Security and Artificial Intelligence最新文献

Physically Unclonable Functions and AI: Two Decades of Marriage 物理上不可克隆的功能和人工智能:二十年的婚姻

Security and Artificial Intelligence Pub Date : 2020-08-26 DOI: 10.1007/978-3-030-98795-4_5

F. Ganji, Shahin Tajik

引用次数: 4

Artificial Neural Networks and Fault Injection Attacks 人工神经网络与故障注入攻击

Security and Artificial Intelligence Pub Date : 2020-08-17 DOI: 10.1007/978-3-030-98795-4_4

Shahin Tajik, F. Ganji

引用次数: 6

Deep Learning Backdoors 深度学习后门

Security and Artificial Intelligence Pub Date : 2020-07-16 DOI: 10.1007/978-3-030-98795-4_13

Shaofeng Li, Shiqing Ma, Minhui Xue, Benjamin Zi Hao Zhao

引用次数: 26

Adversarial machine learning 对抗性机器学习

Security and Artificial Intelligence Pub Date : 2019-04-11 DOI: 10.1145/2046684.2046692

Ling Huang, A. Joseph, B. Nelson, Benjamin I. P. Rubinstein, J. D. Tygar

引用次数: 1277

Automating Reverse Engineering with Machine Learning Techniques 用机器学习技术自动化逆向工程

Security and Artificial Intelligence Pub Date : 2014-11-07 DOI: 10.1145/2666652.2666665

Blake Anderson, C. Storlie, Micah Yates, Aaron McPhall

{"title":"Automating Reverse Engineering with Machine Learning Techniques","authors":"Blake Anderson, C. Storlie, Micah Yates, Aaron McPhall","doi":"10.1145/2666652.2666665","DOIUrl":"https://doi.org/10.1145/2666652.2666665","url":null,"abstract":"Malware continues to be an ongoing threat, with millions of unique variants created every year. Unlike the majority of this malware, Advanced Persistent Threat (APT) malware is created to target a specific network or set of networks and has a precise objective, e.g. exfiltrating sensitive data. While 0-day malware detectors are a good start, they do not help the reverse engineers better understand the threats attacking their networks. Understanding the behavior of malware is often a time sensitive task, and can take anywhere between several hours to several weeks. Our goal is to automate the task of identifying the general function of the subroutines in the function call graph of the program to aid the reverse engineers. Two approaches to model the subroutine labels are investigated, a multiclass Gaussian process and a multiclass support vector machine. The output of these methods is the probability that the subroutine belongs to a certain class of functionality (e.g., file I/O, exploit, etc.). Promising initial results, illustrating the efficacy of this method, are presented on a sample of 201 subroutines taken from two malicious families.","PeriodicalId":225009,"journal":{"name":"Security and Artificial Intelligence","volume":"280 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117342737","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: 10

A Plea for Utilising Synthetic Data when Performing Machine Learning Based Cyber-Security Experiments 在执行基于机器学习的网络安全实验时使用合成数据的请求

Security and Artificial Intelligence Pub Date : 2014-11-07 DOI: 10.1145/2666652.2666663

Sebastian Abt, Harald Baier

引用次数: 18

Adversarial Active Learning 对抗性主动学习

Security and Artificial Intelligence Pub Date : 2014-11-07 DOI: 10.1145/2666652.2666656

Brad Miller

{"title":"Adversarial Active Learning","authors":"Brad Miller","doi":"10.1145/2666652.2666656","DOIUrl":"https://doi.org/10.1145/2666652.2666656","url":null,"abstract":"Active learning is an area of machine learning examining strategies for allocation of finite resources, particularly human labeling efforts and to an extent feature extraction, in situations where available data exceeds available resources. In this open problem paper, we motivate the necessity of active learning in the security domain, identify problems caused by the application of present active learning techniques in adversarial settings, and propose a framework for experimentation and implementation of active learning systems in adversarial contexts. More than other contexts, adversarial contexts particularly need active learning as ongoing attempts to evade and confuse classifiers necessitate constant generation of labels for new content to keep pace with adversarial activity. Just as traditional machine learning algorithms are vulnerable to adversarial manipulation, we discuss assumptions specific to active learning that introduce additional vulnerabilities, as well as present vulnerabilities that are amplified in the active learning setting. Lastly, we present a software architecture, Security-oriented Active Learning Testbed (SALT), for the research and implementation of active learning applications in adversarial contexts.","PeriodicalId":225009,"journal":{"name":"Security and Artificial Intelligence","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128683304","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: 79

Leveraging Machine Learning to Improve Unwanted Resource Filtering 利用机器学习改进不需要的资源过滤

Security and Artificial Intelligence Pub Date : 2014-11-07 DOI: 10.1145/2666652.2666662

Sruti Bhagavatula, Christopher W. Dunn, Chris Kanich, Minaxi Gupta, Brian D. Ziebart

引用次数: 40

Using extreme learning machine for intrusion detection in a big data environment 利用极限学习机进行大数据环境下的入侵检测

Security and Artificial Intelligence Pub Date : 2014-11-07 DOI: 10.1145/2666652.2666664

Junlong Xiang, M. Westerlund, Dusan Sovilj, G. Pulkkis

引用次数: 37

Detecting Malicious Domains via Graph Inference 通过图推理检测恶意域

Security and Artificial Intelligence Pub Date : 2014-11-07 DOI: 10.1145/2666652.2666659

P. Manadhata, Sandeep Yadav, P. Rao, Bill G. Horne

{"title":"Detecting Malicious Domains via Graph Inference","authors":"P. Manadhata, Sandeep Yadav, P. Rao, Bill G. Horne","doi":"10.1145/2666652.2666659","DOIUrl":"https://doi.org/10.1145/2666652.2666659","url":null,"abstract":"Enterprises routinely collect terabytes of security relevant data, e.g., network logs and application logs, for several reasons such as cheaper storage, forensic analysis, and regulatory compliance. Analyzing these big data sets to identify actionable security information and hence to improve enterprise security, however, is a relatively unexplored area. In this paper, we introduce a system to detect malicious domains accessed by an enterprise’s hosts from the enterprise’s HTTP proxy logs. Specifically, we model the detection problem as a graph inference problemwe construct a host-domain graph from proxy logs, seed the graph with minimal ground truth information, and then use belief propagation to estimate the marginal probability of a domain being malicious. Our experiments on data collected at a global enterprise show that our approach scales well, achieves high detection rates with low false positive rates, and identifies previously unknown malicious domains when compared with state-of-the-art systems. Since malware infections inside an enterprise spread primarily via malware domain accesses, our approach can be used to detect and prevent malware infections.","PeriodicalId":225009,"journal":{"name":"Security and Artificial Intelligence","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129181032","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: 94