Android Malware Detection Using ML: Comparison
View Latest Version
Please note this is a comparison between Version 3 by Janaka Senanayake and Version 2 by Dean Liu.

WitTh the increasing use of mobile devices, malware attacks are rising, especially on Android phones, which account for 72.2% of the total market share. Hackers try to attack smartphones with various methods such as credential theft, surveillance, and malicious advertising. Among numerous countermeasures, machine learning (ML)-based methods have proven to be an effective means of detecting these attacks, as they are able to derive a classifier from a set of training examples, thus eliminating the need for an explicit definition of the signatures when developing malware detectors. This paper provides a systematic review of is systematic review discussed ML-based Android malware detection techniques. It critically evaluatesd 106 carefully selected articles and highlightsed their strengths and weaknesses as well as potential improvements. Finally, tThe ML-based methods for detecting source code vulnerabilities arewere also discussed, because it might be more difficult to add security after the app is deployed. Therefore, this entry aimspaper aimed to enable researchers to acquire in-depth knowledge in the field and to identify potential future research and development directions.

  • Android security
  • malware detection
  • code vulnerability
  • machine learning
Please wait, diff process is still running!

References

  1. Number of Mobile Phone Users Worldwide from 2016 to 2023 (In Billions). Available online: https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/ (accessed on 19 May 2021).
  2. Mobile Operating System Market Share Worldwide. Available online: https://gs.statcounter.com/os-market-share/mobile/worldwide/ (accessed on 19 May 2021).
  3. Number of Android Applications on the Google Play Store. Available online: https://www.appbrain.com/stats/number-of-android-apps/ (accessed on 19 May 2021).
  4. Gibert, D.; Mateu, C.; Planes, J. The rise of machine learning for detection and classification of malware: Research developments, trends and challenges. J. Netw. Comput. Appl. 2020, 153, 102526.
  5. Khan, J.; Shahzad, S. Android Architecture and Related Security Risks. Asian J. Technol. Manag. Res. [ISSN: 2249–0892] 2015, 5, 14–18. Available online: http://www.ajtmr.com/papers/Vol5Issue2/Vol5Iss2_P4.pdf (accessed on 19 May 2021).
  6. Platform Architecture. Available online: https://developer.android.com/guide/platform (accessed on 19 May 2021).
  7. Android Runtime (ART) and Dalvik. Available online: https://source.android.com/devices/tech/dalvik (accessed on 19 May 2021).
  8. Cai, H.; Ryder, B.G. Understanding Android application programming and security: A dynamic study. In Proceedings of the 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME), Shanghai, China, 17–22 September 2017; pp. 364–375.
  9. Liu, K.; Xu, S.; Xu, G.; Zhang, M.; Sun, D.; Liu, H. A Review of Android Malware Detection Approaches Based on Machine Learning. IEEE Access 2020, 8, 124579–124607.
  10. Gilski, P.; Stefanski, J. Android os: A review. Tem J. 2015, 4, 116. Available online: https://www.temjournal.com/content/41/14/temjournal4114.pdf (accessed on 19 May 2021).
  11. Privacy in Android 11 | Android Developers. Available online: https://developer.android.com/about/versions/11/privacy (accessed on 19 May 2021).
  12. Garg, S.; Baliyan, N. Comparative analysis of Android and iOS from security viewpoint. Comput. Sci. Rev. 2021, 40, 100372.
  13. Odusami, M.; Abayomi-Alli, O.; Misra, S.; Shobayo, O.; Damasevicius, R.; Maskeliunas, R. Android malware detection: A survey. In International Conference on Applied Informatics; Springer: Cham, Switzerland, 2018; pp. 255–266.
  14. Bhat, P.; Dutta, K. A survey on various threats and current state of security in android platform. ACM Comput. Surv. (CSUR) 2019, 52, 1–35.
  15. Tam, K.; Feizollah, A.; Anuar, N.B.; Salleh, R.; Cavallaro, L. The evolution of android malware and android analysis techniques. ACM Comput. Surv. (CSUR) 2017, 49, 1–41.
  16. Li, L.; Li, D.; Bissyandé, T.F.; Klein, J.; Le Traon, Y.; Lo, D.; Cavallaro, L. Understanding android app piggybacking: A systematic study of malicious code grafting. IEEE Trans. Inf. Forensics Secur. 2017, 12, 1269–1284.
  17. Ashawa, M.A.; Morris, S. Analysis of Android malware detection techniques: A systematic review. Int. J. Cyber-Secur. Digit. Forensics 2019, 8, 177–187.
  18. Suarez-Tangil, G.; Tapiador, J.E.; Peris-Lopez, P.; Ribagorda, A. Evolution, detection and analysis of malware for smart devices. IEEE Commun. Surv. Tutor. 2013, 16, 961–987.
  19. Mos, A.; Chowdhury, M.M. Mobile Security: A Look into Android. In Proceedings of the 2020 IEEE International Conference on Electro Information Technology (EIT), Chicago, IL, USA, 31 July–1 August 2020; pp. 638–642.
  20. Faruki, P.; Bharmal, A.; Laxmi, V.; Ganmoor, V.; Gaur, M.S.; Conti, M.; Rajarajan, M. Android security: A survey of issues, malware penetration, and defenses. IEEE Commun. Surv. Tutor. 2014, 17, 998–1022.
  21. Android Security & Privacy 2018 Year in Review. Available online: https://source.android.com/security/reports/Google_Android_Security_2018_Report_Final.pdf (accessed on 19 May 2021).
  22. Kalutarage, H.K.; Nguyen, H.N.; Shaikh, S.A. Towards a threat assessment framework for apps collusion. Telecommun. Syst. 2017, 66, 417–430.
  23. Asavoae, I.M.; Blasco, J.; Chen, T.M.; Kalutarage, H.K.; Muttik, I.; Nguyen, H.N.; Roggenbach, M.; Shaikh, S.A. Towards automated android app collusion detection. arXiv 2016, arXiv:1603.02308.
  24. Asăvoae, I.M.; Blasco, J.; Chen, T.M.; Kalutarage, H.K.; Muttik, I.; Nguyen, H.N.; Roggenbach, M.; Shaikh, S.A. Detecting malicious collusion between mobile software applications: The Android case. In Data Analytics and Decision Support for Cybersecurity; Springer: Cham, Switzerland, 2017; pp. 55–97.
  25. Malik, J. Making sense of human threats and errors. Comput. Fraud Secur. 2020, 2020, 6–10.
  26. Calciati, P.; Kuznetsov, K.; Gorla, A.; Zeller, A. Automatically Granted Permissions in Android apps: An Empirical Study on their Prevalence and on the Potential Threats for Privacy. In Proceedings of the 17th International Conference on Mining Software Repositories, Seoul, Korea, 29–30 June 2020; pp. 114–124.
  27. Nguyen, D.C.; Wermke, D.; Acar, Y.; Backes, M.; Weir, C.; Fahl, S. A stitch in time: Supporting android developers in writing secure code. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, TX, USA, 30 October–3 November 2017; pp. 1065–1077.
  28. Garg, S.; Baliyan, N. Android Security Assessment: A Review, Taxonomy and Research Gap Study. Comput. Secur. 2020, 100, 102087.
  29. Van Engelen, J.E.; Hoos, H.H. A survey on semi-supervised learning. Mach. Learn. 2020, 109, 373–440.
  30. Alauthman, M.; Aslam, N.; Al-Kasassbeh, M.; Khan, S.; Al-Qerem, A.; Choo, K.K.R. An efficient reinforcement learning-based Botnet detection approach. J. Netw. Comput. Appl. 2020, 150, 102479.
  31. Shrestha, A.; Mahmood, A. Review of deep learning algorithms and architectures. IEEE Access 2019, 7, 53040–53065.
  32. Page, M.; McKenzie, J.; Bossuyt, P.; Boutron, I.; Hoffmann, T.; Mulrow, C.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2020, 372.
  33. Wohlin, C. Guidelines for snowballing in systematic literature studies and a replication in software engineering. In Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, London, UK, 13–14 May 2014; pp. 1–10.
  34. Li, L.; Bissyandé, T.F.; Papadakis, M.; Rasthofer, S.; Bartel, A.; Octeau, D.; Klein, J.; Traon, L. Static analysis of android apps: A systematic literature review. Inf. Softw. Technol. 2017, 88, 67–95.
  35. Pan, Y.; Ge, X.; Fang, C.; Fan, Y. A Systematic Literature Review of Android Malware Detection Using Static Analysis. IEEE Access 2020, 8, 116363–116379.
  36. Sharma, T.; Rattan, D. Malicious application detection in android—A systematic literature review. Comput. Sci. Rev. 2021, 40, 100373.
  37. Liu, Y.; Tantithamthavorn, C.; Li, L.; Liu, Y. Deep Learning for Android Malware Defenses: A Systematic Literature Review. arXiv 2021, arXiv:2103.05292.
  38. Ghaffarian, S.M.; Shahriari, H.R. Software vulnerability analysis and discovery using machine-learning and data-mining techniques: A survey. ACM Comput. Surv. (CSUR) 2017, 50, 1–36.
  39. Chen, T.; Mao, Q.; Yang, Y.; Lv, M.; Zhu, J. TinyDroid: A lightweight and efficient model for Android malware detection and classification. Mob. Inf. Syst. 2018, 2018.
  40. Nisa, M.; Shah, J.H.; Kanwal, S.; Raza, M.; Khan, M.A.; Damaševičius, R.; Blažauskas, T. Hybrid malware classification method using segmentation-based fractal texture analysis and deep convolution neural network features. Appl. Sci. 2020, 10, 4966.
  41. Amin, M.; Shah, B.; Sharif, A.; Ali, T.; Kim, K.l.; Anwar, S. Android malware detection through generative adversarial networks. Trans. Emerg. Telecommun. Technol. 2019, e3675.
  42. Arp, D.; Spreitzenbarth, M.; Hubner, M.; Gascon, H.; Rieck, K.; Siemens, C. Drebin: Effective and explainable detection of android malware in your pocket. In Proceedings of the 2014 Network and Distributed System Security Symposium, San Diego, CA, USA, 23–26 February 2014.
  43. Google Play. Available online: https://play.google.com/ (accessed on 19 May 2021).
  44. AndroZoo. Available online: https://androzoo.uni.lu/ (accessed on 19 May 2021).
  45. AppChina. Available online: https://tracxn.com/d/companies/appchina.com (accessed on 19 May 2021).
  46. Tencent. Available online: https://www.pcmgr-global.com/ (accessed on 19 May 2021).
  47. YingYongBao. Available online: https://android.myapp.com/ (accessed on 19 May 2021).
  48. Contagio. Available online: https://www.impactcybertrust.org/dataset_view?idDataset=1273/ (accessed on 19 May 2021).
  49. Zhou, Y.; Jiang, X. Dissecting android malware: Characterization and evolution. In Proceedings of the 2012 IEEE Symposium on Security and Privacy, San Francisco, CA, USA, 20–23 May 2012; pp. 95–109.
  50. VirusShare. Available online: https://virusshare.com/ (accessed on 19 May 2021).
  51. Intel Security/MacAfee. Available online: https://steppa.ca/portfolio-view/malware-threat-intel-datasets/ (accessed on 19 May 2021).
  52. Chen, K.; Wang, P.; Lee, Y.; Wang, X.; Zhang, N.; Huang, H.; Zou, W.; Liu, P. Finding unknown malice in 10 s: Mass vetting for new threats at the google-play scale. In Proceedings of the 24th USENIXSecurity Symposium (USENIX Security 15), Redmond, WA, USA, 7–8 May 2015; pp. 659–674.
  53. Android Malware Dataset. Available online: http://amd.arguslab.org/ (accessed on 19 May 2021).
  54. APKPure. Available online: https://m.apkpure.com/ (accessed on 19 May 2021).
  55. Anrdoid Permission Dataset. Available online: https://data.mendeley.com/datasets/b4mxg7ydb7/3 (accessed on 19 May 2021).
  56. Maggi, F.; Valdi, A.; Zanero, S. Andrototal: A flexible, scalable toolbox and service for testing mobile malware detectors. In Proceedings of the Third ACM Workshop on Security and Privacy in Smartphones & Mobile Devices, Berlin, Germany, 8 November 2013; pp. 49–54.
  57. Wandoujia App Market. Available online: https://www.wandoujia.com/apps (accessed on 19 May 2021).
  58. Google Playstore Appsin Kaggle. Available online: https://www.kaggle.com/gauthamp10/google-playstore-apps (accessed on 19 May 2021).
  59. CICMaldroid Dataset. Available online: https://www.unb.ca/cic/datasets/maldroid-2020.html (accessed on 19 May 2021).
  60. AZ Dataset. Available online: https://www.azsecure-data.org/other-data.html/ (accessed on 19 May 2021).
  61. Github Malware Dataset. Available online: https://github.com/topics/malware-dataset (accessed on 19 May 2021).
  62. Alqahtani, E.J.; Zagrouba, R.; Almuhaideb, A. A Survey on Android Malware Detection Techniques Using Machine Learning Algorithms. In Proceedings of the 2019 Sixth International Conference on Software Defined Systems (SDS), Rome, Italy, 10–13 June 2019; pp. 110–117.
  63. Lopes, J.; Serrão, C.; Nunes, L.; Almeida, A.; Oliveira, J. Overview of machine learning methods for Android malware identification. In Proceedings of the 2019 7th International Symposium on Digital Forensics and Security (ISDFS), Barcelos, Portugal, 10–12 June 2019; pp. 1–6.
  64. Choudhary, M.; Kishore, B. HAAMD: Hybrid analysis for Android malware detection. In Proceedings of the 2018 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 4–6 January 2018; pp. 1–4.
  65. Kouliaridis, V.; Kambourakis, G. A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection. Information 2021, 12, 185.
  66. Chen, L.; Hou, S.; Ye, Y.; Chen, L. An adversarial machine learning model against android malware evasion attacks. In Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data; Springer: Cham, Switzerland, 2017; pp. 43–55.
  67. Lubuva, H.; Huang, Q.; Msonde, G.C. A review of static malware detection for Android apps permission based on deep learning. Int. J. Comput. Netw. Appl. 2019, 6, 80–91.
  68. Li, J.; Sun, L.; Yan, Q.; Li, Z.; Srisa-An, W.; Ye, H. Significant permission identification for machine-learning-based android malware detection. IEEE Trans. Ind. Inform. 2018, 14, 3216–3225.
  69. Mcdonald, J.; Herron, N.; Glisson, W.; Benton, R. Machine Learning-Based Android Malware Detection Using Manifest Permissions. In Proceedings of the 54th Hawaii International Conference on System Sciences, Maui, HI, USA, 5–8 January 2021; p. 6976.
  70. Şahin, D.Ö.; Kural, O.E.; Akleylek, S.; Kılıç, E. A novel permission-based Android malware detection system using feature selection based on linear regression. Neural Comput. Appl. 2021, 1–16.
  71. Nawaz, A. Feature Engineering based on Hybrid Features for Malware Detection over Android Framework. Turk. J. Comput. Math. Educ. (TURCOMAT) 2021, 12, 2856–2864.
  72. Cai, L.; Li, Y.; Xiong, Z. JOWMDroid: Android malware detection based on feature weighting with joint optimization of weight-mapping and classifier parameters. Comput. Secur. 2021, 100, 102086.
  73. Zhang, P.; Cheng, S.; Lou, S.; Jiang, F. A novel Android malware detection approach using operand sequences. In Proceedings of the 2018 Third International Conference on Security of Smart Cities, Industrial Control System and Communications (SSIC), Shanghai, China, 18–19 October 2018; pp. 1–5.
  74. Wei, L.; Luo, W.; Weng, J.; Zhong, Y.; Zhang, X.; Yan, Z. Machine learning-based malicious application detection of android. IEEE Access 2017, 5, 25591–25601.
  75. Onwuzurike, L.; Mariconti, E.; Andriotis, P.; Cristofaro, E.D.; Ross, G.; Stringhini, G. MaMaDroid: Detecting Android malware by building Markov chains of behavioral models (extended version). ACM Trans. Priv. Secur. (TOPS) 2019, 22, 1–34.
  76. Zhang, H.; Luo, S.; Zhang, Y.; Pan, L. An efficient Android malware detection system based on method-level behavioral semantic analysis. IEEE Access 2019, 7, 69246–69256.
  77. Meng, G.; Xue, Y.; Xu, Z.; Liu, Y.; Zhang, J.; Narayanan, A. Semantic modelling of android malware for effective malware comprehension, detection, and classification. In Proceedings of the 25th International Symposium on Software Testing and Analysis, Saarbrücken, Germany, 18–20 July 2016; pp. 306–317.
  78. Wang, Z.; Li, C.; Yuan, Z.; Guan, Y.; Xue, Y. DroidChain: A novel Android malware detection method based on behavior chains. Pervasive Mob. Comput. 2016, 32, 3–14.
  79. Androguard. Available online: https://pypi.org/project/androguard/ (accessed on 19 May 2021).
  80. Damodaran, A.; Di Troia, F.; Visaggio, C.A.; Austin, T.H.; Stamp, M. A comparison of static, dynamic, and hybrid analysis for malware detection. J. Comput. Virol. Hacking Tech. 2017, 13, 1–12.
  81. Sun, Y.; Xie, Y.; Qiu, Z.; Pan, Y.; Weng, J.; Guo, S. Detecting Android malware based on extreme learning machine. In Proceedings of the 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 15th International Conference on Pervasive Intelligence and Computing, 3rd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), Orlando, FL, USA, 6–10 November 2017; pp. 47–53.
  82. Tian, K.; Yao, D.; Ryder, B.G.; Tan, G.; Peng, G. Detection of repackaged android malware with code-heterogeneity features. IEEE Trans. Dependable Secur. Comput. 2017, 17, 64–77.
  83. Kabakus, A.T. What static analysis can utmost offer for Android malware detection. Inf. Technol. Control 2019, 48, 235–249.
  84. Koli, J. RanDroid: Android malware detection using random machine learning classifiers. In Proceedings of the 2018 Technologies for Smart-City Energy Security and Power (ICSESP), Bhubaneswar, India, 28–30 March 2018; pp. 1–6.
  85. Lou, S.; Cheng, S.; Huang, J.; Jiang, F. TFDroid: Android malware detection by topics and sensitive data flows using machine learning techniques. In Proceedings of the 2019 IEEE 2nd International Conference on Information and Computer Technologies (ICICT), Kahului, HI, USA, 14–17 March 2019; pp. 30–36.
  86. Wang, W.; Gao, Z.; Zhao, M.; Li, Y.; Liu, J.; Zhang, X. DroidEnsemble: Detecting Android malicious applications with ensemble of string and structural static features. IEEE Access 2018, 6, 31798–31807.
  87. Garg, S.; Peddoju, S.K.; Sarje, A.K. Network-based detection of Android malicious apps. Int. J. Inf. Secur. 2017, 16, 385–400.
  88. Sikder, A.K.; Aksu, H.; Uluagac, A.S. 6thsense: A context-aware sensor-based attack detector for smart devices. In Proceedings of the 26th USENIX Security Symposium (USENIX Security 17), Vancouver, BC, Canada, 16–18 August 2017; pp. 397–414.
  89. Mahindru, A.; Singh, P. Dynamic permissions based android malware detection using machine learning techniques. In Proceedings of the 10th Innovations in Software Engineering Conference, Jaipur, India, 5–7 February 2017; pp. 202–210.
  90. Salehi, M.; Amini, M.; Crispo, B. Detecting malicious applications using system services request behavior. In Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Houston, TX, USA, 12–14 November 2019; pp. 200–209.
  91. Thangavelooa, R.; Jinga, W.W.; Lenga, C.K.; Abdullaha, J. DATDroid: Dynamic Analysis Technique in Android Malware Detection. Int. J. Adv. Sci. Eng. Inf. Technol. 2020, 10, 536–541.
  92. Hasan, H.; Ladani, B.T.; Zamani, B. MEGDroid: A model-driven event generation framework for dynamic android malware analysis. Inf. Softw. Technol. 2021, 135, 106569.
  93. Raphael, R.; Mathiyalagan, P. An Exploration of Changes Addressed in the Android Malware Detection Walkways. In Proceedings of the International Conference on Computational Intelligence, Cyber Security, and Computational Models, Coimbatore, India, 19–21 December 2019; Springer: Singapore, 2019; pp. 61–84.
  94. Jannat, U.S.; Hasnayeen, S.M.; Shuhan, M.K.B.; Ferdous, M.S. Analysis and detection of malware in Android applications using machine learning. In Proceedings of the 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’sBazar, Bangladesh, 7–9 February 2019; pp. 1–7.
  95. Kapratwar, A.; Di Troia, F.; Stamp, M. Static and Dynamic Analysis of Android Malware; ICISSP: Porto, Portugal, 2017; pp. 653–662.
  96. Leeds, M.; Keffeler, M.; Atkison, T. A comparison of features for android malware detection. In Proceedings of the SouthEast Conference, Kennesaw, GA, USA, 13–15 April 2017; pp. 63–68.
  97. Hadiprakoso, R.B.; Kabetta, H.; Buana, I.K.S. Hybrid-Based Malware Analysis for Effective and Efficiency Android Malware Detection. In Proceedings of the 2020 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), Jakarta, Indonesia, 19–20 November 2020; pp. 8–12.
  98. Surendran, R.; Thomas, T.; Emmanuel, S. A TAN based hybrid model for android malware detection. J. Inf. Secur. Appl. 2020, 54, 102483.
  99. Martín, A.; Menéndez, H.D.; Camacho, D. MOCDroid: Multi-objective evolutionary classifier for Android malware detection. Soft Comput. 2017, 21, 7405–7415.
  100. Mahindru, A.; Sangal, A. MLDroid—Framework for Android malware detection using machine learning techniques. Neural Comput. Appl. 2021, 33, 5183–5240.
  101. Xu, K.; Li, Y.; Deng, R.H.; Chen, K. Deeprefiner: Multi-layer android malware detection system applying deep neural networks. In Proceedings of the 2018 IEEE European Symposium on Security and Privacy (EuroS&P), London, UK, 24–26 April 2018; pp. 473–487.
  102. JADX. Available online: https://github.com/skylot/jadx/ (accessed on 19 May 2021).
  103. McLaughlin, N.; Martinez del Rincon, J.; Kang, B.; Yerima, S.; Miller, P.; Sezer, S.; Safaei, Y.; Trickel, E.; Zhao, Z.; Doupé, A.; et al. Deep android malware detection. In Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy, Scottsdale, AZ, USA, 22–24 March 2017; pp. 301–308.
  104. Amin, M.; Tanveer, T.A.; Tehseen, M.; Khan, M.; Khan, F.A.; Anwar, S. Static malware detection and attribution in android byte-code through an end-to-end deep system. Future Gener. Comput. Syst. 2020, 102, 112–126.
  105. Alzaylaee, M.K.; Yerima, S.Y.; Sezer, S. DL-Droid: Deep learning based android malware detection using real devices. Comput. Secur. 2020, 89, 101663.
  106. Vu, L.N.; Jung, S. AdMat: A CNN-on-Matrix Approach to Android Malware Detection and Classification. IEEE Access 2021, 9, 39680–39694.
  107. Millar, S.; McLaughlin, N.; del Rincon, J.M.; Miller, P. Multi-view deep learning for zero-day Android malware detection. J. Inf. Secur. Appl. 2021, 58, 102718.
  108. Qaisar, Z.H.; Li, R. Multimodal information fusion for android malware detection using lazy learning. Multimed. Tools Appl. 2021, 1–15.
  109. Acar, Y.; Stransky, C.; Wermke, D.; Weir, C.; Mazurek, M.L.; Fahl, S. Developers need support, too: A survey of security advice for software developers. In Proceedings of the 2017 IEEE Cybersecurity Development (SecDev), Cambridge, MA, USA, 24–26 September 2017; pp. 22–26.
  110. Mohammed, N.M.; Niazi, M.; Alshayeb, M.; Mahmood, S. Exploring software security approaches in software development lifecycle: A systematic mapping study. Comput. Stand. Interfaces 2017, 50, 107–115.
  111. Weir, C.; Becker, I.; Noble, J.; Blair, L.; Sasse, M.A.; Rashid, A. Interventions for long-term software security: Creating a lightweight program of assurance techniques for developers. Softw. Pract. Exp. 2020, 50, 275–298.
  112. Alenezi, M.; Almomani, I. Empirical analysis of static code metrics for predicting risk scores in android applications. In Proceedings of the 5th International Symposium on Data Mining Applications, Cham, Switzerland, 29 March 2018; Springer: Cham, Switzerland, 2018; pp. 84–94.
  113. Palomba, F.; Di Nucci, D.; Panichella, A.; Zaidman, A.; De Lucia, A. Lightweight detection of android-specific code smells: The adoctor project. In Proceedings of the 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), Klagenfurt, Austria, 20–24 February 2017; pp. 487–491.
  114. Pustogarov, I.; Wu, Q.; Lie, D. Ex-vivo dynamic analysis framework for Android device drivers. In Proceedings of the 2020 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, 18–21 May 2020; pp. 1088–1105.
  115. Amin, A.; Eldessouki, A.; Magdy, M.T.; Abdeen, N.; Hindy, H.; Hegazy, I. AndroShield: Automated android applications vulnerability detection, a hybrid static and dynamic analysis approach. Information 2019, 10, 326.
  116. Tahaei, M.; Vaniea, K.; Beznosov, K.; Wolters, M.K. Security Notifications in Static Analysis Tools: Developers’ Attitudes, Comprehension, and Ability to Act on Them. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, Yokohama, Japan, 8–13 May 2021; pp. 1–17.
  117. Goaër, O.L. Enforcing green code with Android lint. In Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering Workshops, Melbourne, VIC, Australia, 21–25 September 2020; pp. 85–90.
  118. Habchi, S.; Blanc, X.; Rouvoy, R. On adopting linters to deal with performance concerns in android apps. In Proceedings of the 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE), Montpellier, France, 3–7 September 2018; pp. 6–16.
  119. Wei, L.; Liu, Y.; Cheung, S.C. OASIS: Prioritizing static analysis warnings for Android apps based on app user reviews. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, Paderborn, Germany, 4–8 September 2017; pp. 672–682.
  120. Luo, L.; Dolby, J.; Bodden, E. MagpieBridge: A General Approach to Integrating Static Analyses into IDEs and Editors (Tool Insights Paper). In Proceedings of the 33rd European Conference on Object-Oriented Programming (ECOOP 2019). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany, 15–19 July 2019.
  121. Wang, Y.; Xu, G.; Liu, X.; Mao, W.; Si, C.; Pedrycz, W.; Wang, W. Identifying vulnerabilities of SSL/TLS certificate verification in Android apps with static and dynamic analysis. J. Syst. Softw. 2020, 167, 110609.
  122. Gupta, A.; Suri, B.; Kumar, V.; Jain, P. Extracting rules for vulnerabilities detection with static metrics using machine learning. Int. J. Syst. Assur. Eng. Manag. 2021, 12, 65–76.
  123. Kim, S.; Yeom, S.; Oh, H.; Shin, D.; Shin, D. Automatic Malicious Code Classification System through Static Analysis Using Machine Learning. Symmetry 2021, 13, 35.
  124. Bilgin, Z.; Ersoy, M.A.; Soykan, E.U.; Tomur, E.; Çomak, P.; Karaçay, L. Vulnerability Prediction From Source Code Using Machine Learning. IEEE Access 2020, 8, 150672–150684.
  125. Russell, R.; Kim, L.; Hamilton, L.; Lazovich, T.; Harer, J.; Ozdemir, O.; Ellingwood, P.; McConley, M. Automated vulnerability detection in source code using deep representation learning. In Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA, 17–20 December 2018; pp. 757–762.
  126. Chernis, B.; Verma, R. Machine learning methods for software vulnerability detection. In Proceedings of the Fourth ACM International Workshop on Security and Privacy Analytics, Tempe, AZ, USA, 21 March 2018; pp. 31–39.
  127. Wu, F.; Wang, J.; Liu, J.; Wang, W. Vulnerability detection with deep learning. In Proceedings of the 2017 3rd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China, 13–16 December 2017; pp. 1298–1302.
  128. Pang, Y.; Xue, X.; Wang, H. Predicting vulnerable software components through deep neural network. In Proceedings of the 2017 International Conference on Deep Learning Technologies, Chengdu, China, 2–4 June 2017; pp. 6–10.
  129. Garg, S.; Baliyan, N. A novel parallel classifier scheme for vulnerability detection in android. Comput. Electr. Eng. 2019, 77, 12–26.
  130. Ponta, S.E.; Plate, H.; Sabetta, A.; Bezzi, M.; Dangremont, C. A manually-curated dataset of fixes to vulnerabilities of open-source software. In Proceedings of the 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR), Montreal, QC, Canada, 26–27 May 2019; pp. 383–387.
  131. Namrud, Z.; Kpodjedo, S.; Talhi, C. AndroVul: A repository for Android security vulnerabilities. In Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering, Toronto, ON, Canada, 4–6 November 2019; pp. 64–71.
  132. Cui, J.; Wang, L.; Zhao, X.; Zhang, H. Towards predictive analysis of android vulnerability using statistical codes and machine learning for IoT applications. Comput. Commun. 2020, 155, 125–131.
  133. Zhuo, L.; Zhimin, G.; Cen, C. Research on Android intent security detection based on machine learning. In Proceedings of the 2017 4th International Conference on Information Science and Control Engineering (ICISCE), Changsha, China, 21–23 July 2017; pp. 569–574.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback