Big Data Analytics with Machine Learning for Cyber Security

Dear Colleagues,

This Special Issue focuses on big data analytics, the critical role of machine learning (ML) in it, and the possible security challenges in big data. In this data-driven era, organisations generate an unprecedented volume and variety of data from various sources such as hospitals, business transactions, social media interactions, IoT devices, sensors, and communication devices. Big data analytics refers to the process of extracting valuable insights and hidden patterns from large and complex datasets. When combined with machine learning (ML)/deep learning (DL), big data analytics becomes a meaningful and powerful tool for uncovering hidden patterns, predicting outcomes, and making data-driven decisions. The growing volume and variety of data generated from different sources pose significant challenges for traditional security apparatus. The successful combination of big data analytics with ML techniques offers a convincing solution to effectively detect, prevent, and respond to cyber threats in this complex environment or landscape. ML/DL algorithms can be trained to recognise normal behaviour and identify deviations that could signify suspicious activities or security breaches.

Big data analytics in cybersecurity involves processing and analysing massive datasets collected from various sources over time such as network traffic logs, system logs, application logs, sensor data, and security events. Similarly, we need to secure healthcare-related patient data in the internet of medical things (IoMT) against unauthorised access. The objective is to extract actionable insights and identify patterns that may indicate potential security issues or flaws, anomalies, malicious activities, or any other security-related concerns. Behavioural analytics is another aspect where ML/DL models come in handy. By analysing user behaviour, ML/DL algorithms can create profiles of normal activities and detect deviations that may indicate insider threats or compromised accounts. In this upcoming Special Issue, we invite submissions of original research or review articles on the topics and related areas listed below. We look forward to receiving your contributions as we aim to explore different research areas within (but not limited to) the following topics:

1. Different security approaches of big data analytics;
2. Privacy and security of big data using ML/DL/reinforcement learning/deep reinforcement learning;
3. IoT and IoMT security;
4. Security information and event management: tools, architecture, and methods;
5. Cloud security analytics;
6. Privacy-preserving data analysis;
7. Predictive security analytics;
8. Self-sovereign identity;
9. Zero-day attacks and prevention methods;
10. Open-source intelligence in cybersecurity applications;
11. Cyberthreat intelligence and malware analysis;
12. Big data security paradigms/architectures;
13. Existing big data policy and protocols.

Dr. Babu Baniya
Dr. Sherif Abdelfattah
Dr. Deepak GC
Guest Editors

Manuscript Submission Information

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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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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.

• big data analytics
• cybersecurity
• machine learning
• deep learning
• IoT