Neuro-Fuzzy Based Software Risk Estimation Tool

Neuro-Fuzzy Based Software Risk Estimation Tool

Pooja Rani

Contact

Dalwinder Singh Salaria

Contact

Lovely Professional University

Neuro-Fuzzy Based Software Risk Estimation Tool

Article Fingerprint

ReserarchID

CSTSDE1SL31

Neuro-Fuzzy Based Software Risk Estimation Tool Banner

AI TAKEAWAY

Connecting with the Eternal Ground

Abstract

To develop the secure software is one of the major concerns in the software industry. To make the easier task of finding and fixing the security flaws, software developers should integrate the security at all stages of Software Development Life Cycle (SDLC).In this paper, based on Neuro-Fuzzy approach software Risk Prediction tool is created. Firstly Fuzzy Inference system is created and then Neural Network based three different training algorithms: BR (Bayesian Regulation), BP (Back propagation) and LM (Levenberg-Marquardt) are used to train the neural network. From the results it is conclude that for the Software Risk Estimation, BR (Bayesian Regulation) performs better and also achieves the greater accuracy than other algorithms.

References

24 Cites in Article

Reference Format

T Lee,S Jung,H In,H Lee (2007). Cyber Threat Trend Analysis Model Using HMM.
Frank Greitzer,Andrew Moore,Dawn Cappelli,Dee Andrews,Lynn Carroll,Thomas Hull (2008). Combating the Insider Cyber Threat.
Jeffrey Ingalsbe,Tim Louis Kunimatsu,Nancy Baeten,Mead (2008). Threat modeling: diving into the deep end.
P Ongsakorn,K Turney,M Thornton,S Nair,S Szygenda,T Manikas (2010). Cyber threat trees for large system threat cataloging and analysis.
Michael Gegick,Laurie Williams (2005). Matching attack patterns to security vulnerabilities in softwareintensive system designs.
Vandana Gandotra,Archana Singhal,Punam Bedi (2009). Threat Mitigation, Monitoring and Management Plan - A New Approach in Risk Management.
Xucheng Tang,Beijun Shen (2009). Extending Model Driven Architecture with Software Security Assessment.
Danny Dhillon (2011). Developer-Driven Threat Modeling: Lessons Learned in the Trenches.
T Okubo,H Kaiya,N Yoshioka (2011). Effective security impact analysis with patterns for software enhancement.
Antonietta Stango,Neeli Prasad,Dimitris Kyriazanos (2009). A Threat Analysis Methodology for Security Evaluation and Enhancement Planning.
T Frantz,K Carley (2009). Information assurances and threat identification in networked organizations.
H Debar,M Becker,D Siboni (1992). A neural network component for an intrusion detection system.
S Mukkamala,G Janoski,A Sung (2002). Intrusion detection using neural networks and support vector machines.
R Gong,M Zulkernine,P Abolmaesumi (2005). A software implementation of a genetic algorithm based approach to network intrusion detection.
K Haslum,A Abraham,S Knapskog (2008). Hinfra: Hierarchical neuro-fuzzy learning for online risk assessment.
E Ngai,F Wat (2005). Fuzzy decision support system for risk analysis in e-commerce development.
Vandana Gandotra,Archana Singhal,Punam Bedi (2010). A step towards Secure Software System using fuzzy logic.
Shuliang Li (2000). The development of a hybrid intelligent system for developing marketing strategy.
C Shieh,C Lin (2000). Direction of arrival estimation based on phase differences using neural fuzzy network.
J Wong,D Ho,L Capretz (2008). Calibrating function point backfiring conversion ratios using neuro-fuzzy technique.
Kjetil Haslum,Ajith Abraham,Svein Knapskog (2008). HiNFRA: Hierarchical Neuro-Fuzzy Learning for Online Risk Assessment.
B Boehm (1991). Software risk management: principles and practices.
Y Hu,X Zhang,X Sun,J Zhang,J Du,J Zhao (2010). A unified intelligent model for software project risk analysis and planning.
T Bragina,G Tabunshchyk (2011). 11th International Conference on Experience of Designing and Application of CAD Systems in Microelectronics (CADSM-2011) Met in February [Chapters].

Download References

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Pooja Rani. 2013. \u201cNeuro-Fuzzy Based Software Risk Estimation Tool\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 13 (GJCST Volume 13 Issue C6).

More Citation Formats

Select Citation Style:

Download Citation

GJCST Volume 13 Issue C6

Explore Journals Explore Volume Read This Issue

Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Fuzzy logic Neural Network neuro-fuzzy Software Security software threat

Submission ReceivedDecember 11, 2012
Peer Review Double Blind
Handling Editor
Accepted December 31, 2012
Published January 15, 2013

Version of record

v1.2

Issue date

June 4, 2013

Language

Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

View in VR

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

View in 3D

Article Matrices

Total Score: 102

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Pooja Rani, Dalwinder Singh Salaria (PhD/Dr. count: 0)

View Count (all-time): 275

Total Views (Real + Logic): 9777

Total Downloads (simulated): 2505

Publish Date: 2013 06, Tue

Monthly Totals (Real + Logic):

Month 1: 40 views
Month 2: 50 views
Month 3: 28 views
Month 4: 52 views
Month 5: 55 views
Month 6: 41 views
Month 7: 47 views
Month 8: 30 views
Month 9: 23 views
Month 10: 25 views
Month 11: 45 views
Month 12: 19 views
Month 13: 21 views
Month 14: 45 views
Month 15: 26 views
Month 16: 26 views
Month 17: 50 views
Month 18: 22 views
Month 19: 37 views
Month 20: 22 views
Month 21: 25 views
Month 22: 41 views
Month 23: 37 views
Month 24: 16 views
Month 25: 38 views
Month 26: 26 views
Month 27: 22 views
Month 28: 45 views
Month 29: 14 views
Month 30: 50 views
Month 31: 14 views
Month 32: 15 views
Month 33: 37 views
Month 34: 44 views
Month 35: 15 views
Month 36: 32 views
Month 37: 31 views
Month 38: 36 views
Month 39: 43 views
Month 40: 41 views
Month 41: 26 views
Month 42: 28 views
Month 43: 19 views
Month 44: 40 views
Month 45: 17 views
Month 46: 34 views
Month 47: 21 views
Month 48: 24 views
Month 49: 37 views
Month 50: 34 views
Month 51: 28 views
Month 52: 20 views
Month 53: 32 views
Month 54: 31 views
Month 55: 40 views
Month 56: 29 views
Month 57: 30 views
Month 58: 35 views
Month 59: 35 views
Month 60: 14 views
Month 61: 45 views
Month 62: 26 views
Month 63: 23 views
Month 64: 13 views
Month 65: 28 views
Month 66: 36 views
Month 67: 15 views
Month 68: 27 views
Month 69: 28 views
Month 70: 49 views
Month 71: 36 views
Month 72: 33 views
Month 73: 47 views
Month 74: 21 views
Month 75: 31 views
Month 76: 46 views
Month 77: 25 views
Month 78: 26 views
Month 79: 26 views
Month 80: 11 views
Month 81: 36 views
Month 82: 37 views
Month 83: 37 views
Month 84: 34 views
Month 85: 22 views
Month 86: 41 views
Month 87: 38 views
Month 88: 36 views
Month 89: 21 views
Month 90: 37 views
Month 91: 35 views
Month 92: 42 views
Month 93: 15 views
Month 94: 17 views
Month 95: 20 views
Month 96: 38 views
Month 97: 31 views
Month 98: 43 views
Month 99: 38 views
Month 100: 38 views
Month 101: 44 views
Month 102: 37 views
Month 103: 28 views
Month 104: 36 views
Month 105: 36 views
Month 106: 45 views
Month 107: 49 views
Month 108: 36 views
Month 109: 20 views
Month 110: 46 views
Month 111: 43 views
Month 112: 35 views
Month 113: 20 views
Month 114: 36 views
Month 115: 14 views
Month 116: 17 views
Month 117: 42 views
Month 118: 36 views
Month 119: 42 views
Month 120: 44 views
Month 121: 27 views
Month 122: 35 views
Month 123: 34 views
Month 124: 31 views
Month 125: 32 views
Month 126: 21 views
Month 127: 34 views
Month 128: 31 views
Month 129: 26 views
Month 130: 26 views
Month 131: 43 views
Month 132: 20 views
Month 133: 38 views
Month 134: 14 views
Month 135: 36 views
Month 136: 27 views
Month 137: 33 views
Month 138: 35 views
Month 139: 34 views
Month 140: 13 views
Month 141: 31 views
Month 142: 33 views
Month 143: 38 views
Month 144: 32 views
Month 145: 25 views
Month 146: 43 views
Month 147: 36 views
Month 148: 40 views
Month 149: 55 views
Month 150: 27 views
Month 151: 30 views
Month 152: 37 views
Month 153: 25 views
Month 154: 41 views
Month 155: 44 views
Month 156: 35 views

Total Views: 9777

Total Downloads: 2505

2026 Trends

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]