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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Journal ArticleDOI
Benchmarks for Evaluating Anomaly Based Intrusion Detection Solutions
TL;DR: This paper proposes a benchmark that measures both accuracy and performance to produce objective metrics that can be used in the evaluation of each algorithm implementation, and uses this benchmark to compare accuracy as well as the performance of four different Anomaly-based IDS solutions based on various ML algorithms.
Journal ArticleDOI
Network Traffic Anomaly Detection via Deep Learning
Konstantina Fotiadou,Terpsichori-Helen Velivassaki,Artemis Voulkidis,Dimitrios Skias,Sofia Tsekeridou,Theodore Zahariadis +5 more
TL;DR: In this paper, the authors proposed novel deep learning formulations for detecting threats and alerts on network logs that were acquired by pfSense, an open-source software that acts as firewall on FreeBSD operating system.
Proceedings ArticleDOI
An intrusion detection system integrating network-level intrusion detection and host-level intrusion detection
TL;DR: This paper proposes an efficient scalable neural-network-based hybrid IDS framework with the combination of Host-level IDS (HIDS), and designed HIDS using word embedding and convolutional neural network.
Book ChapterDOI
Enhancing Network Security Via Machine Learning: Opportunities and Challenges.
Mahdi Amrollahi,Shahrzad Hadayeghparast,Hadis Karimipour,Farnaz Derakhshan,Gautam Srivastava +4 more
TL;DR: This document presents a review of the work related to network security via machine learning, which can assist in the analysis and storage of data in intrusion detection systems to help reduce both processing and training time.
Journal ArticleDOI
Feature selection and classification methods for vehicle tracking and detection
C. Ranjeeth Kumar,R. Anuradha +1 more
TL;DR: This research introduces the method namely Enhanced Convolution neural network with Support Vector Machine (ECNN-SVM) based vehicle detection, which has compact representation, capture information from multiple scales, encoded edge and structural information and can be efficient computation.
References
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Related Papers (5)
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
Robin Sommer,Vern Paxson +1 more