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Machine Learning for Computer and Cyber Security最新文献

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Detecting Controller Interlock-based Tax Evasion Groups in a Corporate Governance Network 公司治理网络中基于控制器联锁的逃税群体检测
Pub Date : 2019-02-07 DOI: 10.1201/9780429504044-12
Ruan Jianfei, Zheng Yan, Bo Dong, Q. Zheng
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引用次数: 0
A Bio-inspired Approach To Cyber Security 生物启发的网络安全方法
Pub Date : 2019-02-07 DOI: 10.1201/9780429504044-4
Siyakha N. Mthunzi, E. Benkhelifa, Tomasz Bosakowski, S. Hariri
Owing to a growing reliance on information, technology and connectivity, Cyberspace has become the lifeline and interactive place for modern life. As such, Cyber security challenges are a global phenomenon whose adverse implications are catastrophic. Cyberspace is complex and unpredictable; its global connectedness and an explosion of data increases the threat surface as cyber infrastructures become highly complex and dynamic. Managing, i.e. ensuring and assuring security in cyberspace requires inspiration from advanced complex systems. Through evolution, nature has developed natural propensities in complex systems (including animalia and plants) that enable survival through adaptation. Predation-avoidance and anti-predation techniques employed by non-extinct preys could be exploited/adopted as mechanisms for adaptation through their application in Cyber security. This chapter presents an overall review of the current state of the Cyber security landscape. In addition, it demonstrates through further review, significant trends towards bio-inspired approaches as unconventional solutions to problems in other fields. Drawing from survivable preys in nature, the chapter speculates solutions for Cyberspace and Cyber security as follows; given an old problem (Pold) with an old solutions (Sold), a new problem (Pnew) can be conceptualized with new partial and perhaps null solutions (Snew) in the solutions space Sold to Snew. Keywords: Bio-inspired, Artificial Life, Cyber security, Cyberdefense, Autonomic Computing, Survivability, Cloud Computing, Machine Learning, Predator-Prey.
由于对信息、技术和互联互通的依赖日益增强,网络空间已成为现代生活的生命线和互动场所。因此,网络安全挑战是一种全球性现象,其不利影响是灾难性的。网络空间复杂而不可预测;随着网络基础设施变得高度复杂和动态,其全球连通性和数据爆炸增加了威胁面。管理,即确保和保障网络空间的安全,需要从先进复杂的系统中汲取灵感。通过进化,自然界在复杂系统(包括动物和植物)中发展了自然倾向,使其能够通过适应而生存。非灭绝猎物采用的捕食避免和反捕食技术可以通过其在网络安全中的应用,作为适应机制加以利用/采用。本章对网络安全现状进行了全面回顾。此外,通过进一步的回顾,它展示了生物启发方法作为其他领域问题的非常规解决方案的重要趋势。根据自然界中可生存的猎物,本章推测了网络空间和网络安全的解决方案如下:给定一个旧问题(Pold)和一个旧解(Sold),一个新问题(Pnew)可以在解空间Sold to Snew中用新的偏解或者零解(Snew)概念化。关键词:仿生,人工生命,网络安全,网络防御,自主计算,生存能力,云计算,机器学习,捕食者-猎物
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引用次数: 13
Teaching Johnny to thwart phishing attacks 教约翰尼如何阻止网络钓鱼攻击
Pub Date : 2019-02-05 DOI: 10.1201/9780429504044-15
N. Arachchilage, M. A. Hameed
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引用次数: 1
Next Generation Adaptable Opportunistic Sensing-based Wireless Sensor Networks a Machine Learning perspective 基于机器学习视角的下一代可适应机会感知无线传感器网络
Pub Date : 2019-02-05 DOI: 10.1201/9780429504044-3
Jasminder Kaur Sandhu, A. Verma, P. Rana
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引用次数: 0
A Taxonomy of Bitcoin Security Issues and Defense Mechanisms 比特币安全问题和防御机制的分类
Pub Date : 2019-02-05 DOI: 10.1201/9780429504044-9
Prachi Gulihar, B. Gupta
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引用次数: 0
Early Detection and Prediction of Lung Cancer using Machine-learning Algorithms Applied on a Secure Healthcare Data-system Architecture 应用于安全医疗数据系统架构的机器学习算法的肺癌早期检测和预测
Pub Date : 2019-02-05 DOI: 10.1201/9780429504044-10
M. Alloghani, T. Baker, D. Al-Jumeily, A. Hussain, A. Aljaaf, J. Mustafina
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引用次数: 3
Metaheuristic Algorithms-based Feature Selection Approach for Intrusion Detection 基于元启发式算法的入侵检测特征选择方法
Pub Date : 2019-02-05 DOI: 10.1201/9780429504044-8
Ammar Almomani, Mohammed Alweshah, Saleh Al Khalayleh, M. Al-Refai, R. Qashi
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引用次数: 15
A Deep-dive on Machine Learning for Cyber Security Use Cases 机器学习在网络安全用例中的深入研究
Pub Date : 2019-02-05 DOI: 10.1201/9780429504044-6
R. Vinayakumar, K. Soman, P. Poornachandran, V. Menon
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引用次数: 10
Importance of providing Incentives and economic Solutions in It Security 在资讯科技保安方面提供激励和经济解决方案的重要性
Pub Date : 2019-02-05 DOI: 10.1201/9780429504044-14
Amrita Dahiya, B. Gupta
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引用次数: 1
Applications of a Model to Evaluate and Utilize Users’ Interactions in Online Social Networks 在线社交网络中用户交互评估与利用模型的应用
Pub Date : 2019-02-05 DOI: 10.1201/9780429504044-5
I. Alsmadi, Muhammad Al-Abdullah
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引用次数: 0
期刊
Machine Learning for Computer and Cyber Security
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