Proceedings ArticleDOI
AI^2: Training a Big Data Machine to Defend
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TLDR
The system presents four key features: a big data behavioral analytics platform, an outlier detection system, a mechanism to obtain feedback from security analysts, and a supervised learning module, which is capable of learning to defend against unseen attacks.Abstract:
We present AI2, an analyst-in-the-loop security system where Analyst Intuition (AI) is put together with state-of-the-art machine learning to build a complete end-to-end Artificially Intelligent solution (AI). The system presents four key features: a big data behavioral analytics platform, an outlier detection system, a mechanism to obtain feedback from security analysts, and a supervised learning module. We validate our system with a real-world data set consisting of 3.6 billion log lines and 70.2 million entities. The results show that the system is capable of learning to defend against unseen attacks. With respect to unsupervised outlier analysis, our system improves the detection rate in 2.92× and reduces false positives by more than 5×.read more
Citations
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Journal ArticleDOI
N-BaIoT—Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders
Yair Meidan,Michael Bohadana,Yael Mathov,Yisroel Mirsky,Asaf Shabtai,Dominik Breitenbacher,Yuval Elovici +6 more
TL;DR: N-BaIoT as discussed by the authors is a network-based anomaly detection method for the IoT that extracts behavior snapshots of the network and uses deep autoencoders to detect anomalous network traffic from compromised IoT devices.
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Survey of Attack Projection, Prediction, and Forecasting in Cyber Security
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References
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Journal ArticleDOI
Anomaly detection: A survey
TL;DR: This survey tries to provide a structured and comprehensive overview of the research on anomaly detection by grouping existing techniques into different categories based on the underlying approach adopted by each technique.
Journal ArticleDOI
A Survey of Outlier Detection Methodologies
Victoria J. Hodge,Jim Austin +1 more
TL;DR: A survey of contemporary techniques for outlier detection is introduced and their respective motivations are identified and distinguish their advantages and disadvantages in a comparative review.
Proceedings ArticleDOI
Query by committee
TL;DR: It is suggested that asymptotically finite information gain may be an important characteristic of good query algorithms, in which a committee of students is trained on the same data set.
Book
Outlier Analysis
TL;DR: Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit.
Book ChapterDOI
Heterogenous uncertainty sampling for supervised learning
David D. Lewis,Jason A. Catlett +1 more
TL;DR: This work test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program) and finds that the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger.
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