Date of Publication


Document Type

Master's Thesis

Degree Name

Master of Science in Electronics and Communications Engineering

Subject Categories

Electrical and Computer Engineering


Gokongwei College of Engineering


Electronics And Communications Engg


Best Paper Award for an outstanding paper titled: Detection of Malicious Binaries and Executables Using Machine Learning-based Detectors, 2nd International Conference on Information Security and Computer Technology, Online Via Cisco Webex, 2021.

Thesis Advisor

Lawrence Y. Materum

Defense Panel Chair

Melvin Cabatuan

Defense Panel Member

Marnel Peradilla
Gregory Cu


In digital networks, the most common goal of cybercriminals is to steal high-privilege credentials or valuable data. By obtaining high-privilege credentials, cybercriminals can easily navigate, destroy, or steal an organization's data, such as bank details, personal data, and intellectual properties. With the advent of information technology and operational technology convergence like the Internet of things (IoT), it becomes more critical on protecting the high-privilege credentials as cybercriminals can have the power to control operational technologies such as industrial control systems (ICS) and supervisory control and data acquisition (SCADA). Unfortunately, even with this information, many organizations are easily susceptible to these attacks, especially manufacturing firms. This thesis presents how cybercriminals from the Internet can utilize malicious payloads and executables to compromise an organization. This thesis’ attack methods emphasize how cybercriminals perform initial compromise, establish a foothold, escalate privileges, and move laterally within the organizations using the compromised or stolen credentials. This thesis also shows how organizations can detect the malicious binaries and executables utilized in the attacks to protect their digital infrastructure from adversaries using (ML) machine learning-based detection. Doing so could help organizations be equipped with proper knowledge in understanding the underlying attack and, at the same time, implement their detection mechanism specific to the cybercriminals attacking their network.

Abstract Format






Physical Description



Malware (Computer software); Computer crimes; Internet fraud; Machine learning; Computers—Access control—Passwords

Upload Full Text


Embargo Period


Available for download on Monday, September 20, 2027