Anomaly detection for cyber security applications

2016 ◽  
pp. 137-156 ◽  
Author(s):  
Patrick Rubin-Delanchy ◽  
Daniel J. Lawson ◽  
Nicholas A. Heard
Keyword(s):  
Anomaly Detection ◽  
Cyber Security ◽  
Security Applications

scholarly journals Bayesian Models Applied to Cyber Security Anomaly Detection Problems

2021 ◽  
Author(s):  
José A. Perusquía ◽  
Jim E. Griffin ◽  
Cristiano Villa
Keyword(s):  
Anomaly Detection ◽  
Cyber Security ◽  
Bayesian Models

Entropy analysis of n-grams and estimation of the number of meaningful language texts. Cyber security applications

2021 ◽  
Author(s):  
Anastasia Malashina
Keyword(s):  
Cyber Security ◽  
English Language ◽  
Heuristic Method ◽  
Entropy Analysis ◽  
Short Text ◽  
Security Applications ◽  
N Gram ◽  
Low Coverage

Abstract We estimate the n-gram entropies of English- language texts, using dictionaries and taking into account punctuation, and find a heuristic method for estimating the marginal entropy. We propose a method for evaluating the coverage of empirically generated dictionaries and an ap- proach to address the disadvantage of low coverage. In ad- dition, we compare the probability of obtaining a meaning- ful text by directly iterating through all possible n-grams of the alphabet and conclude that this is only possible for very short text segments.

scholarly journals DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications

2021 ◽  
Author(s):  
Dongqi Han ◽  
Zhiliang Wang ◽  
Wenqi Chen ◽  
Ying Zhong ◽  
Su Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Security Applications

Artificial Intelligence-Based Solutions for Cyber Security Problems

2021 ◽  
pp. 68-86
Author(s):  
Merve Yildirim
Keyword(s):  
Artificial Intelligence ◽  
Cyber Security ◽  
Cyber Attacks ◽  
Network Systems ◽  
Security Systems ◽  
Artificial Intelligence Techniques ◽  
Security Issues ◽  
Security Applications ◽  
Cyber Threats ◽  
Legal Risks

Due to its nature, cyber security is one of the fields that can benefit most from the techniques of artificial intelligence (AI). Under normal circumstances, it is difficult to write software to defend against cyber-attacks that are constantly developing and strengthening in network systems. By applying artificial intelligence techniques, software that can detect attacks and take precautions can be developed. In cases where traditional security systems are inadequate and slow, security applications developed with artificial intelligence techniques can provide better security against many complex cyber threats. Apart from being a good solution for cyber security problems, it also brings usage problems, legal risks, and concerns. This study focuses on how AI can help solve cyber security issues while discussing artificial intelligence threats and risks. This study also aims to present several AI-based techniques and to explain what these techniques can provide to solve problems in the field of cyber security.

scholarly journals A Survey of Deep Learning Methods for Cyber Security

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 122 ◽  
Author(s):  
Daniel Berman ◽  
Anna Buczak ◽  
Jeffrey Chavis ◽  
Cherita Corbett
Keyword(s):  
Deep Learning ◽  
Cyber Security ◽  
Generative Adversarial Networks ◽  
Restricted Boltzmann Machines ◽  
Domain Names ◽  
Survey Paper ◽  
Security Applications ◽  
Adversarial Networks ◽  
Network Intrusions ◽  
Broad Array

This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets.

scholarly journals Improved Testing of AI-Based Anomaly Detection Systems Using Synthetic Surveillance Data

Proceedings ◽  
2020 ◽  
Vol 59 (1) ◽  
pp. 9
Author(s):  
Antoine Chevrot ◽  
Alexandre Vernotte ◽  
Pierre Bernabe ◽  
Aymeric Cretin ◽  
Fabien Peureux ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Cyber Security ◽  
Surveillance Data ◽  
Automatic Identification ◽  
Identification System ◽  
Surveillance Systems ◽  
Sea Transport ◽  
Detection Model ◽  
Detection Systems ◽  
Anomalous Data

Major transportation surveillance protocols have not been specified with cyber security in mind and therefore provide no encryption nor identification. These issues expose air and sea transport to false data injection attacks (FDIAs), in which an attacker modifies, blocks or emits fake surveillance messages to dupe controllers and surveillance systems. There has been growing interest in conducting research on machine learning-based anomaly detection systems that address these new threats. However, significant amounts of data are needed to achieve meaningful results with this type of model. Raw, genuine data can be obtained from existing databases but need to be preprocessed before being fed to a model. Acquiring anomalous data is another challenge: such data is much too scarce for both the Automatic Dependent Surveillance–Broadcast (ADS-B) and the Automatic Identification System (AIS). Crafting anomalous data by hand, which has been the sole method applied to date, is hardly suitable for broad detection model testing. This paper proposes an approach built upon existing libraries and ideas that offers ML researchers the necessary tools to facilitate the access and processing of genuine data as well as to automatically generate synthetic anomalous surveillance data to constitute broad, elaborated test datasets. We demonstrate the usability of the approach by discussing work in progress that includes the reproduction of related work, creation of relevant datasets and design of advanced anomaly detection models for both domains of application.

scholarly journals Cyber-Physical Power System (CPPS): A Review on Modeling, Simulation, and Analysis With Cyber Security Applications

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 151019-151064
Author(s):  
Rajaa Vikhram Yohanandhan ◽  
Rajvikram Madurai Elavarasan ◽  
Premkumar Manoharan ◽  
Lucian Mihet-Popa
Keyword(s):  
Power System ◽  
Cyber Security ◽  
Security Applications ◽  
Modeling Simulation
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