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A deep learning system for health care IoT and smartphone malware detection

  • S.I. : Healthcare Analytics
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Abstract

The use of smart and connected devices, such as Android and Internet of Things (IoT) have increased exponentially. In the last 10 years, mobiles and IoT devices have surpassed PC’s utilization. Android hosts an array of connected sensors like IoT. It has transformed a simple gadget into a hub of mobile phone with IoT. With a high number of clients and enormous assortment of Android applications it has been an appealing target for many security threats including malware attacks. To monitor a host of the applications that runs on Android and IoT devices, this study employs a deep learning based feature detector for malware detection which can easily be trained and be used with different classifiers to assess an application’s behavior. The features learnt by the detector can be reused to transfer their learning to any future endeavors toward malware detection. To test the accuracy and effectiveness of the feature detector we test it in two phases: (i) first the features extracted are fed to a fully connected network (FCN) with Softmax activation and in (ii) second scheme we use recurrent layers of attentions to classify the Applications either as malicious or benign. Our findings reveal that the proposed feature detector achieves significant results with an F1-Score of 98.97% and an accuracy of 98%.

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References

  1. Android Malware stats techjury. https://techjury.net/stats-about/android-market-share/. (Accessed 14 Oct 2019)

  2. Android Malware Stats f-secure. https://blog.f-secure.com/another-reason-99-percent-of-mobile-malware-targets-androids/. (Accessed 14 Oct 2019)

  3. Milosevic J, Sklavos N, Koutsikou K (2016) MalAware: effective and efficient run-time mobile malware detector malware in IoT software and hardware

  4. Arp D, Spreitzenbarth M, Hubner M, Gascon H, Rieck K, Siemens C (2014) DREBIN: effective and explainable detection of android malware in your pocket in NDSS

  5. Razzak MI, Imran M, Xu G (2020) Big data analytics for preventive medicine. Neural Comput Appl 32(9):4417–4451

    Article  Google Scholar 

  6. La Marra A, Martinelli F, Saracino A, Sheikhalishahi M (2016) MalProfiler: automatic and effective classification of android malicious apps in behavioral classes. In: International symposium on foundations and practice of security, Springer, pp 3–19

  7. Bedford A, Garvin S, Desharnais J, Tawbi N, Ajakan H, Audet F, Lebel B (2016) Andrana: quick and accurate malware detection for android. In: International symposium on foundations and practice of security, Springer, pp 20–35

  8. Yang Y, Wei Z, Xu Y, He H, Wang W (xxxx) DroidWard: an effective dynamic analysis method for vetting android applications cluster computing, pp. 1–11

  9. Ferrante A, Medvet E, Mercaldo F, Milosevic J, Visaggio CA (2016) Spotting the malicious moment: characterizing malware behavior using dynamic features. In: 2016 11th international conference on availability, reliability and security (ARES), IEEE, pp 372–381

  10. Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G (2020) Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput Appl 32(3):839–854

    Article  Google Scholar 

  11. Razzak MI, Imran M, Xu G (2018) Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks. IEEE J Biomed Health Inf 23(5):1911–1919

    Article  Google Scholar 

  12. Razzak MI, Naz S, Zaib A (2018) Deep learning for medical image processing: overview, challenges and the future. In: Classification in BioApps. Springer, Cham, pp 323–350

  13. Rehman A, Naz S, Razzak MI, Akram F, Imran M (2020) A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits Syst Sig Process 39(2):757–775

    Article  Google Scholar 

  14. Feizollah A, Anuar NB, Salleh R, Suarez-Tangil G, Furnell S (2017) AndroDialysis: analysis of android intent effectiveness in malware detection. Comput Secur 65:121

    Article  Google Scholar 

  15. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  16. Lescroart M, Agrawal P, Gallant J (2016) Both convolutional neural networks and voxel-wise encoding models of brain activity derived from ConvNets represent boundary-and surface-related features. J Vis 16(12):756

    Article  Google Scholar 

  17. Razzak I, Zafar K, Imran M, Xu G (2020) Randomized nonlinear one-class support vector machines with bounded loss function to detect of outliers for large scale IoT data. Fut Gener Comput Syst 112:715–723

    Article  Google Scholar 

  18. Kosmidis K, Kalloniatis C (2017) Machine learning and images for malware detection and classification. In: Proceedings of the 21st Pan-Hellenic conference on informatics, ACM, p 5

  19. McLaughlin N, Martinez del Rincon J, Kang B, Yerima S, Miller P, Sezer S, Safaei Y, Trickel E, Zhao Z, Doupe A et al (2017) Deep android malware detection. In: Proceedings of the seventh ACM on conference on data and application security and privacy, ACM, pp 301–308

  20. chars2vec intuition engineering. https://hackernoon.com/chars2vec-character-based-language-model-for-handling-real-world-texts-with-spelling-errors-and-a3e4053a147d. (Accessed 27 Feb 2019)

  21. chars2vec-code intuition engineering. https://github.com/IntuitionEngineeringTeam. (Accessed 27 Feb 2019)

  22. Yerima SY, Sezer S, Muttik I (2014) Android malware detection using parallel machine learning classifiers In: 2014 eighth international conference on next generation mobile apps, services and technologies, IEEE, pp 37–42

  23. Yerima SY, Sezer S, Muttik I (2015) Android malware detection: an eigenspace analysis approach. In: 2015 science and information conference (SAI), IEEE, pp 1236–1242

  24. Elish KO, Yao D, Ryder BG (2015) On the need of precise inter-app ICC classification for detecting android malware collusions. In: Proceedings of IEEE mobile security technologies (MoST), in conjunction with the IEEE symposium on security and privacy

  25. Verma S, Muttoo SK, Pal S (2016) MDROID:android based malware detection using MCM classifier

  26. Wang X, Zhu S, Zhou D, Yang Y (2017) Droid-AntiRM: taming control flow anti-analysis to support automated dynamic analysis of android malware. In: Proceedings of the 33rd annual computer security applications conference, ACM, pp 350–361

  27. Feng Y, Bastani O, Martins R, Dillig I, Anand S (2017) Automatically learning android malware signatures from few samples In: Proceedings of the 2017 annual network and distributed system security symposium (NDSS), San Diego, California, USA

  28. Karbab EB, Debbabi M, Derhab A, Mouheb D (2018) MalDozer: automatic framework for android malware detection using deep learning. Dig Investig 24:S48

    Article  Google Scholar 

  29. Schmicker R, Breitinger F, Baggili I (2018) AndroParse-an android feature extraction framework and dataset. In: International conference on digital forensics and cyber crime, Springer, pp 66–88

  30. Wang J, Jing Q, Gao J (2019) SEdroid: a robust android malware detector using selective ensemble learning. arXiv preprint arXiv:1909.03837

  31. Graf R, Kaplan LA, King R (2019) Neural network-based technique for android smartphone applications classification. In: 2019 11th international conference on cyber conflict (CyCon), IEEE, vol 900, pp 1–17

  32. Alazab M, Alazab M, Shalaginov A, Mesleh A, Awajan A (2020) Intelligent mobile malware detection using permission requests and api calls. Fut Gener Comput Syst 107:509

    Article  Google Scholar 

  33. Mercaldo F, Santone A (2020) Deep learning for image-based mobile malware detection. J Comput Virol Hack Techniq 6:1–15

    Google Scholar 

  34. Halim Z, Ali O, Khan G (2019) On the efficient representation of datasets as graphs to mine maximal frequent itemsets. IEEE transactions on knowledge and data engineering

  35. Halim Z, Atif M, Rashid A, Edwin CA (2017) Profiling players using real-world datasets: clustering the data and correlating the results with the big-five personality traits. In: IEEE transactions on affective computing

  36. Halim Z, Rehan M (2020) On identification of driving-induced stress using electroencephalogram signals: a framework based on wearable safety-critical scheme and machine learning. Inf Fusion 53:66

    Article  Google Scholar 

  37. 1D Convolutional nils ackermann, https://blog.goodaudience.com/introduction-to-1d-convolutional-neural-networks-in-keras-for-time-sequences-3a7ff801a2cf. (Accessed 05 Nov 2018)

  38. Total V (2012) Online: https://www.virustotal.com/en

  39. HaddadPajouh H, Dehghantanha A, Khayami R, Choo KKR (2018) A deep recurrent neural network based approach for Internet of Things malware threat hunting. Fut Gener Comput Syst 85:88

    Article  Google Scholar 

  40. Pa YMP, Suzuki S, Yoshioka K, Matsumoto T, Kasama T, Rossow C (2015) IoTPOT: analysing the rise of IoT compromises. In: 9th USENIX workshop on offensive technologies (WOOT 15) USENIX Association, Washington, D.C., https://www.usenix.org/conference/woot15/workshop-program/presentation/pa

  41. IoTMalware cyberiocs. https://freeiocs.cyberiocs.pro. (Accessed 04 Oct 2018)

  42. IoTMalware fei ding. https://github.com/ifding/iot-malware. (Accessed 04 Oct 2018)

  43. Van Der Walt S, Colbert SC, Varoquaux G (2011) The NumPy array: a structure for efficient numerical computation. Comput Sci Eng 13(2):22

    Article  Google Scholar 

  44. McKinney W (2011) Pandas: a foundational Python library for data analysis and statistics. In: Python for high performance and scientific computing, p 14

  45. Collette A (2013) Python and HDF5: unlocking scientific data. O’Reilly Media Inc, New York

    Google Scholar 

  46. Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9(3):90

    Article  Google Scholar 

  47. Waskom M (2014) Seaborn

  48. Peng H, Gates C, Sarma B, Li N, Qi Y, Potharaju R, Nita-Rotaru C, Molloy I (2012) Using probabilistic generative models for ranking risks of android apps. In: Proceedings of the 2012 ACM conference on computer and communications security, ACM, pp 241–252

  49. Garcia J, Hammad M, Malek S (2018) Lightweight, obfuscation-resilient detection and family identification of android malware. ACM Trans Softw Eng Methodol 26(3):11

    Article  Google Scholar 

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Authors and Affiliations

  1. NUCES-FAST, Peshawar, Pakistan

    Muhammad Amin

  2. Institute of Management Sciences, Peshawar, Pakistan

    Duri Shehwar, Tamleek Ali Tanveer & Sajid Anwar

  3. Heriot-Watt University Dubai Campus, Dubai, UAE

    Abrar Ullah

  4. Universidad Estatal Península de Santa Elena, La Libertad, Ecuador

    Teresa Guarda

Authors
  1. Muhammad Amin
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  2. Duri Shehwar
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  3. Abrar Ullah
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  4. Teresa Guarda
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  5. Tamleek Ali Tanveer
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  6. Sajid Anwar
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Correspondence to Tamleek Ali Tanveer.

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Amin, M., Shehwar, D., Ullah, A. et al. A deep learning system for health care IoT and smartphone malware detection. Neural Comput & Applic 34, 11283–11294 (2022). https://doi.org/10.1007/s00521-020-05429-x

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  • DOI: https://doi.org/10.1007/s00521-020-05429-x

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