The Internet has been widely adopted by UAE citizens, with one of the highest penetration rates in the world, yet the potential to become a victim of cybercrime is high. A quantitative cross-sectional online survey strategy was adopted to collect the data on cybercrime and cybervictimisation to support the identification of the most appropriate approaches to address cybervictimisation in the UAE. The results indicate the key patterns of cybervictimisation in the UAE as identity fraud, cyberharassment and cyber attack, with impacts mainly on a psychological/emotional level. Evidence pointed to strong associations between online activity and time spent online and cybervictimisation. There was a significant perception that legislative measures did not sufficiently address cybervictimisation and punitive measures were lenient. Logistic regression analysis indicated likelihood of cybervictimisation was associated with technical guardianship, online behaviour and usage, computer proficiency, time spent online, region of residence and gender. Classification and regression tree analysis identified different patterns indicating user characteristics consisting of security measures and online routine behaviour. The findings support the development of a comprehensive Incident Response Framework (IRF), which is needed to inform legal authorities and victim service provision.
{"title":"Cyber Victimization: UAE as a Case Study","authors":"Abdelrahman Abdalla Humaid Al-Ali, Ameer Al-Nemrat","doi":"10.1109/CCC.2017.14","DOIUrl":"https://doi.org/10.1109/CCC.2017.14","url":null,"abstract":"The Internet has been widely adopted by UAE citizens, with one of the highest penetration rates in the world, yet the potential to become a victim of cybercrime is high. A quantitative cross-sectional online survey strategy was adopted to collect the data on cybercrime and cybervictimisation to support the identification of the most appropriate approaches to address cybervictimisation in the UAE. The results indicate the key patterns of cybervictimisation in the UAE as identity fraud, cyberharassment and cyber attack, with impacts mainly on a psychological/emotional level. Evidence pointed to strong associations between online activity and time spent online and cybervictimisation. There was a significant perception that legislative measures did not sufficiently address cybervictimisation and punitive measures were lenient. Logistic regression analysis indicated likelihood of cybervictimisation was associated with technical guardianship, online behaviour and usage, computer proficiency, time spent online, region of residence and gender. Classification and regression tree analysis identified different patterns indicating user characteristics consisting of security measures and online routine behaviour. The findings support the development of a comprehensive Incident Response Framework (IRF), which is needed to inform legal authorities and victim service provision.","PeriodicalId":367472,"journal":{"name":"2017 Cybersecurity and Cyberforensics Conference (CCC)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124219714","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}
Malicious software (malware) attacks are on the rise with the explosion of Internet of Things (IoT) worldwide. With the proliferation of Big Data, it becomes a time consuming process to use various automatic approaches and techniques that are available to detect and capture malware thoroughly. Visualisation techniques can support the malware analysis process for performing the similarity comparisons and summarisation of possible malware in such Big Data contexts. In this paper, we design a novel classification of malware using visualization of similarity matrices. The prime motivation of our proposal is to detect unknown malwares that undergo the innumerable obfuscations of extended x86 IA-32 (opcodes) in order to evade from traditional detection methods. Overall, the high accuracy of classification achieved with our proposed model can be observed visually due to significant dissimilarity of the behaviour patterns exhibited by malware opcodes as compared to benign opcodes.
{"title":"Classification of Malware Using Visualisation of Similarity Matrices","authors":"S. Venkatraman, M. Alazab","doi":"10.1109/CCC.2017.11","DOIUrl":"https://doi.org/10.1109/CCC.2017.11","url":null,"abstract":"Malicious software (malware) attacks are on the rise with the explosion of Internet of Things (IoT) worldwide. With the proliferation of Big Data, it becomes a time consuming process to use various automatic approaches and techniques that are available to detect and capture malware thoroughly. Visualisation techniques can support the malware analysis process for performing the similarity comparisons and summarisation of possible malware in such Big Data contexts. In this paper, we design a novel classification of malware using visualization of similarity matrices. The prime motivation of our proposal is to detect unknown malwares that undergo the innumerable obfuscations of extended x86 IA-32 (opcodes) in order to evade from traditional detection methods. Overall, the high accuracy of classification achieved with our proposed model can be observed visually due to significant dissimilarity of the behaviour patterns exhibited by malware opcodes as compared to benign opcodes.","PeriodicalId":367472,"journal":{"name":"2017 Cybersecurity and Cyberforensics Conference (CCC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130447039","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 desire for eHealth systems (technology) is ever growing as public institutions (governments), healthcare providers, and its users (patients) see the gains that could possibly arise from having systems like databases of patient health information in a single place which will facilitate the way healthcare can be access by patients and their caregivers. The aim of this paper is to provide a supportive environment for the health and social care workplace with special reference in the Primary Care sector in England on the impact and changes to the information governance toolkit (IGTK) as a result of the new European General Data Protection Regulation (GDPR) which will be implemented in full from May 2018 as agreed by the UK Government thereby replacing the UK Data Protection Act of 1998. These challenges will also include the implementation of the National Data Guardian (NDG) review of data security and opt-outs amongst others.
{"title":"The Impact of the new European General Data Protection Regulation (GDPR) on the Information Governance Toolkit in Health and Social Care with Special Reference to Primary Care in England","authors":"I. Shu, H. Jahankhani","doi":"10.1109/CCC.2017.16","DOIUrl":"https://doi.org/10.1109/CCC.2017.16","url":null,"abstract":"The desire for eHealth systems (technology) is ever growing as public institutions (governments), healthcare providers, and its users (patients) see the gains that could possibly arise from having systems like databases of patient health information in a single place which will facilitate the way healthcare can be access by patients and their caregivers. The aim of this paper is to provide a supportive environment for the health and social care workplace with special reference in the Primary Care sector in England on the impact and changes to the information governance toolkit (IGTK) as a result of the new European General Data Protection Regulation (GDPR) which will be implemented in full from May 2018 as agreed by the UK Government thereby replacing the UK Data Protection Act of 1998. These challenges will also include the implementation of the National Data Guardian (NDG) review of data security and opt-outs amongst others.","PeriodicalId":367472,"journal":{"name":"2017 Cybersecurity and Cyberforensics Conference (CCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130844960","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}
Forensic analysts typically search through a large volume of data in different locations looking for possible evidences. The process can be very tedious and time consuming. Automating the process of searching for possible evidences can be very useful even if this can be as an initial stage before further deep human or manual analysis. Toward this goal, we developed a tool to automate extracting forensic artifacts from network resources. We evaluated the tool using artifacts of network packets and switch memory dumps. We found out that their is a need to balance between customization and level of details or accuracy that such tools can produce. This means that it will be impractical to develop a one-for-all tool or else such tool will be very large, complex and possible inefficient.
{"title":"A Model Based Approach for the Extraction of Network Forensic Artifacts","authors":"I. Alsmadi, M. Alazab","doi":"10.1109/CCC.2017.13","DOIUrl":"https://doi.org/10.1109/CCC.2017.13","url":null,"abstract":"Forensic analysts typically search through a large volume of data in different locations looking for possible evidences. The process can be very tedious and time consuming. Automating the process of searching for possible evidences can be very useful even if this can be as an initial stage before further deep human or manual analysis. Toward this goal, we developed a tool to automate extracting forensic artifacts from network resources. We evaluated the tool using artifacts of network packets and switch memory dumps. We found out that their is a need to balance between customization and level of details or accuracy that such tools can produce. This means that it will be impractical to develop a one-for-all tool or else such tool will be very large, complex and possible inefficient.","PeriodicalId":367472,"journal":{"name":"2017 Cybersecurity and Cyberforensics Conference (CCC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131667935","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 present work presents an evaluation between two methods of Root to Local (R2L) intrusion detection, by examining changes in mean of the TCP source bytes. Two statistical change detection techniques utilized for this purpose: the Exponential Weighted Moving Average (EWMA) control chart, as well as the tabular Cumulative sum (CUSUM) control chart, while for both detection techniques the experimental dataset used was the NSL-KDD. For the EWMA chart evaluation a sequence of eight attacks took place at specified instances, which were clearly detected by adjusting the parameters L and λ. For the CUSUM chart evaluation, two cases were examined: the first case with one attack at a specified instance and the second case with three attacks. In both cases the detections were succesfuly achieved. A limitation that concerned both detection techniques was that the examined TCP source bytes size was in the range of (0 - 1000). The EWMA chart was evaluated as the more efficient technique as far as the accuracy of the detection is concerned.
{"title":"A Statistical Approach Based on EWMA and CUSUM Control Charts for R2L Intrusion Detection","authors":"D. Sklavounos, Aloysius Edoh, Markos Plytas","doi":"10.1109/CCC.2017.15","DOIUrl":"https://doi.org/10.1109/CCC.2017.15","url":null,"abstract":"The present work presents an evaluation between two methods of Root to Local (R2L) intrusion detection, by examining changes in mean of the TCP source bytes. Two statistical change detection techniques utilized for this purpose: the Exponential Weighted Moving Average (EWMA) control chart, as well as the tabular Cumulative sum (CUSUM) control chart, while for both detection techniques the experimental dataset used was the NSL-KDD. For the EWMA chart evaluation a sequence of eight attacks took place at specified instances, which were clearly detected by adjusting the parameters L and λ. For the CUSUM chart evaluation, two cases were examined: the first case with one attack at a specified instance and the second case with three attacks. In both cases the detections were succesfuly achieved. A limitation that concerned both detection techniques was that the examined TCP source bytes size was in the range of (0 - 1000). The EWMA chart was evaluated as the more efficient technique as far as the accuracy of the detection is concerned.","PeriodicalId":367472,"journal":{"name":"2017 Cybersecurity and Cyberforensics Conference (CCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115026173","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 Golden Ratio is the most irrational among irrational numbers. Its successive continued fraction converges with the Fibonacci sequence F(n+1)/F(n) are the slowest to approximate to its actual value.This paper proposes a new method to determine the Golden Ratio with infinite precision and compares the new method with the well-known Fibonacci sequence method. The results show that our proposed method outperforms Fibonacci sequence method. Hence, cryptosystems that use Fibonacci numbers would be much faster using our new method of Golden Ratio computation. This paves way in improving counter measures from security attacks since higher precisions of the Golden Ratio method can take place in cryptographic operations very quickly when used in elliptic curve cryptosystems, power analysis security, and other applications.
{"title":"A New Method of Golden Ratio Computation for Faster Cryptosystems","authors":"A. Overmars, S. Venkatraman","doi":"10.1109/CCC.2017.12","DOIUrl":"https://doi.org/10.1109/CCC.2017.12","url":null,"abstract":"The Golden Ratio is the most irrational among irrational numbers. Its successive continued fraction converges with the Fibonacci sequence F(n+1)/F(n) are the slowest to approximate to its actual value.This paper proposes a new method to determine the Golden Ratio with infinite precision and compares the new method with the well-known Fibonacci sequence method. The results show that our proposed method outperforms Fibonacci sequence method. Hence, cryptosystems that use Fibonacci numbers would be much faster using our new method of Golden Ratio computation. This paves way in improving counter measures from security attacks since higher precisions of the Golden Ratio method can take place in cryptographic operations very quickly when used in elliptic curve cryptosystems, power analysis security, and other applications.","PeriodicalId":367472,"journal":{"name":"2017 Cybersecurity and Cyberforensics Conference (CCC)","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130426932","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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