Machine Learning for Cyber Security

Artificial intelligence (AI) and machine learning (ML) have witnessed great forward leaps in recent years. AI and ML have achieved near-human level performance in areas for which this had previously seemed impossible. However, the wide adoption of these technologies in applications of our daily life has opened the door to new kinds of cybersecurity threats. This Special Issue seeks to report the recent advances and uses of AI and ML in the realm of cybersecurity. Both AI and ML can be seen as a double-edged sword. For instance, they can be used to bolster the security infrastructure by better analyzing threats, detecting anomalies, and responding to security incidents. In addition, they can also facilitate new forms of cybersecurity threats such as backdoor attacks, deepfakes, adversarial attacks, etc. Moreover, the current research trend is to build privacy-aware machine learning models that can train and/or make decisions on private data. This includes novel techniques such as federated learning, secure multi-party computation, homomorphic encryption, and differential privacy.

This Special Issue is dedicated to the presentation of novel approaches and results on the aforementioned topics. The intention is to span many related fields and, yet, deal with each in such a way that the commonalities are apparent. We invite you to submit significant updates to previously published papers or completely new manuscripts which will be subject to double-blind peer review.

Dr. Mahmoud Nabil Mahmoud
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Secure artificial intelligence
• Private machine learning
• Adversarial machine learning
• Deep fakes
• Anomaly detection
• Federated learning
• Differential privacy
• Malware detection