Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics最新文献_第2页

Identifying Key Cyber-Physical Terrain 识别关键网络物理地形

Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics Pub Date : 2017-03-24 DOI: 10.1145/3041008.3041015

Brian Thompson, Richard E. Harang

引用次数: 4

Feature Cultivation in Privileged Information-augmented Detection 特权信息增强检测中的特征培养

Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics Pub Date : 2017-03-24 DOI: 10.1145/3041008.3041018

Z. B. Celik, P. Mcdaniel, R. Izmailov

{"title":"Feature Cultivation in Privileged Information-augmented Detection","authors":"Z. B. Celik, P. Mcdaniel, R. Izmailov","doi":"10.1145/3041008.3041018","DOIUrl":"https://doi.org/10.1145/3041008.3041018","url":null,"abstract":"Modern detection systems use sensor outputs available in the deployment environment to probabilistically identify attacks. These systems are trained on past or synthetic feature vectors to create a model of anomalous or normal behavior. Thereafter, run-time collected sensor outputs are compared to the model to identify attacks (or the lack of attack). While this approach to detection has been proven to be effective in many environments, it is limited to training on only features that can be reliably collected at detection time. Hence, they fail to leverage the often vast amount of ancillary information available from past forensic analysis and post-mortem data. In short, detection systems do not train (and thus do not learn from) features that are unavailable or too costly to collect at run-time. Recent work proposed an alternate model construction approach that integrates forensic \"privilege\" information---features reliably available at training time, but not at run-time---to improve accuracy and resilience of detection systems. In this paper, we further evaluate two of proposed techniques to model training with privileged information: knowledge transfer, and model influence. We explore the cultivation of privileged features, the efficiency of those processes and their influence on the detection accuracy. We observe that the improved integration of privileged features makes the resulting detection models more accurate. Our evaluation shows that use of privileged information leads to up to 8.2% relative decrease in detection error for fast-flux bot detection over a system with no privileged information, and 5.5% for malware classification.","PeriodicalId":137012,"journal":{"name":"Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127202959","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: 6

MCDefender: Toward Effective Cyberbullying Defense in Mobile Online Social Networks MCDefender:面向移动在线社交网络的有效网络欺凌防御

Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics Pub Date : 2017-03-24 DOI: 10.1145/3041008.3041013

Nishant Vishwamitra, Xiang Zhang, Jonathan Tong, Hongxin Hu, Feng Luo, Robin M. Kowalski, Joseph P. Mazer

{"title":"MCDefender: Toward Effective Cyberbullying Defense in Mobile Online Social Networks","authors":"Nishant Vishwamitra, Xiang Zhang, Jonathan Tong, Hongxin Hu, Feng Luo, Robin M. Kowalski, Joseph P. Mazer","doi":"10.1145/3041008.3041013","DOIUrl":"https://doi.org/10.1145/3041008.3041013","url":null,"abstract":"Cyberbullying in Online Social Networks (OSNs) has emerged as one of the most severe social concerns. Cyberbullying can be described as a form of bullying where a perpetrator uses electronic means to cause harm to a victim. With the proliferation of smartphone technology in present times, there has been a steady shift in the usage of OSNs from traditional computers to mobile devices. However, existing systems that defend against cyberbullying are largely applicable only to traditional computing platforms and cannot be directly applied to detect cyberbullying in mobile platforms. To address such a critical issue, we investigate an innovative mobile cyberbullying defense system called MCDefender that can effectively detect and prevent cyberbullying in mobile OSNs. We first analyze the key challenges that differentiate cyberbullying conditions in traditional and mobile platforms. We then investigate a two-level detection mechanism for comprehensive cyberbullying detection in mobile OSNs where cyberbullying can be quickly detected before a cyberbullying message is sent through a mobile device and hidden cyberbullying attacks can be also detected through a more fine-grained and context-aware analysis. To demonstrate the feasibility of our approach, we implement and evaluate an Android application based on MCDefender. Our evaluation results show that our mobile application can detect cyberbullying with a high accuracy of 98.9% for OSNs.","PeriodicalId":137012,"journal":{"name":"Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125225748","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: 13

An Internal/Insider Threat Score for Data Loss Prevention and Detection 数据丢失预防和检测的内部/内部威胁评分

Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics Pub Date : 2017-03-24 DOI: 10.1145/3041008.3041011

Kyrre Wahl Kongsgård, N. Nordbotten, Federico Mancini, P. Engelstad

引用次数: 7

Session details: Attacks and New Detection Method Session 会话详细信息:攻击和新检测方法会话

Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics Pub Date : 2017-03-24 DOI: 10.1145/3252735

Wenyaw Chan

引用次数: 0

Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics 第三届美国计算机学会安全与隐私分析国际研讨会论文集

Proceedings of the 3rd ACM on International Workshop on Security And Privacy Analytics Pub Date : 1900-01-01 DOI: 10.1145/3041008

引用次数: 0