Security and Communication Networks

Machine Learning and Applied Cryptography


Publishing date
01 Jan 2021
Status
Published
Submission deadline
04 Sep 2020

Lead Editor

1La Trobe University, Melbourne, Australia

2Qatar University, Doha, Qatar

3Georgia Gwinnett College, Lawrenceville, USA

4HITEC University, Taxila, Pakistan

5King Khalid University, Abha, Saudi Arabia


Machine Learning and Applied Cryptography

Description

Machine Learning (ML) and cryptography have many things in common; the amount of data to be handled and large search spaces for instance. The application of ML in cryptography is not new, but with over 3 quintillion bytes of data being generated every day, it is now more relevant to apply ML techniques in cryptography than ever before.

ML generally automates analytical model building to continuously learn and adapt to the large amount of data being fed as input. ML techniques can be used to indicate the relationship between the input and output data created by cryptosystems. ML techniques such as Boosting and Mutual Learning can be used to create the private cryptographic key over the public and insecure channel. Methods such as Naive Bayesian, support vector machine, and AdaBoost, which come under the category of classification, can be used to classify the encrypted traffic and objects into steganograms used in steganography. Besides the application in cryptography, which is an art of creating secure systems for encrypting/decrypting confidential data, the ML techniques can also be applied in cryptanalysis, which is an art of breaking cryptosystems to perform certain side-channel attacks.

The aim of this Special Issue is to create a volume of recent works on advances in all aspects of ML applications in cryptosystems and cryptanalysis. Both original research articles, and review articles discussing the current state of the art, are welcomed.

Potential topics include but are not limited to the following:

  • Machine learning to analyze cryptosystems
  • Machine learning to perform cryptanalysis
  • Machine learning based intrusion detection
  • Deep learning for security and privacy
  • Data mining for authentication
  • End-to-end system security models
  • Machine learning based key exchange framework
  • Machine learning based threat and attack model generation
  • Nonlinear aspects of cryptosystems
  • Adversarial machine learning for data security

Articles

  • Special Issue
  • - Volume 2022
  • - Article ID 9797604
  • - Editorial

Machine Learning and Applied Cryptography

Amir Anees | Iqtadar Hussain | ... | Sajjad Shaukat
  • Special Issue
  • - Volume 2021
  • - Article ID 8887666
  • - Research Article

Detection and Blocking of Replay, False Command, and False Access Injection Commands in SCADA Systems with Modbus Protocol

Rajesh L | Penke Satyanarayana
  • Special Issue
  • - Volume 2021
  • - Article ID 8876893
  • - Research Article

Distributed Outsourced Privacy-Preserving Gradient Descent Methods among Multiple Parties

Zuowen Tan | Haohan Zhang | ... | Rui Gao
  • Special Issue
  • - Volume 2021
  • - Article ID 8820082
  • - Research Article

Survey on Reversible Watermarking Techniques of Echocardiography

Rabiya Ghafoor | Danish Saleem | ... | M. Fahad Khan
  • Special Issue
  • - Volume 2021
  • - Article ID 8868355
  • - Review Article

Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

Elmustafa Sayed Ali | Mohammad Kamrul Hasan | ... | Savitri Bevinakoppa
  • Special Issue
  • - Volume 2021
  • - Article ID 6673992
  • - Research Article

Fusion of Machine Learning and Privacy Preserving for Secure Facial Expression Recognition

Asad Ullah | Jing Wang | ... | Zesong Fei
  • Special Issue
  • - Volume 2020
  • - Article ID 8897098
  • - Research Article

Protect Mobile Travelers Information in Sensitive Region Based on Fuzzy Logic in IoT Technology

Imran Memon | Riaz Ahmed Shaikh | ... | Khairul Akram Zainol
  • Special Issue
  • - Volume 2020
  • - Article ID 8869688
  • - Research Article

An Improved Method to Evaluate the Synchronization in Neural Key Exchange Protocol

Yi Liang Han | Yu Li | ... | Shuai Shuai Zhu
  • Special Issue
  • - Volume 2020
  • - Article ID 8883884
  • - Research Article

The Effect of the Primitive Irreducible Polynomial on the Quality of Cryptographic Properties of Block Ciphers

Sajjad Shaukat Jamal | Dawood Shah | ... | Tariq Shah
  • Special Issue
  • - Volume 2020
  • - Article ID 8867792
  • - Research Article

Towards an Improved Energy Efficient and End-to-End Secure Protocol for IoT Healthcare Applications

Arshad Ahmad | Ayaz Ullah | ... | Habib Ullah Khan
Security and Communication Networks
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Acceptance rate51%
Submission to final decision52 days
Acceptance to publication27 days
CiteScore3.300
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Impact Factor1.968
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.