This course provides aspects of structural bioinformatics and deals with computational applications in drug discovery. It starts with a review of protein modeling and then quickly moves into computational techniques with a special emphasis on drug discovery context. Example topics include protein homology modeling, ligand-protein molecular docking, and other prediction methods. The course meetings

This course aims to teach student how to conduct a digital forensics analysis of the file system, e.g. NTFS and FAT, volatile memory, and network traffic, using a sound digital investigation process. It begins by a basic background of interoperating raw-data and timestamps. Then NTFS and FAT file system layout will be discussed in details. The basic of memory acquisition, analysis and evidence

The course aims to teach the students how to detect, analyzing and track a malicious program. It begins, by introducing the basics of reverse engineering concepts and using tools like IDA pro to analyze x86 malicious code. The concept of static and dynamic malware analysis will be introduced gradually through the course. After, the techniques commonly employed by malware to thwart the analysis

Software is commonly vulnerable to flaws and bugs that affect the program logic, intention, and executions. These vulnerabilities are further allowing an attacker to execute arbitrary malicious code on a target system. This class will cover both the identification of software vulnerabilities and the most common techniques used to exploit them. In addition, current existing techniques that attempt

This course exposes the mobile hacking techniques and countermeasures for iOS and Android. Student will practice how to analyze and evaluate mobile application threats as well as exploring how the attackers identify weaknesses. This course is designed to equip the student with the required knowledge and skills in securing mobile devices, mobile applications and mobile networks of their

The main aim of the course is to analyze and visualize biological data using the R language. The course starts with how to use the R language to retrieve, process, and store data in different formats. Then the course goes with how to calculate data basic descriptive statistics, visualize data in different graphic types, assume initial conclusions that can help with hypotheses generation, and use