Security Assessment Model for Software Systems

The rapid growth of software systems and internet-based applications has increased the risk of cyber threats such as phishing attacks, unauthorized access, and malicious software execution. Traditional security mechanisms are often insufficient to detect dynamic threats in real time. This paper presents a *Security Assessment Model for Software System*, which integrates system monitoring, phishing detection, executable file analysis, and authentication log analysis into a unified web-based platform. The proposed system continuously monitors system processes, network activity, and resource usage while allowing users to analyze website URLs and uploaded executable files for potential threats. Authentication log analysis is used to detect suspicious login activities such as repeated failed attempts or unauthorized access. The system is developed using Python Flask for backend processing, SQLite for data storage, and machine learning–based decision logic for phishing detection. Experimental evaluation demonstrates that the system effectively identifies suspicious behavior and provides timely security alerts. The proposed model can assist system administrators in enhancing software system security by proactively detecting and mitigating threats.