Security and Artificial Intelligence最新文献_第4页

Towards modeling the behavior of physical intruders in a region monitored by a wireless sensor network 在无线传感器网络监测的区域中，对物理入侵者的行为进行建模

Security and Artificial Intelligence Pub Date : 2010-10-08 DOI: 10.1145/1866423.1866425

Pranav Krishnamoorthy, M. Wright

{"title":"Towards modeling the behavior of physical intruders in a region monitored by a wireless sensor network","authors":"Pranav Krishnamoorthy, M. Wright","doi":"10.1145/1866423.1866425","DOIUrl":"https://doi.org/10.1145/1866423.1866425","url":null,"abstract":"A priority task for homeland security is the coverage of large spans of open border that cannot be continuously physically monitored for intrusion. Low-cost monitoring solutions based on wireless sensor networks have been identified as an effective means to perform perimeter monitoring. An ad-hoc wireless sensor network scattered near a border could be used to perform surveillance over a large area with relatively little human intervention. Determining the effectiveness of such an autonomous network in detecting and thwarting an intelligent intruder is a difficult task. We propose a model for an intelligent attacker that attempts to find a detection-free path in a region with sparse sensing coverage. In particular, we apply reinforcement learning (RL) - a machine learning approach - for our model. RL algorithms are well suited for scenarios in which specifying and finding an optimal solution is difficult. By using RL, our attacker can easily adapt to new scenarios by translating constraints into rewards. We compare our RL-based technique to a reasonable heuristic in simulation. Our results suggest that our RL-based attacker model is significantly more effective, and therefore more realistic, than the heuristic approach.","PeriodicalId":225009,"journal":{"name":"Security and Artificial Intelligence","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124779990","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: 2

Synthetic security policy generation via network traffic clustering 通过网络流量集群生成综合安全策略

Security and Artificial Intelligence Pub Date : 2010-10-08 DOI: 10.1145/1866423.1866433

T. Samak, E. Al-Shaer

引用次数: 6

Cheap and automated socio-technical attacks based on social networking sites 基于社交网站的廉价和自动化的社会技术攻击

Security and Artificial Intelligence Pub Date : 2010-10-08 DOI: 10.1145/1866423.1866435

Markus Huber, M. Mulazzani, S. Schrittwieser, E. Weippl

引用次数: 11

Eliminating routing protocol anomalies in wireless sensor networks using AI techniques 利用人工智能技术消除无线传感器网络中的路由协议异常

Security and Artificial Intelligence Pub Date : 2010-10-08 DOI: 10.1145/1866423.1866426

Z. Bankovic, J. C. Vallejo, P. Malagón, Á. Araujo, Jose M. Moya

引用次数: 4

Lexical feature based phishing URL detection using online learning 基于词法特征的网络钓鱼URL在线检测

Security and Artificial Intelligence Pub Date : 2010-10-08 DOI: 10.1145/1866423.1866434

Aaron Blum, Brad Wardman, T. Solorio, Gary Warner

引用次数: 199

The limits of automatic OS fingerprint generation 操作系统自动指纹生成的限制

Security and Artificial Intelligence Pub Date : 2010-10-08 DOI: 10.1145/1866423.1866430

David W. Richardson, S. Gribble, Tadayoshi Kohno

{"title":"The limits of automatic OS fingerprint generation","authors":"David W. Richardson, S. Gribble, Tadayoshi Kohno","doi":"10.1145/1866423.1866430","DOIUrl":"https://doi.org/10.1145/1866423.1866430","url":null,"abstract":"Remote operating system fingerprinting relies on implementation differences between OSs to identify the specific variant executing on a remote host. Because these differences can be subtle and difficult to find, most fingerprinting tools require expert manual effort to construct discriminative fingerprints and classification models. In prior work, Caballero et al. proposed a promising technique to eliminate manual intervention: the automatic generation of fingerprints using an approach similar to fuzz testing [6]. Their work evaluated the technique in a small-scale, carefully controlled test environment. In this paper, we re-examine automatic OS fingerprinting in a more challenging large-scale scenario to better understand the viability of the technique. In contrast to the prior work, we find that automatic fingerprint generation suffers from several limitations and technical hurdles that can limit its effectiveness, particularly in more demanding, realistic environments.\u0000 We use machine learning algorithms from the well-known Weka [11] data mining toolkit to automatically generate fingerprints over 329 different machine instances, and we compare the accuracy of our automatically generated fingerprints to Nmap. Our results suggest that overfitting to non-OS-specific behavioral differences, the indistinguishability of different OS variants, the biasing of an automatic tool to the makeup of the training data, and the lack of ability of an automatic tool to exploit protocol and software semantics significantly limit the usefulness of this technique in practice. Automatic techniques can help identify candidate signatures, but our results suggest that manual expertise will remain an integral part of fingerprint generation.","PeriodicalId":225009,"journal":{"name":"Security and Artificial Intelligence","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126492054","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: 33

Empirical evaluation of authorship obfuscation using JGAAP 使用JGAAP对作者身份混淆进行实证评估

Security and Artificial Intelligence Pub Date : 2010-10-08 DOI: 10.1145/1866423.1866427

P. Juola, Darren Vescovi

引用次数: 34

Relational network-service clustering analysis with set evidences 利用集合证据进行关系网络服务聚类分析

Security and Artificial Intelligence Pub Date : 2010-10-08 DOI: 10.1145/1866423.1866432

Li Pu, B. Faltings, Qiang Yang, Derek Hao Hu

{"title":"Relational network-service clustering analysis with set evidences","authors":"Li Pu, B. Faltings, Qiang Yang, Derek Hao Hu","doi":"10.1145/1866423.1866432","DOIUrl":"https://doi.org/10.1145/1866423.1866432","url":null,"abstract":"Network administrators are faced with a large amount of network data that they need to sift through to analyze user behaviors and detect anomalies. Through a network monitoring tool, we obtained TCP and UDP connection records together with additional information of the associated users and software in an enterprise network. Instead of using traditional payload inspection techniques, we propose a method that clusters such network traffic data by using relations between entities so that it can be analyzed for frequent behaviors and anomalies. Relational methods like Markov Logic Networks is able to avoid the feature extraction stage and directly handle multi-relation situations. We extend the common pairwise representation in relational models by adopting set evidence to build a better objective for the network service clustering problem. The automatic clustering process helps the administrator filter out normal traffic in shorter time and get an abstract overview of opening transport layer ports in the whole network, which is beneficial for assessing network security risks. Experimental results on synthetic and real datasets suggest that our method is able to discover underlying services and anomalies (malware or abused ports) with good interpretations.","PeriodicalId":225009,"journal":{"name":"Security and Artificial Intelligence","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127607970","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

Adjustable autonomy for cross-domain entitlement decisions 跨域授权决策的可调整自主权

Security and Artificial Intelligence Pub Date : 2010-10-08 DOI: 10.1145/1866423.1866436

J. Beal, J. Webb, M. Atighetchi

引用次数: 5

Towards security policy decisions based on context profiling 基于上下文分析的安全策略决策

Security and Artificial Intelligence Pub Date : 2010-10-08 DOI: 10.1145/1866423.1866428

Markus Miettinen, N. Asokan

引用次数: 7