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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Veröffentlicht in:	SN computer science Jg. 4; H. 4; S. 331
Hauptverfasser:	Althar, Raghavendra Rao, Samanta, Debabrata, Purushotham, Sathvik, Sengar, Sandeep Singh, Hewage, Chaminda
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Nature Singapore 01.07.2023 Springer Nature B.V
Schlagworte:	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
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Abstract	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.
AbstractList	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.
ArticleNumber	331
Author	Hewage, Chaminda Althar, Raghavendra Rao Purushotham, Sathvik Sengar, Sandeep Singh Samanta, Debabrata
Author_xml	– sequence: 1 givenname: Raghavendra Rao surname: Althar fullname: Althar, Raghavendra Rao organization: CHRIST (Deemed to be) University, First American India Private Limited – sequence: 2 givenname: Debabrata surname: Samanta fullname: Samanta, Debabrata organization: CHRIST (Deemed to be) University, Department of Computational Information Technology, Rochester Institute of Technology-RIT Global – sequence: 3 givenname: Sathvik surname: Purushotham fullname: Purushotham, Sathvik organization: Department of Information Science, BMS Institute of Technology and Management – sequence: 4 givenname: Sandeep Singh orcidid: 0000-0003-2171-9332 surname: Sengar fullname: Sengar, Sandeep Singh email: SSSengar@cardiffmet.ac.uk organization: Department of Computer Science, Cardiff Metropolitan University – sequence: 5 givenname: Chaminda surname: Hewage fullname: Hewage, Chaminda organization: Department of Computer Science, Cardiff Metropolitan University
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WenJLiSLinZHuYHuangCSystematic literature review of machine learning based software development effort estimation modelsInf Softw Technol2012541415910.1016/j.infsof.2011.09.002 Chapter 16: lessons learned from software analytics in practice—the art and science of analyzing software data [Book] Yamaguchi F, Lottmann M, Rieck K. Generalized vulnerability extrapolation using abstract syntax trees. in: proceedings of the 28th annual computer security applications conference, Orlando. 2012. p. 359–368. Mishra MK, Sengar SS, Mukhopadhyay S. Algorithm for secure visual communication In: 2015 2nd international conference on signal processing and integrated networks. IEEE; 2015. p. 831–836. ZengPLinGPanLTaiYZhangJSoftware vulnerability analysis and discovery using deep learning techniques: a surveyIEEE Access2020819715819717210.1109/ACCESS.2020.3034766 SengarSSKumarSRainaPMahaliyanMBot detection in social networks based on multilayered deep learning approachSens Transducers202024453743 Othmane L, Chehrazi G, Bodden E, Tsalovski P, Brucker AD, Miseldine P. Factors impacting the effort required to fix security vulnerabilities. In: Proceedings of the 18th international conference on information security, vol 9290, Trondheim. 2015. p. 102–119. NinaHPow-SangJAVillavicencioMSystematic mapping of the literature on secure software developmentIEEE Access20219368523686710.1109/ACCESS.2021.3062388 Aggarwal CC, Wang H. A Survey of clustering algorithms for graph data. In: Aggarwal CC, Wang H, editors. Managing and mining graph data. Boston: Springer US; 2010. p. 275–301. LongFRinardMAutomatic patch generation by learning correct codeSIGPLAN Not201651129831210.1145/2914770.2837617 AltharRRSamantaDThe realist approach for evaluation of computational intelligence in software engineeringInnov Syst Softw Eng2021171172710.1007/s11334-020-00383-2 AhmadAA systematic literature review on using machine learning algorithms for software requirements identification on stack overflowSecur Commun Netw2020202010.1155/2020/8830683 Hamill M, Goseva-Popstojanova K. Software faults fixing effort, NASA Goddard Space Flight Center, Greenbelt, 2014. MoyoSMnkandlaEA novel lightweight solo software development methodology with optimum security practicesIEEE Access20208337353374710.1109/ACCESS.2020.2971000 KocaguneliEMenziesTMendesETransfer learning in effort estimationEmpir Softw Eng201520381384310.1007/s10664-014-9300-5 Al-MatouqHMahmoodSAlshayebMNiaziMA maturity model for secure software design: a multivocal studyIEEE Access2020821575821577610.1109/ACCESS.2020.3040220 Althar RR, Samanta D, Kaur M, Alnuaim AA, Aljaffan N, Aman Ullah M. Software systems security vulnerabilities management by exploring the capabilities of language models using NLP. Comput Intell Neurosci. 2021:e8522839. Given-WilsonTJafriNLegayACombined software and hardware fault injection vulnerability detectionInnov Syst Softw Eng202016210112010.1007/s11334-020-00364-5 Peng H, Mou L, Li G, Liu Y, Zhang L, Jin Z. Building program vector representations for deep learning. In: Proceedings of the 8th international conference on knowledge science, engineering and management, vol 9403, Chongqing, China. 2015. p. 547–553. Shin Y, Williams, L. An initial study on the use of execution complexity metrics as indicators of software vulnerabilities. In: Proceeding of the 7th international workshop on Software engineering for secure systems-SESS ’11. 2011. GhaffarianSMShahriariHRSoftware vulnerability analysis and discovery using machine-learning and data-mining techniques: a surveyACM Comput Surv2017504 DamHKTranTPhamTNgSWGrundyJGhoseAAutomatic feature learning for predicting vulnerable software componentsIEEE Trans Softw Eng2021471678510.1109/TSE.2018.2881961 ShinYWilliamsLCan traditional fault prediction models be used for vulnerability prediction?Empir Softw Eng2013181255910.1007/s10664-011-9190-8 GrayARMacDonellSGA comparison of techniques for developing predictive models of software metricsInf Softw Technol199739642543710.1016/S0950-5849(96)00006-7 BreretonPKitchenhamBABudgenDTurnerMKhalilMLessons from applying the systematic literature review process within the software engineering domainJ Syst Softw200780457158310.1016/j.jss.2006.07.009 Rodeghero P, Jiang S, Armaly A, McMillan C. Detecting user story information in developer-client conversations to generate extractive summaries. In: 2017 IEEE/ACM 39th international conference on software engineering (ICSE). 2017. p. 49–59. A survey of clustering algorithms for graph data, Managing and mining graph data. 2010. p. 275–301. Sengar SS, Hariharan U, Rajkumar K. Multimodal biometric authentication system using deep learning method. In: 2020 international conference on emerging smart computing and informatics (ESCI). IEEE. 2020. p. 309–312. Wallace D. Software requirements analysis as fault predictor. 2003. Mezouar ME, Zhang F, Zou Y. Local versus global models for effort-aware defect prediction. In: Proceedings of the 26th annual international conference on computer science and software engineering, Toronto, 2016. p. 178–187. QuYUsing K-core decomposition on class dependency networks to improve bug prediction model’s practical performanceIEEE Trans Softw Eng2021472348366420779310.1109/TSE.2019.2892959 ben Othmane L, Chehrazi G, Bodden E, Tsalovski P. Brucker AD, Miseldine P. Factors impacting the effort required to fix security vulnerabilities. Inf Secur. 2015:102–119. Bosu A, Carver JC, Hafiz M, Hilley P , Janni D. Identifying the characteristics of vulnerable code changes: an empirical study. In: Proceedings of the 22nd ACM SIGSOFT international symposium on foundations of software engineering, Hong Kong, China. 2014. p. 257–268. Ö. SönmezFKiliçBGHolistic web application security visualization for multi-project and multi-phase dynamic application security test resultsIEEE Access20219258582588410.1109/ACCESS.2021.3057044 Singh AP, Kumar V, Sengar SS, Wairiya M. Detection and prevention of phishing attack using dynamic watermarking. In: International conference on advances in information technology and mobile communication. Berlin: Springer; 2011. p. 132-137. IqbalWAbbasHDaneshmandMRaufBBangashYAAn in-depth analysis of iot security requirements, challenges, and their countermeasures via software-defined securityIEEE Internet Things J2020710102501027610.1109/JIOT.2020.2997651 MenziesTGreenwaldJFrankAData mining static code attributes to learn defect predictorsIEEE Trans Softw Eng200733121310.1109/TSE.2007.256941 Rao Althar R, Samanta D, Konar D, Bhattacharyya S. Software source code: statistical modeling. De Gruyter; 2021. Long F, Rinard M. Automatic patch generation by learning correct code. In: Proceedings of the 43rd annual ACM SIGPLAN-SIGACT symposium on principles of programming languages, St. Petersburg. 2016. p. 298–312. KhanRAKhanSUKhanHUIlyasMSystematic mapping study on security approaches in secure software engineeringIEEE Access20219191391916010.1109/ACCESS.2021.3052311 T Given-Wilson (1785_CR8) 2020; 16 L Ben Othmane (1785_CR3) 2017; 2 F Ö. Sönmez (1785_CR33) 2021; 9 AR Gray (1785_CR6) 1997; 39 P Foggia (1785_CR21) 2014; 28 1785_CR14 1785_CR15 1785_CR12 1785_CR13 H Nina (1785_CR34) 2021; 9 1785_CR18 1785_CR19 1785_CR16 1785_CR38 1785_CR39 Y Qu (1785_CR36) 2021; 47 SM Ghaffarian (1785_CR10) 2017; 50 SS Sengar (1785_CR30) 2020; 244 W Iqbal (1785_CR29) 2020; 7 F Long (1785_CR45) 2016; 51 RR Althar (1785_CR2) 2021; 17 P Zeng (1785_CR43) 2020; 8 1785_CR22 T Menzies (1785_CR41) 2007; 33 1785_CR44 P Brereton (1785_CR17) 2007; 80 1785_CR20 1785_CR25 1785_CR26 H Al-Matouq (1785_CR32) 2020; 8 1785_CR23 1785_CR24 S Moyo (1785_CR31) 2020; 8 A Ahmad (1785_CR40) 2020; 2020 1785_CR1 N Medeiros (1785_CR35) 2020; 8 AT Misirli (1785_CR42) 2014; 40 1785_CR28 J Wen (1785_CR7) 2012; 54 Y Shin (1785_CR11) 2013; 18 E Kocaguneli (1785_CR9) 2015; 20 1785_CR5 RA Khan (1785_CR27) 2021; 9 HK Dam (1785_CR37) 2021; 47 1785_CR4
References_xml	– reference: ben Othmane L, Chehrazi G, Bodden E, Tsalovski P. Brucker AD, Miseldine P. Factors impacting the effort required to fix security vulnerabilities. Inf Secur. 2015:102–119. – reference: LongFRinardMAutomatic patch generation by learning correct codeSIGPLAN Not201651129831210.1145/2914770.2837617 – reference: Aggarwal CC, Wang H. A Survey of clustering algorithms for graph data. In: Aggarwal CC, Wang H, editors. Managing and mining graph data. Boston: Springer US; 2010. p. 275–301. – reference: KhanRAKhanSUKhanHUIlyasMSystematic mapping study on security approaches in secure software engineeringIEEE Access20219191391916010.1109/ACCESS.2021.3052311 – reference: Althar RR, Samanta D, Kaur M, Alnuaim AA, Aljaffan N, Aman Ullah M. Software systems security vulnerabilities management by exploring the capabilities of language models using NLP. Comput Intell Neurosci. 2021:e8522839. – reference: Yamaguchi F, Lottmann M, Rieck K. Generalized vulnerability extrapolation using abstract syntax trees. in: proceedings of the 28th annual computer security applications conference, Orlando. 2012. p. 359–368. – reference: Al-MatouqHMahmoodSAlshayebMNiaziMA maturity model for secure software design: a multivocal studyIEEE Access2020821575821577610.1109/ACCESS.2020.3040220 – reference: AhmadAA systematic literature review on using machine learning algorithms for software requirements identification on stack overflowSecur Commun Netw2020202010.1155/2020/8830683 – reference: Mishra MK, Sengar SS, Mukhopadhyay S. Algorithm for secure visual communication In: 2015 2nd international conference on signal processing and integrated networks. IEEE; 2015. p. 831–836. – reference: ShinYWilliamsLCan traditional fault prediction models be used for vulnerability prediction?Empir Softw Eng2013181255910.1007/s10664-011-9190-8 – reference: MisirliATBenerABBayesian networks for evidence-based decision-making in software engineeringIEEE Trans Softw Eng201440653355410.1109/TSE.2014.2321179 – reference: Shin Y, Williams, L. An initial study on the use of execution complexity metrics as indicators of software vulnerabilities. In: Proceeding of the 7th international workshop on Software engineering for secure systems-SESS ’11. 2011. – reference: AltharRRSamantaDThe realist approach for evaluation of computational intelligence in software engineeringInnov Syst Softw Eng2021171172710.1007/s11334-020-00383-2 – reference: Singh AP, Kumar V, Sengar SS, Wairiya M. Detection and prevention of phishing attack using dynamic watermarking. In: International conference on advances in information technology and mobile communication. Berlin: Springer; 2011. p. 132-137. – reference: DamHKTranTPhamTNgSWGrundyJGhoseAAutomatic feature learning for predicting vulnerable software componentsIEEE Trans Softw Eng2021471678510.1109/TSE.2018.2881961 – reference: Rao Althar R, Samanta D, Konar D, Bhattacharyya S. Software source code: statistical modeling. De Gruyter; 2021. – reference: Ben OthmaneLChehraziGBoddenETsalovskiPBruckerADTime for addressing software security issues: prediction models and impacting factorsData Sci Eng20172210712410.1007/s41019-016-0019-8 – reference: Chapter 16: lessons learned from software analytics in practice—the art and science of analyzing software data [Book] – reference: FoggiaPPercannellaGVentoMGraph matching and learning in pattern recognition in the last 10 yearsInt J Pattern Recognit Artif Intell201428011450001319001010.1142/S0218001414500013 – reference: Peng H, Mou L, Li G, Liu Y, Zhang L, Jin Z. Building program vector representations for deep learning. In: Proceedings of the 8th international conference on knowledge science, engineering and management, vol 9403, Chongqing, China. 2015. p. 547–553. – reference: Wallace D. Software requirements analysis as fault predictor. 2003. – reference: Bosu A, Carver JC, Hafiz M, Hilley P , Janni D. Identifying the characteristics of vulnerable code changes: an empirical study. In: Proceedings of the 22nd ACM SIGSOFT international symposium on foundations of software engineering, Hong Kong, China. 2014. p. 257–268. – reference: Sengar SS, Hariharan U, Rajkumar K. Multimodal biometric authentication system using deep learning method. In: 2020 international conference on emerging smart computing and informatics (ESCI). IEEE. 2020. p. 309–312. – reference: MenziesTGreenwaldJFrankAData mining static code attributes to learn defect predictorsIEEE Trans Softw Eng200733121310.1109/TSE.2007.256941 – reference: WenJLiSLinZHuYHuangCSystematic literature review of machine learning based software development effort estimation modelsInf Softw Technol2012541415910.1016/j.infsof.2011.09.002 – reference: Ö. SönmezFKiliçBGHolistic web application security visualization for multi-project and multi-phase dynamic application security test resultsIEEE Access20219258582588410.1109/ACCESS.2021.3057044 – reference: Hamill M, Goseva-Popstojanova K. Software faults fixing effort, NASA Goddard Space Flight Center, Greenbelt, 2014. – reference: Mezouar ME, Zhang F, Zou Y. Local versus global models for effort-aware defect prediction. In: Proceedings of the 26th annual international conference on computer science and software engineering, Toronto, 2016. p. 178–187. – reference: ZengPLinGPanLTaiYZhangJSoftware vulnerability analysis and discovery using deep learning techniques: a surveyIEEE Access2020819715819717210.1109/ACCESS.2020.3034766 – reference: Othmane L, Chehrazi G, Bodden E, Tsalovski P, Brucker AD, Miseldine P. Factors impacting the effort required to fix security vulnerabilities. In: Proceedings of the 18th international conference on information security, vol 9290, Trondheim. 2015. p. 102–119. – reference: KocaguneliEMenziesTMendesETransfer learning in effort estimationEmpir Softw Eng201520381384310.1007/s10664-014-9300-5 – reference: Given-WilsonTJafriNLegayACombined software and hardware fault injection vulnerability detectionInnov Syst Softw Eng202016210112010.1007/s11334-020-00364-5 – reference: QuYUsing K-core decomposition on class dependency networks to improve bug prediction model’s practical performanceIEEE Trans Softw Eng2021472348366420779310.1109/TSE.2019.2892959 – reference: Long F, Rinard M. Automatic patch generation by learning correct code. 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