Skip to main content
SpringerLink
Log in

Mac OS X Malware Detection with Supervised Machine Learning Algorithms

  • Chapter
  • First Online:
Handbook of Big Data Analytics and Forensics

Abstract

Nowadays, the complexity and variety of malware are dramatically increasing which makes the detection and classification of new and unknown malware much more difficult respectively. The traditional approaches for dealing with malware detection are not efficient anymore which paves the way for the adoption of machine learning algorithms as a solution for this issue. Also, there is a significant rise in malware concerned with Mac OS X devices due to their increasing market size. Consequently, in this paper, various machine learning algorithms from five main categories of Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Ensemble and Logistic Regression are adopted for detecting Mac OS X malware. Also, performance metrics and Receiver Operating Characteristic Curve (ROC) Curve are used for evaluating the performance of these algorithms. In addition, a novel technique of considering library calls as independent features is employed. The results demonstrate that considering the aforesaid new features increased the best accuracy obtained by about 4% which led to the accuracy of 94.7% achieved by Subspace KNN as an Ensemble classifier.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. S. Nakhodchi, A. Dehghantanha, H. Karimipour, Privacy and security in smart and precision farming: A bibliometric analysis, in Handbook of Big Data Privacy, (Springer, Cham, 2020), pp. 305–318

    Chapter  Google Scholar 

  2. S. Walker-Roberts, M. Hammoudeh, A. Dehghantanha, A systematic review of the availability and efficacy of countermeasures to internal threats in healthcare critical infrastructure. IEEE Access 6, 25167–25177 (2018 March). https://doi.org/10.1109/ACCESS.2018.2817560

    Article  Google Scholar 

  3. H.M. Rouzbahani, H. Karimipour, A. Dehghantanha, R.M. Parizi, Blockchain applications in power systems: A bibliometric analysis, in Blockchain Cybersecurity, Trust and Privacy, ed. by K.-K. R. Choo, A. Dehghantanha, R. M. Parizi, vol. 79, (Springer, Cham)

    Google Scholar 

  4. M. Conti, A. Dehghantanha, K. Franke, S. Watson, Internet of things security and forensics: Challenges and opportunities. Futur. Gener. Comput. Syst. 78, 544–546 (2018). https://doi.org/10.1016/j.future.2017.07.060

    Article  Google Scholar 

  5. A. Yazdinejad, R.M. Parizi, A. Dehghantanha, K.-K.R. Choo, P4-to-blockchain: A secure blockchain-enabled packet parser for software defined networking. Comput. Secur. 88, 101629 (2020). https://doi.org/10.1016/j.cose.2019.101629

    Article  Google Scholar 

  6. I. Santos, J. Devesa, F. Brezo, J. Nieves, P.G. Bringas, Opem: A static-dynamic approach for machine-learning-based malware detection,in International Joint Conference CISIS’12-ICEUTE 12-SOCO 12 Special Sessions, (Springer, Berlin, Heidelberg, 2013), pp. 271–280

    Google Scholar 

  7. H. Hashemi, A. Azmoodeh, A. Hamzeh, S. Hashemi, Graph embedding as a new approach for unknown malware detection. J. Comput. Virol. Hacking Tech. 13(3), 153–166 (2017)

    Article  Google Scholar 

  8. A. Azmoodeh, A. Dehghantanha, Big data and privacy: Challenges and opportunities, in Handbook of Big Data Privacy, (Springer, Cham, 2020), pp. 1–5. https://doi.org/10.1007/978-3-030-38557-6_1

    Chapter  Google Scholar 

  9. A. Azmoodeh, A. Dehghantanha, K.-K.R. Choo, Big data and internet of things security and forensics: Challenges and opportunities, in Handbook of Big Data and IoT Security, (Springer, Cham, 2019), pp. 1–4. https://doi.org/10.1007/978-3-030-10543-3_1

    Chapter  Google Scholar 

  10. L. Liu, B. Wang, B. Yu, Q. Zhong, Automatic malware classification and new malware detection using machine learning. Front. Inf. Technol. Electron. Eng. 18(9), 1336–1347 (2017)

    Article  Google Scholar 

  11. McAfee, McAfee Labs Threats Report: April 2017, no. April (2017), p. 49

    Google Scholar 

  12. H.H. Pajouh, A. Dehghantanha, R. Khayami, K.-K.R. Choo, Intelligent OS X malware threat detection with code inspection. J. Comput. Virol. Hacking Tech. 14(3), 213–223 (2018)

    Article  Google Scholar 

  13. A. Demontis et al., Yes, machine learning can be more secure! a case study on android malware detection. IEEE Trans. Depend. Secur. Comput. 16(4), 711–724 (2017)

    Google Scholar 

  14. M. Saharkhizan, A. Azmoodeh, A. Dehghantanha, K.-K.R. Choo, R.M. Parizi, An ensemble of deep recurrent neural networks for detecting IoT cyber attacks using network traffic. IEEE Internet Things J. 7(9), 8852–8859 (2020). https://doi.org/10.1109/jiot.2020.2996425

    Article  Google Scholar 

  15. M. Saharkhizan, A. Azmoodeh, H. HaddadPajouh, A. Dehghantanha, R.M. Parizi, G. Srivastava, A hybrid deep generative local metric learning method for intrusion detection, in Handbook of Big Data Privacy, (Springer International Publishing, Cham, 2020), pp. 343–357. https://doi.org/10.1007/978-3-030-38557-6_16

    Chapter  Google Scholar 

  16. A. Yazdinejad, A. Bohlooli, K. Jamshidi, Efficient design and hardware implementation of the OpenFlow v1.3 switch on the Virtex-6 FPGA ML605. J. Supercomput. 74(3), 1299 (2018). https://doi.org/10.1007/s11227-017-2175-7

    Article  Google Scholar 

  17. N. Milosevic, A. Dehghantanha, K.-K.R. Choo, Machine learning aided android malware classification. Comput. Electr. Eng. 61, 266–274 (2017)

    Article  Google Scholar 

  18. M. Damshenas, A. Dehghantanha, K.-K.R. Choo, R. Mahmud, M0droid: An android behavioral-based malware detection model. J. Inf. Priv. Secur. 11(3), 141–157 (2015)

    Google Scholar 

  19. A.N. Jahromi et al., An improved two-hidden-layer extreme learning machine for malware hunting. Comput. Secur. 89, 101655 (2020)

    Article  Google Scholar 

  20. S. Homayoun et al., DRTHIS: Deep ransomware threat hunting and intelligence system at the fog layer. Futur. Gener. Comput. Syst. 90, 94–104 (2019). https://doi.org/10.1016/j.future.2018.07.045

    Article  Google Scholar 

  21. H. Darabian et al., Detecting Cryptomining malware: A deep learning approach for static and dynamic analysis. J. Grid Comput. 18, 1–11 (2020)

    Article  Google Scholar 

  22. A. Azmoodeh, A. Dehghantanha, M. Conti, K.-K.R. Choo, Detecting crypto-ransomware in IoT networks based on energy consumption footprint. J. Ambient. Intell. Humaniz. Comput. 9(4), 1141–1152 (2018)

    Article  Google Scholar 

  23. S. Homayoun, A. Dehghantanha, M. Ahmadzadeh, S. Hashemi, R. Khayami, Know abnormal, find evil: Frequent pattern mining for ransomware threat hunting and intelligence. IEEE Trans. Emerg. Top. Comput. 8, 341 (2017)

    Article  Google Scholar 

  24. F.A. Narudin, A. Feizollah, N.B. Anuar, A. Gani, Evaluation of machine learning classifiers for mobile malware detection. Soft. Comput. 20(1), 343–357 (2016)

    Article  Google Scholar 

  25. H. Papadopoulos, N. Georgiou, C. Eliades, A. Konstantinidis, Android malware detection with unbiased confidence guarantees. Neurocomputing 280, 3–12 (2018)

    Article  Google Scholar 

  26. A. Yazdinejad, H. HaddadPajouh, A. Dehghantanha, R.M. Parizi, G. Srivastava, M.-Y. Chen, Cryptocurrency malware hunting: A deep recurrent neural network, in Applied Soft Computing, vol 96, (Elsevier, 2020), p. 106630

    Google Scholar 

  27. A. Yazdinejad, R.M. Parizi, G. Srivastava, A. Dehghantanha, K.K.R. Choo, Energy efficient decentralized authentication in internet of underwater things using blockchain, in 2019 IEEE Globecom Workshops (GC Wkshps), (IEEE, 2019), pp. 1–6

    Google Scholar 

  28. M. Aledhari, R. Razzak, R.M. Parizi, F. Saeed, Federated learning: A survey on enabling technologies, protocols, and applications. IEEE Access 8, 140699–140725 (2020). https://doi.org/10.1109/ACCESS.2020.3013541

    Article  Google Scholar 

  29. A. Yazdinejad, R.M. Parizi, A. Dehghantanha, H. Karimipour, G. Srivastava, M. Aledhari, Enabling drones in the internet of things with decentralized Blockchain-based security. IEEE Internet Things J., 1 (2020). https://doi.org/10.1109/jiot.2020.3015382

  30. V. Mothukuri, R.M. Parizi, S. Pouriyeh, Y. Huang, A. Dehghantanha, G. Srivastava, A survey on security and privacy of federated learning. Futur. Gener. Comput. Syst. 115, 619 (2020)

    Article  Google Scholar 

  31. R.M. Parizi, S. Homayoun, A. Yazdinejad, A. Dehghantanha, K.-K.R. Choo, Integrating Privacy Enhancing Techniques into Blockchains Using Sidechains (2019). https://doi.org/10.1109/CCECE.2019.8861821

  32. A. Yazdinejad, R.M. Parizi, A. Dehghantanha, G. Srivastava, S. Mohan, A.M. Rababah, Cost optimization of secure routing with untrusted devices in software defined networking. J. Parallel Distrib. Comput. 143, 36 (2020)

    Article  Google Scholar 

  33. I. Martín, J.A. Hernández, S. de los Santos, Machine-learning based analysis and classification of android malware signatures. Futur. Gener. Comput. Syst. 97, 295–305 (2019)

    Article  Google Scholar 

  34. H. Zhang, X. Xiao, F. Mercaldo, S. Ni, F. Martinelli, A.K. Sangaiah, Classification of ransomware families with machine learning based on N-gram of opcodes. Futur. Gener. Comput. Syst. 90, 211–221 (2019)

    Article  Google Scholar 

  35. H. Aghakhani, G. Fabio, M. Francesco, L. Martina, O. Stefano, B. Davide, V. Giovanni, K. Christopher, When malware is Packin'Heat; limits of machine learning classifiers based on static analysis features, in Network and Distributed Systems Security (NDSS) Symposium 2020. (2020)

    Chapter  Google Scholar 

  36. T. Dargahi, A. Dehghantanha, P.N. Bahrami, M. Conti, G. Bianchi, L. Benedetto, A cyber-kill-chain based taxonomy of crypto-ransomware features. J. Comput. Virol. Hacking Tech. 15(4), 277–305 (2019)

    Article  Google Scholar 

  37. E.M. Dovom, A. Azmoodeh, A. Dehghantanha, D.E. Newton, R.M. Parizi, H. Karimipour, Fuzzy pattern tree for edge malware detection and categorization in IoT. J. Syst. Archit. 97, 1–7 (2019)

    Article  Google Scholar 

  38. H. Darabian, A. Dehghantanha, S. Hashemi, S. Homayoun, K.R. Choo, An opcode-based technique for polymorphic internet of things malware detection. Concurr. Comput. Pract. Exp. 32(6), e5173 (2020)

    Article  Google Scholar 

  39. H. HaddadPajouh, A. Dehghantanha, R. Khayami, K.-K.R. Choo, A deep recurrent neural network based approach for internet of things malware threat hunting. Futur. Gener. Comput. Syst. 85, 88–96 (2018). https://doi.org/10.1016/j.future.2018.03.007

    Article  Google Scholar 

  40. A. Azmoodeh, A. Dehghantanha, K.-K.R. Choo, Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning. IEEE Trans. Sustain. Comput. 4(1), 88–95 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, University of Guelph, Guelph, ON, Canada

    Samira Eisaloo Gharghasheh & Shahrzad Hadayeghparast

Authors
  1. Samira Eisaloo Gharghasheh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shahrzad Hadayeghparast
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samira Eisaloo Gharghasheh .

Editor information

Editors and Affiliations

  1. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  2. School of Computer Science, University of Guelph, Guelph, ON, Canada

    Ali Dehghantanha

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gharghasheh, S.E., Hadayeghparast, S. (2022). Mac OS X Malware Detection with Supervised Machine Learning Algorithms. In: Choo, KK.R., Dehghantanha, A. (eds) Handbook of Big Data Analytics and Forensics. Springer, Cham. https://doi.org/10.1007/978-3-030-74753-4_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-74753-4_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-74752-7

  • Online ISBN: 978-3-030-74753-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation