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
Big Data Analytics for Safe and Secure City
Setiyono,Suhono Harso Supangkat +1 more
TL;DR: In this article, the authors discuss how to identify, process and analyze safe and secure city parameters using big data analytic techniques, based on which they propose different answers to these questions, depending on unique geographical, economic or social circumstances.
Proceedings ArticleDOI
QuickAdapt: Scalable Adaptation for Big Data Cyber Security Analytics
Faheem Ullah,M. Ali Babar +1 more
TL;DR: An adaptation approach, QuickAdapt, to enable quick adaptation of a BDCA system using descriptive statistics of security events data and fuzzy rules to compose a system with a set of components to ensure optimal accuracy and response time is presented.
Proceedings ArticleDOI
Patent abstract analysis on Chinese big data
TL;DR: It is found that the development of big data in China's domestic can be divided into two distinct stages and it entered into a quickly growth stage after 2008.
Proceedings ArticleDOI
Analyst intuition inspired high velocity big data analysis using PCA ranked fuzzy k-means clustering with multi-layer perceptron (MLP) to obviate cyber security risk
TL;DR: The classification results are encouraging in segregating the types of attacks and compared to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection.
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.