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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Journal ArticleDOI
A Game Theory Study of Big Data Analytics in Internet of Things
TL;DR: In this paper , a non-cooperative game theory model with incentive and payment mechanisms is constructed, and the multi-parties interaction process in BDA-IoT is simulated.
Book ChapterDOI
Big Data Value Chain: Making Sense of the Challenges
TL;DR: A Big data value chain where the value adding stages are decoupled from the technological requirements of data processing is introduced, and it is argued that through viewing the stages of value accumulation, it is possible to identify challenges in dealing with Big Data that cannot be mitigated through technological developments.
Posted Content
Digital Forensics vs. Anti-Digital Forensics: Techniques, Limitations and Recommendations.
TL;DR: In this article, the authors present a holistic view from a literature point of view over the digital forensics domain and also discuss the rise of the anti-anti-forensics as a new forensics protection mechanism against antiforensics activities.
Proceedings ArticleDOI
Combining spark and snort technologies for detection of network attacks and anomalies: assessment of performance for the big data framework
Igor Kotenko,Nikolay Komashinsky +1 more
TL;DR: The proposed combined framework for processing security data using parallel computing environment and measuring the performance of the implemented system for detection of network attacks and anomalies confirm its high efficiency for analyzing network traffic and security events.
Proceedings Article
Security and Privacy Technique in Big Data: A Review
TL;DR: In this article , a review of the literature contains over eight years of the techniques proposed by the researcher for security and privacy in Big-Data and the benefits and difficulties in terms of confidentiality and security.
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.