2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)最新文献_第3页

Potential cyber-attacks against global oil supply chain 针对全球石油供应链的潜在网络攻击

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CYBERSA.2015.7166137

M. Nasir, Shizra Sultan, S. Nefti-Meziani, U. Manzoor

引用次数: 3

Empirical framework for situation awareness measurement techniques in network defense 网络防御中态势感知测量技术的经验框架

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166132

Maria Evangelopoulou, C. W. Johnson

引用次数: 5

Cyber security: A game-theoretic analysis of defender and attacker strategies in defacing-website games 网络安全:毁损网站游戏中防御者和攻击者策略的博弈论分析

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166127

Palvi Aggarwal, Antra Grover, Saumya Singh, Zahid Maqbool, V. Pammi, Varun Dutt

{"title":"Cyber security: A game-theoretic analysis of defender and attacker strategies in defacing-website games","authors":"Palvi Aggarwal, Antra Grover, Saumya Singh, Zahid Maqbool, V. Pammi, Varun Dutt","doi":"10.1109/CyberSA.2015.7166127","DOIUrl":"https://doi.org/10.1109/CyberSA.2015.7166127","url":null,"abstract":"The rate at which cyber-attacks are increasing globally portrays a terrifying picture upfront. The main dynamics of such attacks could be studied in terms of the actions of attackers and defenders in a cyber-security game. However currently little research has taken place to study such interactions. In this paper we use behavioral game theory and try to investigate the role of certain actions taken by attackers and defenders in a simulated cyber-attack scenario of defacing a website. We choose a Reinforcement Learning (RL) model to represent a simulated attacker and a defender in a 2×4 cyber-security game where each of the 2 players could take up to 4 actions. A pair of model participants were computationally simulated across 1000 simulations where each pair played at most 30 rounds in the game. The goal of the attacker was to deface the website and the goal of the defender was to prevent the attacker from doing so. Our results show that the actions taken by both the attackers and defenders are a function of attention paid by these roles to their recently obtained outcomes. It was observed that if attacker pays more attention to recent outcomes then he is more likely to perform attack actions. We discuss the implication of our results on the evolution of dynamics between attackers and defenders in cyber-security games.","PeriodicalId":432356,"journal":{"name":"2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122198673","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: 7

Cross-domain situational awareness and collaborative working for cyber security 跨域态势感知和网络安全协同工作

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166110

M. Hall, Devon David Hansen, K. Jones

引用次数: 7

An SQL-like query tool for data anonymization and outsourcing 一个类似sql的查询工具，用于数据匿名化和外包

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166130

M. Nassar, A. A. Orabi, Marwan Doha, Bechara al Bouna

引用次数: 2

Security management in Wireless Sensor Networks 无线传感器网络中的安全管理

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166112

S. Neogy

引用次数: 8

Developing a secure remote patient monitoring system 开发安全的远程患者监护系统

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166123

S. Neogy, Sayantani Saha

引用次数: 0

Risk analysis in cyber situation awareness using Bayesian approach 基于贝叶斯方法的网络态势感知风险分析

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166119

Moyinoluwa Abidemi Bode, S. Oluwadare, B. K. Alese, A. Thompson

{"title":"Risk analysis in cyber situation awareness using Bayesian approach","authors":"Moyinoluwa Abidemi Bode, S. Oluwadare, B. K. Alese, A. Thompson","doi":"10.1109/CyberSA.2015.7166119","DOIUrl":"https://doi.org/10.1109/CyberSA.2015.7166119","url":null,"abstract":"The unpredictable cyber attackers and threats have to be detected in order to determine the outcome of risk in a network environment. This work develops a Bayesian network classifier to analyse the network traffic in a cyber situation. It is a tool that aids reasoning under uncertainty to determine certainty. It further analyze the level of risk using a modified risk matrix criteria. The classifier developed was experimented with various records extracted from the KDD Cup'99 dataset with 490,021 records. The evaluations showed that the Bayesian Network classifier is a suitable model which resulted in same performance level for classifying the Denial of Service (DoS) attacks with Association Rule Mining while as well as Genetic Algorithm, the Bayesian Network classifier performed better in classifying probe and User to Root (U2R) attacks and classified DoS equally. The result of the classification showed that Bayesian network classifier is a classification model that thrives well in network security. Also, the level of risk analysed from the adapted risk matrix showed that DoS attack has the most frequent occurrence and falls in the generally unacceptable risk zone.","PeriodicalId":432356,"journal":{"name":"2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133763615","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

An immune intelligent approach for security assurance 安全保障的免疫智能方法

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 2015-06-08 DOI: 10.1109/CyberSA.2015.7166116

A. Enache, Mihai-Gabriel Ionita, V. Sgârciu

引用次数: 3

Analysis of malware behavior: Type classification using machine learning 恶意软件行为分析:使用机器学习进行类型分类

2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA) Pub Date : 1900-01-01 DOI: 10.1109/CyberSA.2015.7166128

Radu S. Pirscoveanu, Steven S. Hansen, Thor M. T. Larsen, Matija Stevanovic, J. Pedersen, A. Czech

{"title":"Analysis of malware behavior: Type classification using machine learning","authors":"Radu S. Pirscoveanu, Steven S. Hansen, Thor M. T. Larsen, Matija Stevanovic, J. Pedersen, A. Czech","doi":"10.1109/CyberSA.2015.7166128","DOIUrl":"https://doi.org/10.1109/CyberSA.2015.7166128","url":null,"abstract":"Malicious software has become a major threat to modern society, not only due to the increased complexity of the malware itself but also due to the exponential increase of new malware each day. This study tackles the problem of analyzing and classifying a high amount of malware in a scalable and automatized manner. We have developed a distributed malware testing environment by extending Cuckoo Sandbox that was used to test an extensive number of malware samples and trace their behavioral data. The extracted data was used for the development of a novel type classification approach based on supervised machine learning. The proposed classification approach employs a novel combination of features that achieves a high classification rate with a weighted average AUC value of 0.98 using Random Forests classifier. The approach has been extensively tested on a total of 42,000 malware samples. Based on the above results it is believed that the developed system can be used to pre-filter novel from known malware in a future malware analysis system.","PeriodicalId":432356,"journal":{"name":"2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)","volume":"47 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125718997","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