deception should be considered as a defence. Finally, the issues raised are outlined. However, this article does not provide definitive answers but, hopefully, exposes a number of issues that will stimulate further discussion and research in this general area.","PeriodicalId":41462,"journal":{"name":"International Journal of Cyber Warfare and Terrorism","volume":"123 10 1","pages":"1-14"},"PeriodicalIF":0.5,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77357353","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}
Pub Date : 2020-04-01DOI: 10.4018/ijcwt.2020040101
Naghmeh Moradpoor Sheykhkanloo, A. Hall
An insider threat can take on many forms and fall under different categories. This includes malicious insider, careless/unaware/uneducated/naïve employee, and the third-party contractor. Machine learning techniques have been studied in published literature as a promising solution for such threats. However, they can be biased and/or inaccurate when the associated dataset is hugely imbalanced. Therefore, this article addresses the insider threat detection on an extremely imbalanced dataset which includes employing a popular balancing technique known as spread subsample. The results show that although balancing the dataset using this technique did not improve performance metrics, it did improve the time taken to build the model and the time taken to test the model. Additionally, the authors realised that running the chosen classifiers with parameters other than the default ones has an impact on both balanced and imbalanced scenarios, but the impact is significantly stronger when using the imbalanced dataset.
{"title":"Insider Threat Detection Using Supervised Machine Learning Algorithms on an Extremely Imbalanced Dataset","authors":"Naghmeh Moradpoor Sheykhkanloo, A. Hall","doi":"10.4018/ijcwt.2020040101","DOIUrl":"https://doi.org/10.4018/ijcwt.2020040101","url":null,"abstract":"An insider threat can take on many forms and fall under different categories. This includes malicious insider, careless/unaware/uneducated/naïve employee, and the third-party contractor. Machine learning techniques have been studied in published literature as a promising solution for such threats. However, they can be biased and/or inaccurate when the associated dataset is hugely imbalanced. Therefore, this article addresses the insider threat detection on an extremely imbalanced dataset which includes employing a popular balancing technique known as spread subsample. The results show that although balancing the dataset using this technique did not improve performance metrics, it did improve the time taken to build the model and the time taken to test the model. Additionally, the authors realised that running the chosen classifiers with parameters other than the default ones has an impact on both balanced and imbalanced scenarios, but the impact is significantly stronger when using the imbalanced dataset.","PeriodicalId":41462,"journal":{"name":"International Journal of Cyber Warfare and Terrorism","volume":"61 s81","pages":"1-26"},"PeriodicalIF":0.5,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4018/ijcwt.2020040101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72389991","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}
Pub Date : 2020-04-01DOI: 10.4018/ijcwt.2020040105
Y. Imamverdiyev, F. Abdullayeva
In this article, a review and summarization of the emerging scientific approaches of deep learning (DL) on cybersecurity are provided, a structured and comprehensive overview of the various cyberattack detection methods is conducted, existing cyberattack detection methods based on DL is categorized. Methods covering attacks to deep learning based on generative adversarial networks (GAN) are investigated. The datasets used for the evaluation of the efficiency proposed by researchers for cyberattack detection methods are discussed. The statistical analysis of papers published on cybersecurity with the application of DL over the years is conducted. Existing commercial cybersecurity solutions developed on deep learning are described.
{"title":"Deep Learning in Cybersecurity: Challenges and Approaches","authors":"Y. Imamverdiyev, F. Abdullayeva","doi":"10.4018/ijcwt.2020040105","DOIUrl":"https://doi.org/10.4018/ijcwt.2020040105","url":null,"abstract":"In this article, a review and summarization of the emerging scientific approaches of deep learning (DL) on cybersecurity are provided, a structured and comprehensive overview of the various cyberattack detection methods is conducted, existing cyberattack detection methods based on DL is categorized. Methods covering attacks to deep learning based on generative adversarial networks (GAN) are investigated. The datasets used for the evaluation of the efficiency proposed by researchers for cyberattack detection methods are discussed. The statistical analysis of papers published on cybersecurity with the application of DL over the years is conducted. Existing commercial cybersecurity solutions developed on deep learning are described.","PeriodicalId":41462,"journal":{"name":"International Journal of Cyber Warfare and Terrorism","volume":"50 1","pages":"82-105"},"PeriodicalIF":0.5,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81909534","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}
{"title":"On the Behavior-Based Risk Communication Models in Crisis Management and Social Risks Minimization","authors":"Y. Kostyuchenko, V. Pushkar, O. Malysheva, M. Yuschenko","doi":"10.4018/ijcwt.2020040102","DOIUrl":"https://doi.org/10.4018/ijcwt.2020040102","url":null,"abstract":"Thearticleformulatesandcalibratesaformalmodelofriskcommunicationsintheframeworkof arisk-basedcommunityresilienceassessmentapproachintransformingsocietiesundercrisesand conflicts.Itwasdemonstratedthatperceptionofrisksisnotadequate.Thissituationisrecognizedas athreat,whichleadstoasignificantincreaseoflossesandtospreadingofwrongcrisismanagement practices.Toimprovedecision-makingatthepersonal,group,andpopulationlevels,abehavioralbasedcommunicationmodelhasbeenproposed.Themodifiedformofengagementintocollective actionsforsubstantiallyfractionalizedsocietyisproposed.Anumberofmodelsofactioncallsand acollectivedecision-makingunderstressconditionswithdynamiccommunicationareputforward. Onthebasisof thedevelopedmodel,waysofoptimizingcommunicationstrategiesareaimedat correspondingriskminimizationaredeveloped.Futureresearchdirectionsarehighlighted. KeyWORdS Behavioral Model, Communication Model, Community Resilience, Crisis Anthropology, Crisis, Decision Making, Global Transformations, Group Dynamics, Risk Communications, Risk Perception","PeriodicalId":41462,"journal":{"name":"International Journal of Cyber Warfare and Terrorism","volume":"26 1","pages":"27-45"},"PeriodicalIF":0.5,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4018/ijcwt.2020040102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72460413","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}
Pub Date : 2020-01-01DOI: 10.4018/978-1-7998-2466-4.ch038
Barend Pretorius, B. van Niekerk
Industrial control systems (ICS) or supervisory, control, and data acquisition (SCADA) systems drive many key components of the national infrastructure. It makes these control systems targets for cyber-attacks by terrorists and nation-states who wish to damage their target economically and socially, and cyber-criminals who blackmail the companies operating the infrastructure. Despite the high risk of leaving these systems exposed, providing adequate cyber-security is often challenging. The Stuxnet worm illustrated how vulnerable control systems potentially are when it bypassed a number of security mechanisms to cause physical damage to an Iranian nuclear facility. The article focuses on ICS/SCADA in South Africa discussing the unique challenges and legislation relate to securing control system in the South Africa. A governance and security framework for overcoming these challenges are proposed.
{"title":"Cyber-Security for ICS/SCADA","authors":"Barend Pretorius, B. van Niekerk","doi":"10.4018/978-1-7998-2466-4.ch038","DOIUrl":"https://doi.org/10.4018/978-1-7998-2466-4.ch038","url":null,"abstract":"Industrial control systems (ICS) or supervisory, control, and data acquisition (SCADA) systems drive many key components of the national infrastructure. It makes these control systems targets for cyber-attacks by terrorists and nation-states who wish to damage their target economically and socially, and cyber-criminals who blackmail the companies operating the infrastructure. Despite the high risk of leaving these systems exposed, providing adequate cyber-security is often challenging. The Stuxnet worm illustrated how vulnerable control systems potentially are when it bypassed a number of security mechanisms to cause physical damage to an Iranian nuclear facility. The article focuses on ICS/SCADA in South Africa discussing the unique challenges and legislation relate to securing control system in the South Africa. A governance and security framework for overcoming these challenges are proposed.","PeriodicalId":41462,"journal":{"name":"International Journal of Cyber Warfare and Terrorism","volume":"86 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73468594","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}
Pub Date : 2020-01-01DOI: 10.4018/978-1-7998-2466-4.ch051
M. Hussain, M. Beg, M. S. Alam, S. Laskar
Electric vehicles (EVs) are key players for transport oriented smart cities (TOSC) powered by smart grids (SG) because they help those cities to become greener by reducing vehicle emissions and carbon footprint. In this article, the authors analyze different use-cases to show how big data analytics (BDA) can play vital role for successful electric vehicle (EV) to smart grid (SG) integration. Followed by this, this article presents an edge computing model and highlights the advantages of employing such distributed edge paradigms towards satisfying the store, compute and networking (SCN) requirements of smart EV applications in TOSCs. This article also highlights the distinguishing features of the edge paradigm, towards supporting BDA activities in EV to SG integration in TOSCs. Finally, the authors provide a detailed overview of opportunities, trends, and challenges of both these computing techniques. In particular, this article discusses the deployment challenges and state-of-the-art solutions in edge privacy and edge forensics.
{"title":"Big Data Analytics Platforms for Electric Vehicle Integration in Transport Oriented Smart Cities","authors":"M. Hussain, M. Beg, M. S. Alam, S. Laskar","doi":"10.4018/978-1-7998-2466-4.ch051","DOIUrl":"https://doi.org/10.4018/978-1-7998-2466-4.ch051","url":null,"abstract":"Electric vehicles (EVs) are key players for transport oriented smart cities (TOSC) powered by smart grids (SG) because they help those cities to become greener by reducing vehicle emissions and carbon footprint. In this article, the authors analyze different use-cases to show how big data analytics (BDA) can play vital role for successful electric vehicle (EV) to smart grid (SG) integration. Followed by this, this article presents an edge computing model and highlights the advantages of employing such distributed edge paradigms towards satisfying the store, compute and networking (SCN) requirements of smart EV applications in TOSCs. This article also highlights the distinguishing features of the edge paradigm, towards supporting BDA activities in EV to SG integration in TOSCs. Finally, the authors provide a detailed overview of opportunities, trends, and challenges of both these computing techniques. In particular, this article discusses the deployment challenges and state-of-the-art solutions in edge privacy and edge forensics.","PeriodicalId":41462,"journal":{"name":"International Journal of Cyber Warfare and Terrorism","volume":"46 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80371753","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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