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2022 Cyber Research Conference - Ireland (Cyber-RCI)最新文献

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Beware of Titles: Analysing Media Reporting of Cybercrime in UK and UAE 谨防标题:分析英国和阿联酋网络犯罪的媒体报道
2022 Cyber Research Conference - Ireland (Cyber-RCI) Pub Date : 2022-04-25 DOI: 10.1109/Cyber-RCI55324.2022.10032681
Maitha Khaled Al Mazrouei, Da Piper, L. Connolly
{"title":"Beware of Titles: Analysing Media Reporting of Cybercrime in UK and UAE","authors":"Maitha Khaled Al Mazrouei, Da Piper, L. Connolly","doi":"10.1109/Cyber-RCI55324.2022.10032681","DOIUrl":"https://doi.org/10.1109/Cyber-RCI55324.2022.10032681","url":null,"abstract":"In this paper, media reporting of cybercrime victims (with a specific focus on organisations) was examined. For this purpose, data were collected from media outlets in the UK and UAE. A basic premise of this paper is that media has the ability to restrict readers to either ‘opposing’ or ‘favouring’ views about the subject of the news. Research findings demonstrated that victim organisations were mostly shaped within a negative media frame in a given time period. Comparative analysis between UK and UAE news media showed the usage of similar language in portraying victim organisations.","PeriodicalId":378205,"journal":{"name":"2022 Cyber Research Conference - Ireland (Cyber-RCI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126860038","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}
引用次数: 0
Cyber exclusions: An investigation into the cyber insurance coverage gap 网络排除:对网络保险覆盖面差距的调查
2022 Cyber Research Conference - Ireland (Cyber-RCI) Pub Date : 2022-04-25 DOI: 10.1109/Cyber-RCI55324.2022.10032678
Frank Cremer, Barry Sheehan, Michel Fortmann, Martin Mullins, Finbarr Murphy
{"title":"Cyber exclusions: An investigation into the cyber insurance coverage gap","authors":"Frank Cremer, Barry Sheehan, Michel Fortmann, Martin Mullins, Finbarr Murphy","doi":"10.1109/Cyber-RCI55324.2022.10032678","DOIUrl":"https://doi.org/10.1109/Cyber-RCI55324.2022.10032678","url":null,"abstract":"The importance of cyber insurance as a tool for financial resilience to mitigate the accelerating corporate losses caused by cybercrime is growing. However, there exists a lack of standardization and mutual understanding in cyber insurance policies. With less than a third of cyber insurance claims paid in 2017 in the U.S., there exists a significant gap between the cyber risks businesses need to cover and those actually covered through their cyber insurance policies. This research uses inductive qualitative content analysis to examine the existing exclusions in the terms and conditions of 40 German cyber insurers and compares the summarized results with existing cyber risk events. We posit that the lack of understanding of cyber policy wordings related to cyber risks is a significant problem for companies that could suffer significant losses. The resulting categorization of 15 exclusions and interrelationships with cyber risk events will support businesses, the insurance industry, and researchers in their efforts to understand, measure, and manage cyber risk.","PeriodicalId":378205,"journal":{"name":"2022 Cyber Research Conference - Ireland (Cyber-RCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130716623","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}
引用次数: 0
The Application of Reinforcement Learning to the FlipIt Security Game 强化学习在FlipIt安全游戏中的应用
2022 Cyber Research Conference - Ireland (Cyber-RCI) Pub Date : 2022-04-25 DOI: 10.1109/Cyber-RCI55324.2022.10032686
Xue Yang, E. Howley, M. Schukat
{"title":"The Application of Reinforcement Learning to the FlipIt Security Game","authors":"Xue Yang, E. Howley, M. Schukat","doi":"10.1109/Cyber-RCI55324.2022.10032686","DOIUrl":"https://doi.org/10.1109/Cyber-RCI55324.2022.10032686","url":null,"abstract":"Advanced Persistent Threat is a new, sophisticated type of targeted attack which is continuous, long-living, and stealthy. It has proven to be difficult to detect and defend against in the cloud-based environment by traditional methods, calling for more advanced security technologies. FlipIt is a two-player security game where an attacker and defender compete to control a sensitive resource in advanced scenarios such as APTs. Its robustness against APT attacks is outstanding. We model the FlipIt game as a Markov Decision Process and apply reinforcement learning to the framework. The goal is to find an optimal adaptive strategy for a player to compete against any unknown opponent in a FlipIt game with incomplete information. This means the best result for a player is to maximize the ownership of the resource with minimum cost. We perform experiments on single-agent and multi-agent scenarios, respectively. We further extend the model to involve noisy information and consider the openness of the game. Our experimental analysis proves that in a two-player FlipIt game, an adaptive player can automatically learn and find an optimal strategy using only the last move information of the opponent, who moves with a non-adaptive strategy (i.e. a periodic strategy with random noise). The parameters related to the random noise we considered affect the average benefit for each player. In addition, we consider the openness of the game in which new participants are introduced individually at random time steps with a certain probability. In this case, the model is generalized from two-player to n-player, and the convergence of the optimal strategy learned by each player is confirmed. Moreover, we demonstrate that varying the probability of adding an additional player does not affect the convergence but changes the average benefits for players.","PeriodicalId":378205,"journal":{"name":"2022 Cyber Research Conference - Ireland (Cyber-RCI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122237755","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}
引用次数: 0
Convolutional Neural Network for Software Vulnerability Detection 基于卷积神经网络的软件漏洞检测
2022 Cyber Research Conference - Ireland (Cyber-RCI) Pub Date : 2022-04-25 DOI: 10.1109/Cyber-RCI55324.2022.10032684
Kaixi Yang, Paul Miller, J. Martinez-del-Rincon
{"title":"Convolutional Neural Network for Software Vulnerability Detection","authors":"Kaixi Yang, Paul Miller, J. Martinez-del-Rincon","doi":"10.1109/Cyber-RCI55324.2022.10032684","DOIUrl":"https://doi.org/10.1109/Cyber-RCI55324.2022.10032684","url":null,"abstract":"Exploitable vulnerabilities in software are one of the root causes of cybercrime, leading to financial losses, reputational damage, and wider security breaches for both enterprise and consumers. Furthermore, checking for vulnerabilities in software is no longer a human-scale problem due to code volume and complexity. To help address this problem, our work presents a deep learning model able to identify risk signals in Java source code and output a classification for a program as either vulnerable or safe. Sequences of raw Java opcodes are used to train a convolutional neural network that automatically encapsulates discriminative characteristics of a program that are then used for the prediction. Compared to traditional machine learning methods, this approach requires no prior knowledge of the software vulnerability domain, nor any hand-crafted input features. When evaluated on the publicly available benchmark dataset Juliet Test Suite containing 38520 vulnerable and 38806 safe programs, our method achieves an F1 score of 0.92.","PeriodicalId":378205,"journal":{"name":"2022 Cyber Research Conference - Ireland (Cyber-RCI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121917858","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}
引用次数: 0
Employee Cyber-Security Awareness Training (CSAT) Programs in Ireland’s Financial Institutions 爱尔兰金融机构员工网络安全意识培训(CSAT)项目
2022 Cyber Research Conference - Ireland (Cyber-RCI) Pub Date : 2022-04-25 DOI: 10.1109/Cyber-RCI55324.2022.10032683
Reda Jouaibi, Aisling Keenan Gaylard, Brian A. Lee
{"title":"Employee Cyber-Security Awareness Training (CSAT) Programs in Ireland’s Financial Institutions","authors":"Reda Jouaibi, Aisling Keenan Gaylard, Brian A. Lee","doi":"10.1109/Cyber-RCI55324.2022.10032683","DOIUrl":"https://doi.org/10.1109/Cyber-RCI55324.2022.10032683","url":null,"abstract":"This paper presents work in progress to analyse the use and effectiveness of cyber-security awareness training (CSAT) programs adopted in Ireland’s financial institutions. The findings to date demonstrate that some Irish financial institutions provide CSAT programs for their employees, but their adoption and usage by employees and effectiveness remains unsure. We conclude that further research is required of CSAT and whether these programs alleviate cyber threats.","PeriodicalId":378205,"journal":{"name":"2022 Cyber Research Conference - Ireland (Cyber-RCI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123964676","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}
引用次数: 0
A contribution towards the regulation of anonymised datasets within the framework of GDPR 对GDPR框架内匿名数据集监管的贡献
2022 Cyber Research Conference - Ireland (Cyber-RCI) Pub Date : 2022-04-25 DOI: 10.1109/Cyber-RCI55324.2022.10032680
F. Britton, Seamus Dowling, Mark Frain
{"title":"A contribution towards the regulation of anonymised datasets within the framework of GDPR","authors":"F. Britton, Seamus Dowling, Mark Frain","doi":"10.1109/Cyber-RCI55324.2022.10032680","DOIUrl":"https://doi.org/10.1109/Cyber-RCI55324.2022.10032680","url":null,"abstract":"The European Union’s General Data Protection Regulation legal framework outlines data anonymisation as an effective method to help data processors meet their legal obligations. Once personal data has been anonymised, GDPR no longer recognises it as personal data and it becomes free to be processed unregulated. The potential capacity for re-identification of any anonymised dataset brings into question the value of anonymisation and whether it should be regulated to ensure the privacy of data subjects. This paper identifies and reviews some of these problems, challenges the current set of regulations, and recommends maintaining regulation over anonymised datasets, and enforcing systems of traceability and erasure of datasets over time, which would increase the value of utilising anonymisation and its capacity to be a reliable system for data privacy.","PeriodicalId":378205,"journal":{"name":"2022 Cyber Research Conference - Ireland (Cyber-RCI)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115432398","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}
引用次数: 0
Cybersecurity Threats, Vulnerabilities, Mitigation Measures in Industrial Control and Automation Systems: A Technical Review 工业控制和自动化系统中的网络安全威胁、漏洞和缓解措施:技术综述
2022 Cyber Research Conference - Ireland (Cyber-RCI) Pub Date : 2022-04-25 DOI: 10.1109/Cyber-RCI55324.2022.10032665
Alfred Ocaka, D. O. Briain, S. Davy, Keara Barrett
{"title":"Cybersecurity Threats, Vulnerabilities, Mitigation Measures in Industrial Control and Automation Systems: A Technical Review","authors":"Alfred Ocaka, D. O. Briain, S. Davy, Keara Barrett","doi":"10.1109/Cyber-RCI55324.2022.10032665","DOIUrl":"https://doi.org/10.1109/Cyber-RCI55324.2022.10032665","url":null,"abstract":"Cyberattacks on Industrial Control and Automation Systems (ICAS) have significantly increased in recent years due to IT and OT convergence. Traditionally, ICAS were isolated systems running proprietary protocols on specialised software and hardware. However, to improve business processes and efficiency, ICAS vendors are adopting smart technologies such as Industrial Internet of Things (IIOT), Machine to Machine (M2M), Digital Twin, cloud computing, and Artificial Intelligence (AI). This integration presents new vulnerabilities in ICAS that can be exploited by threat actors. ICAS are utilised in critical infrastructure and widely used in power, nuclear plant, water, oil, natural gas, and manufacturing industries. Therefore, cyberattacks on these systems can pose a significant threat to humans and the environment, disrupt social services, cause financial losses, and threaten national security. Because of these threats, numerous mitigation measures are being implemented to protect ICAS from cyberattacks. However, security experience and expertise have demonstrated that we can never fully protect a system and one should never propose that their solution will fully protect. Rather one can claim that their solution/mitigation technique adds a layer to the defence in depth approach. This paper discusses the different cybersecurity standards and frameworks for ICAS, investigates the existing threats and vulnerabilities, and methods of securing ICAS","PeriodicalId":378205,"journal":{"name":"2022 Cyber Research Conference - Ireland (Cyber-RCI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130357173","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}
引用次数: 1
Data Augmentation for Opcode Sequence Based Malware Detection 基于操作码序列的恶意软件检测的数据增强
2022 Cyber Research Conference - Ireland (Cyber-RCI) Pub Date : 2021-06-22 DOI: 10.1109/Cyber-RCI55324.2022.10032676
Niall McLaughlin, J. M. D. Rincón
{"title":"Data Augmentation for Opcode Sequence Based Malware Detection","authors":"Niall McLaughlin, J. M. D. Rincón","doi":"10.1109/Cyber-RCI55324.2022.10032676","DOIUrl":"https://doi.org/10.1109/Cyber-RCI55324.2022.10032676","url":null,"abstract":"In this paper we study data augmentation for opcode sequence based Android malware detection. Data augmentation has been successfully used in many areas of deep-learning to significantly improve model performance. Typically, data augmentation simulates realistic variations in data to increase the apparent diversity of the training-set. However, for opcode-based malware analysis it is not immediately clear how to apply data augmentation. Hence we first study the use of fixed transformations, then progress to adaptive methods. We propose a novel data augmentation method – Self-Embedding Language Model Augmentation – that uses a malware detection network’s own opcode embedding layer to measure opcode similarity for adaptive augmentation. To the best of our knowledge this is the first paper to carry out a systematic study of different augmentation methods for opcode sequence based Android malware classification.","PeriodicalId":378205,"journal":{"name":"2022 Cyber Research Conference - Ireland (Cyber-RCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116970840","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}
引用次数: 5
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