Security Pattern Detection In Software Code Using Machine Learning Algorithms

SECURITY PATTERN DETECTION IN SOFTWARE CODE USING MACHINE LEARNING ALGORITHMS.

Security patterns, defined as reusable building blocks of secure software code architecture, provide solutions to recurring security flaws and problems in specific contexts. Implementing non-standard or incomplete security patterns may create vulnerabilities that cybercriminals can exploit to execute various attacks on a computer system. Security patterns must be accurately identified and used to enhance software code quality and security features. This study examines the possibility of using machine learning algorithms to detect security patterns in software code. The proposed framework for our research is the Security Pattern Detection (SPD) and its internal pattern matching technique, Non-uniform Distributed Matrix Matching (NDMM). The machine learning algorithms selected for our study are Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The primary data for the study were collected by interviewing experts who agreed to participate in the study. The purposive sampling method was used to select experts in machine learning algorithms and security pattern detection in software code. The experts' responses were analyzed and, in conjunction with findings from recent studies on CNN and LSTM, used to develop a comprehensive discussion of the prospect of using machine learning algorithms to detect security patterns in software code.

Proceedings of the ... ACM Workshop on Visualization and Data Mining for Computer Security

Building Open Source Network Security Tools

Learn how to protect your network with this guide to building complete and fully functional network security tools Although open source network security tools come in all shapes and sizes, a company will eventually discover that these tools are lacking in some area—whether it's additional functionality, a specific feature, or a narrower scope. Written by security expert Mike Schiffman, this comprehensive book will show you how to build your own network security tools that meet the needs of your company. To accomplish this, you'll first learn about the Network Security Tool Paradigm in addition to currently available components including libpcap, libnet, libnids, libsf, libdnet, and OpenSSL. Schiffman offers a detailed discussion of these components, helping you gain a better understanding of the native datatypes and exported functions. Next, you'll find several key techniques that are built from the components as well as easy-to-parse programming examples. The book then ties the model, code, and concepts together, explaining how you can use this information to craft intricate and robust security programs. Schiffman provides you with cost-effective, time-saving guidance on how to build customized network security tools using existing components. He explores: A multilayered model for describing network security tools The ins and outs of several specific security-related components How to combine these components into several useful network security techniques Four different classifications for network security tools: passive reconnaissance, active reconnaissance, attack and penetration, and defensive How to combine techniques to build customized network security tools The companion Web site contains all of the code from the book.