Journal ArticleDOI
Big Data Analytics for Security
Alvaro A. Cardenas,Pratyusa K. Manadhata,Sreeranga P. Rajan +2 more
- Vol. 11, Iss: 6, pp 74-76
TLDR
Big data is changing the landscape of security tools for network monitoring, security information and event management, and forensics; however, in the eternal arms race of attack and defense, security researchers must keep exploring novel ways to mitigate and contain sophisticated attackers.Abstract:
Big data is changing the landscape of security tools for network monitoring, security information and event management, and forensics; however, in the eternal arms race of attack and defense, security researchers must keep exploring novel ways to mitigate and contain sophisticated attackers.read more
Citations
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Proceedings ArticleDOI
A Study on Training Course for Staffs to Solve Expertise Issues with Multiple Criteria Decision Making Methodology – a Case of PC-based Information Security Monitoring Tool Development
Chen-Hua Fu,Chih-Yung Chen +1 more
TL;DR: A multiple criteria decision making (MCDM) process method to explore a PC-based information security monitoring tool (PISMT) development and trains the developing staff to determine which functionalities receive implementation priorities in a PISMT.
Privacy and Security of Big Data in THE Cloud
Narges Naderi,Hasan Alizadeh +1 more
TL;DR: The recent research and development on security and privacy in big data is surveyed and the existing technologies and a wide array of both earlier and state-of- the-art projects on cloud securi- ty and privacy are reviewed.
Book ChapterDOI
Data Management and Big Data Analytics
TL;DR: Big data as discussed by the authors is the data that exceeds storage and processing capacity of conventional database systems and is usually unstructured and qualitative in nature and can be used for various applications like sentiment analysis, increasing business, etc.
Journal ArticleDOI
Technological Revolution in Industrial Ecology
TL;DR: In this article , the authors explore and understand the role of the technological revolution in creating industrial ecology, and deepened knowledge with the recent trends and fashion to adopt emerging technological tools; (artificial intelligence, big data analytics, and blockchain technology) to sustain organizational/business productivity.
Proceedings ArticleDOI
ID-Based Signcryption Scheme Using Extended Chaotic Maps
Wang Cai-fen,Kang Burong +1 more
TL;DR: A novel Identity-based signcryption scheme is proposed using extended chaotic maps to solve the difficulty of chaos-based discrete logarithm (CDL) problem, which lies the foundation of the security of proposed ECM-IBSC scheme.
References
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Proceedings ArticleDOI
Beehive: large-scale log analysis for detecting suspicious activity in enterprise networks
Ting-Fang Yen,Alina Oprea,Kaan Onarlioglu,Todd Leetham,William Robertson,Ari Juels,Engin Kirda +6 more
TL;DR: A novel system, Beehive, that attacks the problem of automatically mining and extracting knowledge from the dirty log data produced by a wide variety of security products in a large enterprise, and is able to identify malicious events and policy violations which would otherwise go undetected.
Proceedings ArticleDOI
Toward a standard benchmark for computer security research: the worldwide intelligence network environment (WINE)
Tudor Dumitras,Darren Shou +1 more
TL;DR: The unique characteristics of the WINE data are reviewed, why rigorous benchmarking will provide fresh insights on the security arms race is discussed, and a research agenda for this area is proposed.
Proceedings ArticleDOI
BotCloud: Detecting botnets using MapReduce
TL;DR: This paper proposes a distributed computing framework that leverages a host dependency model and an adapted PageRank algorithm and reports experimental results from an open-source based Hadoop cluster and highlights the performance benefits when using real network traces from an Internet operator.
Journal Article
Using Large Scale Distributed Computing to Unveil Advanced Persistent Threats
Paul Giura,Wei Wang +1 more
TL;DR: This paper proposes a model of the APT detection problem as well as a methodology to implement it on a generic organization network and shows that this approach is feasible to process very large data sets and is flexible enough to accommodate any context processing algorithm, even to detect sophisticated attacks such as APT.