learning for professionals

Blekinge Institute of Technology

Blekinge Institute of Technology, BTH, has a distinctive focus on the digitalisation of society and sustainability.<br> BTH’s task is to contribute to a more sustainable societal development through higher education, research and innovation. BTH conducts education and research in fields in which society has major needs.<br> Through international excellence, we contribute to digital and sustainable transformation. As an institute of technology, we have a responsibility and a unique opportunity to make our contribution to both regional and national competitiveness and to global sustainability.<br> External engagement with wider society and the private sector contribute to making us more attractive and ensuring that our education and research maintain high quality and relevance.<br>


Advanced Digital Forensics

Companies and their IT systems are affected by advanced intrusions, various ransomware attacks and or thefts of both sensitive and secret information. In case of being compromised companies need to understand their weak points, ways of intrusion and attackers attributes.The course focuses on developing the student’s skills to investigate and analyze complex cyber attacks (Cyber Kill Chain) and to track the threat actor, discover exploited vulnerabilities so that companies can restore data and system integrity. Digital forensic is the process of detecting and investigating hacking attacks via properly extracted and analyzed evidence and artifacts to report the crime and prevent similar attacks in the future. The crime with computers and digital technologies in today’s cyber world is on the rise. Digital forensic techniques are being used by law enforcement agencies, police, government, and corporate entities around the world. The tools and techniques covered in the course will prepare the attendees to conduct digital forensic investigations using ground-breaking technologies.

Data-Driven Security

Organisations today produce a large amount of data. This course covers how to utilize that data for cybersecurity purposes. It covers topics such as how to acquire (e.g., through SIEM) and prepare security data, from collection and storage to management and analysis as well as visualization and presentation, predicting rouge behaviors, and correlate security events. How to use data science to understand and communicate security problems.

Machine Learning Security

The main objective of this course is to acquaint students with existing approaches, methods, and tools of machine Learning (ML) for security as well as unveil security issues in ML itself. This course is divided into the following two parts. First, it covers security problems in Machine Learning (ML) systems, e.g., showing various types of attacks on ML systems in an applied fashion – adversarial ML. Secondly, available methods, tools and other safeguards that could be used against the different types of attacks are covered. The course includes both theoretical introductions to the different attack types and security-enhancing methods and tools, as well as more practical hands-on assignments in Python. After the course the student will have obtained basic knowledge about security-enhancing approaches, and how to use them in order to protect against various risks in ML systems and how to use ML to detect cyber attacks.

Malware Analysis

The course aims to provide students with the skills of real-world threats analysis including phishing attacks, targeted attacks (APTs), cyber weapon, ransomware (cryptolockers) The analysis of such threats requires a special type of education focused on the analysis of modern threats and protection technologies. The course gives knowledge and practical skills in malware analysis for Windows and Android platforms (x86 and ARM). The students will obtain practical skills in reverse engineering, static and dynamic analysis of malware used in the real-life cyber attacks.

Quality Assurance of Security Aware Applications

The purpose of this course is to show how fundamental testing practices are applied in the context of secure software development. The student will learn to integrate automated software testing with different approaches to verify software security, leveraging theories from continuous quality assurance in software development, as well as security best practices.The course is adapted to give a solid introduction to non-testing experts with an interest in software security, and addresses both how professionals (developers, managers, decision-makers) can incorporate security into the quality assurance process of their products/service.