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
A New Approach to Track Multiple Vehicles With the Combination of Robust Detection and Two Classifiers
TL;DR: The qualitative and quantitative analysis of the proposed method not only effectively removed the ghost shadows, and improved the detection accuracy and real-time performance, but also was robust to deal with the occlusion of multiple vehicles in various traffic scenes.
Journal ArticleDOI
Hashed Needham Schroeder Industrial IoT based Cost Optimized Deep Secured data transmission in cloud
Jafar A. Alzubi,Ramachandran Manikandan,Omar A. Alzubi,Issa Qiqieh,Robbi Rahim,Deepak Gupta,Ashish Khanna +6 more
TL;DR: Hashed Needham Schroeder's Cost Optimized Deep Machine Learning (HNS-CODML) method for secure Industrial IoT data transmissions via cloud environment has been proposed by indicating the necessity of providing Industrial IoT security using machine learning technique.
Journal ArticleDOI
A Survey on Network Security-Related Data Collection Technologies
TL;DR: This paper briefly introduces network security-related data, including its definition and characteristics, and the applications of network data collection, and provides the requirements and objectives for security- related data collection and presents a taxonomy of data collection technologies.
Journal ArticleDOI
Detecting web attacks with end-to-end deep learning
TL;DR: The results show that the proposed approach can efficiently and accurately detect attacks, including SQL injection, cross-site scripting, and deserialization, with minimal domain knowledge and little labeled training data.
Proceedings ArticleDOI
IoT Device Identification via Network-Flow Based Fingerprinting and Learning
TL;DR: This paper analyzes a sequence of packets from its high-level network traffic and extracts unique flow-based features to create a fingerprint for each device, and proposes a security system model design that enables enforcement of rules for constraining the IoT device communications.
References
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