Analysis of Software Vulnerabilities Using Machine Learning Techniques

被引：0

作者：

Diako, Doffou Jerome ^{[1
]}

Achiepo, Odilon Yapo M. ^{[2
]}

Mensah, Edoete Patrice ^{[3
]}

机构：

[1] INPHB Yamoussoukro, EDP, Yamoussoukro, Cote Ivoire

[2] Peleforo Gon Coulibaly Univ, Korhogo, Cote Ivoire

[3] INPHB Yamoussoukro, Yamoussoukro, Cote Ivoire

来源：

E-INFRASTRUCTURE AND E-SERVICES FOR DEVELOPING COUNTRIES (AFRICOMM 2019) | 2020年 / 311卷

关键词：

Machine learning; Vulnerabilities; Naive Bayes; Support vectors machines; CVSS;

D O I：

10.1007/978-3-030-41593-8_3

中图分类号：

F0 [经济学]; F1 [世界各国经济概况、经济史、经济地理]; C [社会科学总论];

学科分类号：

0201 ; 020105 ; 03 ; 0303 ;

摘要：

With the increasing development of software technologies, we see that software vulnerabilities are a very critical issue of IT security. Because of their serious impacts, many different approaches have been proposed in recent decades to mitigate the damage caused by software vulnerabilities. Machine learning is also part of an approach to solve this problem. The main objective of this document is to provide three supervised machine to predict software vulnerabilities from a dataset of 6670 observations from national vulnerabilities database (NVD). The effectiveness of the proposed models has been evaluated with several performance indicators including Accuracy.

引用

页码：30 / 37

页数：8

共 50 条

[41] Detecting Android Security Vulnerabilities Using Machine Learning and System Calls Analysis
Campos, Carlos Renato Salim
Jaafar, Fehmi
Malik, Yasir
[J]. 2019 COMPANION OF THE 19TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY (QRS-C 2019), 2019, : 109 - 113
[42] Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey
Ghaffarian, Seyed Mohammad
Shahriari, Hamid Reza
[J]. ACM COMPUTING SURVEYS, 2017, 50 (04)
[43] Machine Learning to Combine Static Analysis Alerts with Software Metrics to Detect Security Vulnerabilities: An Empirical Study
Pereira, Jose D'Abruzzo
Campos, Joao R.
Vieira, Marco
[J]. 2021 17TH EUROPEAN DEPENDABLE COMPUTING CONFERENCE (EDCC 2021), 2021, : 1 - 8
[44] Analysis of Diabetes mellitus using Machine Learning Techniques
Bhat, Salliah Shafi
Selvam, Venkatesan
Ansari, Gufran Ahmad
Ansari, Mohd Dilshad
[J]. 2022 5TH INTERNATIONAL CONFERENCE ON MULTIMEDIA, SIGNAL PROCESSING AND COMMUNICATION TECHNOLOGIES (IMPACT), 2022,
[45] Twitter Sentiment Analysis Using Machine Learning Techniques
Le, Bac
Huy Nguyen
[J]. ADVANCED COMPUTATIONAL METHODS FOR KNOWLEDGE ENGINEERING, 2015, 358 : 279 - 289
[46] Credit Risk Analysis Using Machine Learning Techniques
Shiv, S. J.
Murthy, Srinivasa
Challuru, Krishnaprasad
[J]. 2018 FOURTEENTH INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING (ICINPRO) - 2018, 2018, : 214 - 218
[47] Analysis of Airbnb Prices using Machine Learning Techniques
Dhillon, Jasleen
Eluri, Nandana Priyanka
Kaur, Damanpreet
Chhipa, Aafreen
Gadupudi, Ashwin
Eravi, Rajeswari Cherupulli
Pirouz, Matin
[J]. 2021 IEEE 11TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC), 2021, : 297 - 303
[48] Analysis of Endoscopy Video Using Machine Learning Techniques
Saraf, Santosh S.
Udupi, G. R.
Hajare, Santosh D.
[J]. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2012, 2 (02) : 97 - 101
[49] Sentiment Analysis in Twitter using Machine Learning Techniques
Neethu, M. S.
Rajasree, R.
[J]. 2013 FOURTH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATIONS AND NETWORKING TECHNOLOGIES (ICCCNT), 2013,
[50] Automatic Detection and Correction of Vulnerabilities using Machine Learning
Tommy, Robin
Sundeep, Gullapudi
Jose, Hima
[J]. 2017 INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN COMPUTER, ELECTRICAL, ELECTRONICS AND COMMUNICATION (CTCEEC), 2017, : 1062 - 1065

← 1 2 3 4 5 →