Journal ArticleDOI
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Anna L. Buczak,Erhan Guven +1 more
TLDR
The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/ DM for cyber security is presented, and some recommendations on when to use a given method are provided.Abstract:
This survey paper describes a focused literature survey of machine learning (ML) and data mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial descriptions of each ML/DM method are provided. Based on the number of citations or the relevance of an emerging method, papers representing each method were identified, read, and summarized. Because data are so important in ML/DM approaches, some well-known cyber data sets used in ML/DM are described. The complexity of ML/DM algorithms is addressed, discussion of challenges for using ML/DM for cyber security is presented, and some recommendations on when to use a given method are provided.read more
Citations
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Posted Content
Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design
TL;DR: A hybrid computing architecture design named PIM-CapsNet is proposed, which preserves GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet, while pipelining with an off-chip in-memory acceleration solution that effectively tackles routing procedure's inefficiency.
Posted Content
Hardening Random Forest Cyber Detectors Against Adversarial Attacks
TL;DR: In this paper, the authors presented an original methodology for countering adversarial perturbations targeting intrusion detection systems based on random forests, and integrated the proposed defense method in a cyber detector analyzing network traffic.
Book ChapterDOI
A Network Intrusion Detection System for Concept Drifting Network Traffic Data.
Giuseppina Andresini,Annalisa Appice,Corrado Loglisci,Vincenzo Belvedere,Domenico Redavid,Donato Malerba +5 more
TL;DR: Wang et al. as discussed by the authors proposed a concept drift detection mechanism to discover incoming traffic that deviates from the past and trigger the fine-tuning of the deep neural network architecture to fit the drifted data.
Journal ArticleDOI
DS-kNN: An Intrusion Detection System Based on a Distance Sum-Based K-Nearest Neighbors
Redha Taguelmimt,Rachid Beghdad +1 more
TL;DR: A new and an easy-to-implement approach to intrusion detection, named distance sum-based k-nearest neighbors (DS-kNN), which is an improved version of k-NN classifier that performs better than the original k-nn algorithm in terms of accuracy, detection rate, false positive, and attacks classification.
Journal ArticleDOI
Deep learning based cyber bullying early detection using distributed denial of service flow
Muhammad Hassan Zaib,Faisal Bashir,Kashif Naseer Qureshi,Sumaira Kausar,Muhammad Rizwan,Gwanggil Jeon +5 more
TL;DR: This research proposes a methodology where it can detect early Denial of service (DoS) and Distributed Denials of Service (DDoS) attacks and takes multiple DoS and DDoS single flow to validate the methodology.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Fuzzy sets
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TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
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Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
Robin Sommer,Vern Paxson +1 more