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AI for Cybersecurity

Darius Bufnea, Laura Dioșan, and Adrian Sterca

Students will gain a solid understanding of cybersecurity fundamentals, including common attack vectors and vulnerabilities, as well as an introduction to the defense mechanisms used to protect systems. In the first parts, the course presents the core AI techniques applicable to cybersecurity from a theoretical point of view, with a focus on machine learning (ML) methods such as supervised, unsupervised and reinforcement learning.

In subsequent parts, students will learn how to apply ML techniques practically to specific cybersecurity challenges like malware detection and classification, intrusion detection, and email spam filtering. They will explore the process of implementing ML models for these tasks, training and evaluating them on data using the Python programming language.

Overall, this course equips students with both the theoretical understanding and practical skills needed to apply AI methods in order to protect systems against evolving cyber threats.

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What's inside

Syllabus

Introduction to AI and Cybersecurity

Welcome to the introductory part of the AI and Cybersecurity course! During the 5 video lectures and 2 readings of this module you will find various definitions of the Artificial Intelligence, the evolution of this domain and the classification of the AI algorithms in search-based algorithms and intelligent systems. The domains where AI is successfully used are presented, with focus on the use of AI in cybersecurity related tasks (e.g.: network analysis, intrusion detection, malicious web link detection, anomaly detection or malware classification ). Afterwards, the basic concepts of cybersecurity will be introduced, and the classification of security threats at endpoint level or internet level. You will discover types of cybersecurity threats and how they can be defended.

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Career center

Learners who complete AI for Cybersecurity will develop knowledge and skills that may be useful to these careers:

Security Data Scientist

A Security Data Scientist leverages data science and machine learning to develop advanced solutions for cybersecurity challenges. This AI for Cybersecurity course is exceptionally well-suited for this role, as it focuses on applying machine learning techniques practically to specific cybersecurity challenges such as malware detection, intrusion detection, and email spam filtering. Learners will explore implementing, training, and evaluating ML models on data using Python, providing the exact theoretical understanding and practical skills needed for a Security Data Scientist.

See salaries and explore the career path for Security Data Scientist

Machine Learning Engineer Cybersecurity

A Machine Learning Engineer Cybersecurity designs, develops, and deploys machine learning models specifically tailored for security applications. This AI for Cybersecurity course provides the core competencies required, covering fundamental ML techniques—supervised, unsupervised, and reinforcement learning—and their practical application. Learners gain hands-on experience implementing, training, and evaluating ML models in Python for critical cybersecurity tasks like malware detection and intrusion detection, essential for success as a Machine Learning Engineer Cybersecurity.

See salaries and explore the career path for Machine Learning Engineer Cybersecurity

Intrusion Detection Specialist

An Intrusion Detection Specialist monitors systems and networks for unauthorized access and malicious activity, responding to identified threats. This AI for Cybersecurity course is exceptionally well-suited as it focuses explicitly on the application of machine learning techniques for intrusion detection. Learners gain practical skills in implementing and evaluating ML models to identify suspicious patterns, which is a fundamental responsibility for an Intrusion Detection Specialist ensuring the continuous security of digital environments.

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Cybersecurity Analyst

A Cybersecurity Analyst identifies, assesses, and mitigates cyber threats to an organization's systems and data. This comprehensive AI for Cybersecurity course directly prepares learners by providing a solid understanding of cybersecurity fundamentals, including common attack vectors and defense mechanisms. Learners gain practical skills in applying machine learning methods for tasks like intrusion detection and malware classification, which are core responsibilities of a Cybersecurity Analyst. The course's focus on using AI to protect systems against evolving cyber threats is highly relevant for effective threat analysis and response.

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Malware Analyst

A Malware Analyst investigates malicious software, understanding its behavior, origins, and impact, and develops countermeasures. This AI for Cybersecurity course directly addresses a core aspect of this role by teaching learners how to apply machine learning techniques for malware detection and classification. The course provides practical skills in implementing, training, and evaluating ML models for these tasks using Python, equipping future Malware Analyst professionals with cutting-edge tools to combat evolving threats.

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Threat Intelligence Analyst

A Threat Intelligence Analyst gathers and analyzes information on cyber threats to predict and prevent attacks. This course is highly relevant, introducing learners to AI applications in network analysis, intrusion detection, anomaly detection, and malware classification, all crucial for understanding actor tactics. The material on malicious web link detection and Domain Generation Algorithms further strengthens the ability to identify emerging threats. This foundational knowledge helps a Threat Intelligence Analyst in proactively informing defensive strategies.

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Incident Response Analyst

An Incident Response Analyst is at the forefront of detecting, analyzing, and responding to cybersecurity breaches and incidents. This AI for Cybersecurity course directly enhances these capabilities by providing practical skills in applying machine learning techniques for intrusion detection and anomaly detection. These abilities are crucial for rapidly identifying indicators of compromise, speeding up investigation, and enabling an Incident Response Analyst to effectively mitigate damage from evolving cyber threats.

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Network Security Engineer

A Network Security Engineer designs, implements, and manages security solutions to protect an organization's network infrastructure. This AI for Cybersecurity course is highly relevant, as it explicitly discusses the distinction between host and network security and introduces AI applications in network analysis and intrusion detection. Learners gain both theoretical understanding and practical skills in applying AI methods to safeguard networks against persistent and evolving cyber threats, which is a key responsibility for a Network Security Engineer.

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Security Engineer

A Security Engineer designs, builds, and maintains robust security systems and infrastructure. This course provides a strong foundation by introducing cybersecurity fundamentals, including common attack vectors and defense mechanisms. Crucially, it equips learners with both the theoretical understanding and practical skills to apply AI methods, most specifically machine learning, to protect systems against evolving cyber threats, which is increasingly vital for a modern Security Engineer.

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AI Ethics and Governance Specialist

An AI Ethics and Governance Specialist ensures that artificial intelligence systems are developed and used responsibly, adhering to ethical standards and legal frameworks. The AI for Cybersecurity course's final module directly addresses these critical aspects, exploring ethical challenges, legal frameworks like the EU AI Act, and introducing the AI4People framework. This specialized knowledge is foundational for an AI Ethics and Governance Specialist navigating the complex intersection of AI, security, and societal impact. This role typically requires an advanced degree.

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Security Consultant

A Security Consultant advises organizations on strategies to enhance their cybersecurity posture and mitigate risks. This AI for Cybersecurity course, by equipping learners with a solid understanding of cybersecurity fundamentals and practical skills in applying AI methods, most specifically machine learning, to protect systems, may be useful. Consultants can leverage this knowledge to recommend advanced AI-driven security solutions and articulate how to defend against evolving cyber threats, supporting a Security Consultant's client engagements.

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Cloud Security Engineer

A Cloud Security Engineer protects data, applications, and infrastructure within cloud environments. While this course does not specifically mention cloud security, its comprehensive introduction to applying AI methods, such as machine learning for anomaly and intrusion detection, may be useful for securing cloud resources. The ability to understand attack vectors and implement AI-driven defense mechanisms can be transferred to the unique challenges faced by a Cloud Security Engineer.

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Application Security Engineer

An Application Security Engineer focuses on protecting software applications from design to deployment. While not exclusively centered on application code, the AI for Cybersecurity course’s focus on detecting malicious web links, understanding attack vectors, and applying AI for anomaly detection may be useful for identifying vulnerabilities within applications or securing their interactions. The skills in leveraging machine learning to identify threats can aid in building more resilient and secure software for an Application Security Engineer.

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Digital Forensic Investigator

A Digital Forensic Investigator collects, analyzes, and preserves digital evidence following cyber incidents. The AI for Cybersecurity course, with its focus on machine learning for anomaly detection and understanding various cyber threats, may be useful for efficiently processing large datasets and identifying suspicious activities. This analytical foundation can help a Digital Forensic Investigator piece together attack timelines and identify compromised systems more effectively. This role typically requires an advanced degree.

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DevSecOps Engineer

A DevSecOps Engineer integrates security practices into every phase of the software development and operations lifecycle, promoting automated and continuous security. The AI for Cybersecurity course’s focus on applying AI techniques for detecting and mitigating online threats, such as malicious web links or potentially vulnerable code patterns, may be useful. This helps a DevSecOps Engineer build tools and processes that proactively identify and address security concerns early in the pipeline, enhancing overall system resilience.

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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 AI for Cybersecurity.

Speech and Language Processing. An Introduction to...

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This widely recognized and comprehensive textbook that provides a deep dive into the subject of language processing. It is suitable for both undergraduate and graduate-level courses and is considered a must-read for gaining a strong theoretical and applied understanding of NLP. The authors are leading researchers in the field, and the book is frequently updated to reflect advancements. The latest draft is available online.

AI for Cybersecurity

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What's inside

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