Design and Development of Artificial Intelligence Knowledge Processing System for Optimizing Security of Software System

Software security vulnerabilities are significant for the software development industry. Exploration is conducted for software development industry landscape, software development eco-system landscape, and software system customer landscape. The focus is to explore the data sources that can provide...

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Bibliographic Details
Published in:SN computer science Vol. 4; no. 4; p. 331
Main Authors: Althar, Raghavendra Rao, Samanta, Debabrata, Purushotham, Sathvik, Sengar, Sandeep Singh, Hewage, Chaminda
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.07.2023
Springer Nature B.V
Subjects:
Artificial intelligence
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Customers
Cyber Security and Privacy in Communication Networks
Cybersecurity
Data science
Data sources
Data Structures and Information Theory
Deep learning
Industrial development
Information Systems and Communication Service
Knowledge management
Literature reviews
Machine learning
Modules
Original Research
Pattern Recognition and Graphics
Security
Software
Software development
Software Engineering/Programming and Operating Systems
Teams
Vision
Software vulnerability
Software development
Bi-directional encoder representation for transformers
Software security
Knowledge processing
ISSN:2661-8907, 2662-995X, 2661-8907
Online Access:Get full text
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Summary:Software security vulnerabilities are significant for the software development industry. Exploration is conducted for software development industry landscape, software development eco-system landscape, and software system customer landscape. The focus is to explore the data sources that can provide the software development team with insights to act upon the security vulnerabilities proactively. Across these modules of software landscape, customer landscape, and industry landscape, data sources are leveraged using artificial intelligence approaches to identify the security insights. The focus is also on building a smart knowledge management system that integrates the information processed across modules into a central system. This central intelligence system can be further leveraged to manage software development activities proactively. In this exploration, machine learning and deep learning approaches are devised to model the data and learn from across the modules. Architecture for all the modules and their integration is also proposed. Work helps to envision a smart system for Artificial Intelligence-based knowledge management for managing software security vulnerabilities.
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ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-023-01785-2