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Cyber Security

Application of AI

Matt Bushby

In today’s high-stakes cyber landscape, artificial intelligence (AI) and machine learning (ML) are no longer futuristic add-ons—they are essential pillars of a modern cyber defence strategy. This course is your hands-on, practitioner-focused guide to understanding how AI and ML are being used to detect, disrupt, and defend against cyber threats in real time.

Smarter Threat Detection. Stronger Defences. Real-World Readiness.

Built by Macquarie University’s Cyber Skills Academy—ranked in the top 1% of universities globally and recognised as Australia’s leading cyber security school—this course has been co-designed with industry to ensure practical, real-world impact. It brings together technical depth and tactical awareness, with a focus on applications that are relevant, actionable, and urgently needed by today’s organisations. Key topics include:

• Build foundational knowledge of AI and ML concepts, tasks (classification/regression), accuracy trade-offs, and the unique risks they face in cyber contexts.

• Apply ML tools and models to real-world security problems, including malware analysis, fraud detection, deep packet inspection, and network monitoring.

• Analyse network traffic using anomaly detection techniques powered by supervised and unsupervised ML methods, such as k-nearest neighbours and one-class SVM.

• Unpack malware behaviour and experiment with ML-driven analysis to identify malicious binaries, understand malware types, and apply artificial neural networks to detection tasks.

Dive deep into adversarial machine learning, learning how attackers manipulate models with poisoning and evasion attacks—and how to defend against them by building more robust, resilient systems.

Important Note: While no prior AI/ML experience is required, some basic familiarity with Python programming is recommended to get the most out of the practical activities and hands-on labs.

Building Models That Fight Back

This course is designed for cyber security professionals, SOC analysts, engineers, data scientists, and tech leaders looking to future-proof their security strategies with intelligent automation and machine-driven defence techniques.

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What's inside

Syllabus

Artificial Intelligence (AI) and Machine Learning (ML) Concepts

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the way we defend against cyber threats, offering the power to detect patterns, respond to anomalies, and adapt to evolving risks at machine speed. But with great capability comes complexity and new vulnerabilities. In this module, you’ll build a strong foundation in AI and ML, tailored specifically for cyber security applications. You’ll explore the core concepts behind machine learning, how models are trained, what types of learning exist, and how we measure their accuracy and effectiveness. But you’ll also look under the hood at the darker side of AI: the ways attackers can exploit ML systems through inference, poisoning, and adversarial input. By the end of this module, you'll not only understand how ML can support cyber defence, but also the new attack surfaces it introduces and how to critically evaluate its strengths, weaknesses, and limitations in the real world.

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Career center

Learners who complete Cyber Security: Application of AI will develop knowledge and skills that may be useful to these careers:

Machine Learning Security Engineer

A Machine Learning Security Engineer designs, implements, and maintains robust security systems that leverage artificial intelligence and machine learning, a critical role in integrating AI-driven defenses into an organization's infrastructure. This course is designed specifically to help learners excel as a Machine Learning Security Engineer by providing a deep dive into how AI and ML are used to detect, disrupt, and defend against cyber threats in real time. The curriculum, which includes applying ML tools and models to issues like malware analysis and network monitoring, directly aligns with the practical demands of this role. Furthermore, the focus on adversarial machine learning and developing countermeasures against poisoning and evasion attacks ensures that professionals can build resilient, intelligent automation and machine-driven defense techniques, making this course highly pertinent for achieving success in this field.

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Cybersecurity Data Scientist

This role involves applying statistical analysis and machine learning to understand and combat cyber threats, with a Cybersecurity Data Scientist leveraging data to identify patterns, predict attacks, and build automated defense mechanisms. This course offers a hands-on, practitioner-focused guide, directly equipping learners with the knowledge to build foundational AI and ML concepts, understand accuracy trade-offs, and apply ML models to real-world security problems such as malware analysis and fraud detection. The detailed modules on machine learning for network traffic analysis and anomaly detection, using techniques like k-nearest neighbours and one-class SVM, are particularly relevant. It also covers adversarial machine learning, which helps build more robust systems. For those aspiring to be a Cybersecurity Data Scientist, this course provides practical experience in running ML models on cyber datasets, unlocking new levels of insight crucial for developing intelligent, data-driven security strategies. An advanced degree is often typical for this profession.

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Security Researcher

A Security Researcher explores new vulnerabilities, develops novel defense techniques, and stays ahead of emerging cyber threats. For a Security Researcher, this course provides a robust foundation and practical insights into the cutting edge of cyber defense by focusing on artificial intelligence and machine learning applications. The curriculum, which includes building foundational knowledge of AI and ML concepts, applying ML models to complex problems, and diving deep into adversarial machine learning, is particularly valuable. Learners will understand how attackers manipulate models with poisoning and evasion attacks and, crucially, how to defend against them by building more robust, resilient systems. This knowledge is vital for developing innovative security solutions and publishing findings. While typically requiring an advanced degree, this course helps build practical, current expertise in a highly specialized and rapidly evolving field.

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Malware Analyst

A Malware Analyst specializes in examining malicious software to understand its functionality, origin, and potential impact. This course is exceptionally relevant for an aspiring Malware Analyst, as it includes dedicated topics on malware analysis and identification. Learners will dive deep into unpacking malware behavior and experimenting with machine learning-driven analysis to identify malicious binaries, understand malware types, and apply artificial neural networks to detection tasks. The hands-on approach to working with malware datasets and applying algorithms to classify malicious behavior directly prepares individuals for the technical demands of this role. Furthermore, understanding adversarial machine learning helps in anticipating how malware might adapt to evade AI-driven detection systems. This course can help learners develop intelligent defense mechanisms that continuously learn from evolving threats, crucial for success as a Malware Analyst.

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Network Security Engineer

A Network Security Engineer designs, implements, and maintains secure network infrastructures, protecting data in transit and at rest. This course is highly relevant for a Network Security Engineer, offering practical expertise in using artificial intelligence and machine learning to build advanced network defense mechanisms. Learners will gain proficiency in applying ML tools and models to critical areas such as deep packet inspection and network monitoring. A significant portion of the course focuses on machine learning for network traffic analysis and anomaly detection, where individuals learn to identify unusual patterns and detect threats hiding in plain sight using supervised and unsupervised ML methods like k-nearest neighbours and one-class SVM. This enables engineers to create intelligent defense mechanisms that continuously learn from evolving threats, pushing network resilience far beyond static rule-based systems.

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Security Operations Center Analyst

A Security Operations Center Analyst monitors, detects, analyzes, and responds to cyber threats and incidents. This course directly enhances the capabilities of a Security Operations Center Analyst by teaching how to integrate artificial intelligence and machine learning into modern cyber defense strategies, providing practical insights into smarter threat detection and stronger defenses. Learners will gain hands-on experience applying ML models to real-world security problems, including deep packet inspection and network monitoring, which are core tasks in a SOC. The modules on machine learning for network anomaly detection, using methods like k-nearest neighbours, will significantly improve an analyst's ability to spot subtle signals of compromise. This course empowers analysts to augment human judgment with intelligent automation, enabling faster and more accurate threat identification and response.

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Security Architect

A Security Architect designs and plans an organization's security infrastructure, ensuring robust protection against cyber threats. For a Security Architect, this course is highly relevant for designing future-proof security strategies that leverage intelligent automation and machine-driven defense techniques. The curriculum provides a strong understanding of how AI and machine learning are being used to detect, disrupt, and defend against cyber threats in real time. Architects will learn about the unique risks AI/ML systems face in cyber contexts, including adversarial attacks, enabling them to design more resilient systems. By understanding the application of ML in malware analysis, network monitoring, and anomaly detection, a Security Architect can effectively integrate these advanced capabilities into the overall security framework, ensuring built-in defenses that continuously adapt to evolving risks.

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Threat Intelligence Analyst

A Threat Intelligence Analyst collects, processes, and analyzes information about potential and actual cyber threats to provide actionable intelligence. For a Threat Intelligence Analyst, understanding how AI and machine learning are used to detect, disrupt, and defend against cyber threats is paramount for anticipating future attacks. This course provides a practitioner-focused guide to leveraging AI for smarter threat detection and stronger defenses. The curriculum's focus on analyzing network traffic using anomaly detection techniques, coupled with unpacking malware behavior through ML-driven analysis to identify malicious binaries, directly applies to gathering crucial intelligence. Learning about adversarial machine learning, including how attackers manipulate models, helps analysts anticipate new attack vectors, allowing them to build comprehensive threat profiles. This course can help prepare individuals to provide more proactive and predictive threat intelligence.

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Fraud Detection Analyst

A Fraud Detection Analyst identifies, investigates, and prevents fraudulent activities within an organization using data analysis and investigative techniques. For a Fraud Detection Analyst, integrating artificial intelligence and machine learning is increasingly essential for identifying sophisticated patterns of fraudulent behavior. This course provides direct, hands-on experience in applying ML tools and models to real-world security problems, explicitly including fraud detection. Learners will explore core AI and ML concepts, understand accuracy trade-offs, and gain practical skills in building and testing classification and regression models relevant to financial transactions and user behavior. The ability to run ML models on cyber datasets helps analysts unlock new levels of insight, enabling them to automate the detection of anomalies and augment human judgment in high-stakes environments, thereby strengthening an organization's defenses against financial crimes.

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Incident Response Specialist

An Incident Response Specialist is responsible for responding to cyber incidents, containing their impact, and leading recovery efforts. This course can significantly enhance the capabilities of an Incident Response Specialist by integrating artificial intelligence and machine learning into their toolkit for faster and more effective responses. The curriculum's emphasis on smarter threat detection and stronger defenses, particularly through applying ML models to real-world security problems like network monitoring and anomaly detection, enables quicker identification of compromises. Understanding how to build models that fight back against adversarial attacks allows specialists to design more resilient systems and accelerate response times. By learning to run ML models on cyber datasets, an Incident Response Specialist can augment human judgment, ensuring a more proactive and data-driven approach to mitigating the impact of cyber incidents.

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Artificial Intelligence Engineer

An Artificial Intelligence Engineer develops, deploys, and maintains AI models and systems. While this role can span many domains, specializing as an Artificial Intelligence Engineer with a focus on cyber security is a growing field. This course provides the unique opportunity to build foundational AI and ML concepts explicitly tailored for cyber security applications. Learners will gain hands-on experience applying ML tools and models to real-world security problems, including malware analysis, fraud detection, and network anomaly detection. By exploring the darker side of AI, such as adversarial manipulation, the course equips engineers to build more robust and resilient AI systems within a security context. This specialization can help learners develop intelligent automation and machine-driven defense techniques, making them highly sought after at the intersection of AI and cyber defense.

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Cybersecurity Consultant

A Cybersecurity Consultant advises organizations on security strategies, risk management, and implementing security solutions. This course can be highly beneficial for a Cybersecurity Consultant by providing cutting-edge knowledge in artificial intelligence and machine learning applications for cyber defense. Consultants can leverage this expertise to guide clients on adopting smarter threat detection methods, strengthening their defenses, and future-proofing security strategies with intelligent automation. Understanding key topics such as applying ML to malware analysis, network monitoring, and fraud detection enables the consultant to recommend actionable solutions. Furthermore, insights into adversarial machine learning help consultants advise on building more resilient systems and effectively managing the unique risks AI introduces. This knowledge helps one effectively translate advanced technical concepts into strategic advice for diverse organizational needs.

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Application Security Engineer

An Application Security Engineer focuses on making applications secure throughout their lifecycle, from design to deployment. While this role often involves code review and vulnerability testing, artificial intelligence and machine learning are increasingly used to enhance application security. This course can be helpful for an Application Security Engineer by introducing core AI and ML concepts and their application to real-world security problems. Understanding anomaly detection techniques, for instance, can be applied to user behavior analytics within applications to spot unusual login patterns or malicious actions. The knowledge of adversarial machine learning and building robust, resilient systems can also inform the design of application-level defenses that are less susceptible to manipulation. This course can help bridge the gap between traditional application security practices and emerging AI-driven defense strategies, enhancing one's ability to build and secure modern software.

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Security Product Manager

A Security Product Manager defines the strategy, roadmap, and feature set for security products, working closely with engineering, sales, and marketing teams. For a Security Product Manager, a deep understanding of artificial intelligence and machine learning in cyber security is essential for guiding the development of competitive and effective security solutions. This course provides a hands-on, practitioner-focused guide to how AI and ML are used to detect, disrupt, and defend against cyber threats in real time. Understanding smarter threat detection, stronger defenses, and real-world readiness, including topics like malware analysis, network anomaly detection, and adversarial machine learning, helps in identifying market needs and product opportunities. This course can help in making informed decisions about integrating intelligent automation into security offerings, ensuring that products are robust, resilient, and address critical industry demands for machine-driven defense techniques.

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Data Engineer

A Data Engineer designs and builds systems for collecting, storing, and processing data, making it accessible for analysis and machine learning applications. While not solely focused on security, a Data Engineer working in a cybersecurity context may find this course helpful. The course introduces how machine learning models are trained and how to load, view, and preprocess datasets for cyber security applications. Understanding the specific data requirements for ML in areas such as malware analysis, network traffic analysis, and anomaly detection is crucial for building efficient data pipelines. This course provides context for the types of cyber datasets that need to be managed and prepared, along with an understanding of accuracy trade-offs and the unique risks ML systems face. This knowledge may help a Data Engineer ensure that robust, clean, and relevant data is available for building effective AI-driven cyber defenses.

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Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Cyber Security: Application of AI.

Artificial Intelligence

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The definitive benchmark textbook of modern artificial intelligence, rigorously unifying search algorithms, probabilistic reasoning, knowledge representation, and machine learning into an elegant, coherent agent-based framework.

Artificial Intelligence: A Modern Approach

Hardcover

$$$$

Fraud Analytics Using Descriptive, Predictive, and...

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Establishes the quantitative blueprint for modern fraud detection systems, combining supervised classification under extreme class imbalance, unsupervised anomaly scoring, Benford's Law, and bipartite social network guilt-by-association propagation across insurance, tax, and credit card datasets.

Cyber Security

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