Security Assessment of Software Design using Neural Network

Adebiyi, Adetunji B.; Arreymbi, Johnnes; Imafidon, Chris

doi:10.14569/ijarai.2012.010401

Cited by 7 publications

(7 citation statements)

References 12 publications

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“…Neural network error and security gap tracking works by slicing software code into formal routines prone to typical attack patterns in a systematic way and exploring a broad set of viral strategies to each element. Neural security assessment networks reach an accuracy of more than 90% in an empirical test on a network architecture [85].…”

Section: Ai In Software Maintenancementioning

confidence: 99%

Applications of AI in classical software engineering

Barenkamp¹,

Rebstadt

Thomas

2020

AI Perspect

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Although Artificial Intelligence (AI) has become a buzzword for self-organizing IT applications, its relevance to software engineering has hardly been analyzed systematically. This study combines a systematic review of previous research in the field and five qualitative interviews with software developers who use or want to use AI tools in their daily work routines, to assess the status of development, future development potentials and equally the risks of AI application to software engineering. The study classifies the insights in the software development life cycle. The analysis results that major achievements and future potentials of AI are a) the automation of lengthy routine jobs in software development and testing using algorithms, e.g. for debugging and documentation, b) the structured analysis of big data pools to discover patterns and novel information clusters and c) the systematic evaluation of these data in neural networks. AI thus contributes to speed up development processes, realize development cost reductions and efficiency gains. AI to date depends on man-made structures and is mainly reproductive, but the automation of software engineering routines entails a major advantage: Human developers multiply their creative potential when using AI tools effectively.

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Section: Ai In Software Maintenancementioning

confidence: 99%

Applications of AI in classical software engineering

Barenkamp¹,

Rebstadt

Thomas

2020

AI Perspect

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“…Some studies [20], [43] have also established relationship between software vulnerabilities and code smells. Deep learning has proven to be an intrinsic part of artificial intelligence and has been acknowledged by many researchers [1], [19], [30] for better predictive analysis. Considering enhancement in software security and quality as a primary objective, many research studies [14], [30], [42] have employed Neural Network-based models for security assessment of software designs [1].…”

Section: Related Workmentioning

confidence: 99%

ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications

Gupta

Sharma

Phulli³

2022

Foundations of Computing and Decision Sciences

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There has been a lot of software design concerns in recent years that come under the code smell. Android Applications Developments experiences more security issues related to code smells that lead to vulnerabilities in software. This research focuses on the vulnerability detection in Android applications which consists of code smells. A multi-layer perceptron-based ANN model is generated for detection of software vulnerabilities and has a precision value of 74.7% and 79.6% accuracy with 2 hidden layers. The focus is laid on 1390 Android classes and involves association mining of the software vulnerabilities with android code smells using APRIORI algorithm. The generated ANN model The findings represent that Member Ignoring Method (MIM) code smell shows an association with Bean Member Serialization (BMS) vulnerability having 86% confidence level and 0.48 support value. An algorithm has also been proposed that would help developers in detecting software vulnerability in the smelly source code of an android applications at early stages of development.

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“…To help the amalgamation of security ahead of plan in the SDLC phases, another paper [25] examined approach for assessing security in the midst of the planned phase by neural method. Their disclosures show that by means of setting up a back propagation neural method to perceive attack structures, plausible harms can be recognized from framework displayed to it.…”

Section: Comparative Study Of Defense Approaches In Current Scenariomentioning

confidence: 99%

“…Access Controls, to store and share progressively digital at home, just change after some issue emerges yet does not change consequently by consistent learning. Application Security [25] Neural System approach is discussed in SDLC.…”

Section: Reactivementioning

confidence: 99%

Challenges of Information Security in the Contemporary Cyber Threat Perception

2019

IJITEE

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Information security comprises advancements, methods and practices proposed to guarantee protection of system hubs, programs, information, data and system from hack assaults, modifications to data or unintended access. To repress cybercriminals from gaining access to system resources specifically sensitive data, it is fundamental for organizations and individuals to take necessary actions to preserve the confidentiality, integrity and availability of delicate data. Information security spreads complex strategies to safeguard not only data during transmission, but also data stored in the system even when the system is not connected to the internet. In other words, it encompasses security of both data at rest and data in transmission. Numerous strategies have been employed by the professionals to protect organizations and records from interlopers. To deal with malevolent projects, there are a great deal of "off the rack" just as tweaked items accessible in the market which additionally give ongoing barrier and security to the delicate and sensitive data. There is still absence of techniques on the most capable strategy to appear and execute tasks that can be accustomed to the activities taking place at real time. This paper focuses on looking into the present methodologies used to give digital security, and identify the various loopholes in the present situation

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Security Assessment of Software Design using Neural Network

Cited by 7 publications

References 12 publications

Applications of AI in classical software engineering

Applications of AI in classical software engineering

ANN Modelling on Vulnerabilities Detection in Code Smells-Associated Android Applications

Challenges of Information Security in the Contemporary Cyber Threat Perception

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