A PARALLEL AVERAGED NEURAL NETWORK APPROACH FOR DETECTING SMARTPHONE PHISHES

Authors:

E Sudarshan,Seena Naik Korra,P. Pavan Kumar,S Venkatesulu,

DOI NO:

https://doi.org/10.26782/jmcms.2020.06.00008

Keywords:

Smartphone,Phishing,Mobile Security,GPU,Parallel avNNet,Smishing,

Abstract

Smartphone with the Internet is the most common item today and it provides the best online platform for businesses to trade their goods. Customers are more comfortable with online shopping and banking transactions, which are enough for hackers to cheat. Phishing attacks are now very common for smart phones. These attacks come in a variety of ways to steal customer sensitive information and payment information through fake Short Message Service(SMS) or E-Mail or Uniform Resource Locator (URL) links or applications(APPs). Therefore, the end user needs to know a few precautions to avoid phishing attackers. This paper explicitly discusses phishing attacks by their behavior and proposes a parallel defending approach to classifying messages as harm or spams using the Graphics Processing Unit (GPU) platform, which is achieved in logarithmic time of O(n log n) and also discusses the future scope.

Refference:

I. Abi-Chahla, Fedy. “Nvidia’s CUDA: The End of the CPU?’.” Tom’s Hardware (2008): 1954-7.

II. Almeida, T.A., Hidalgo, J.M.G. and Yamakami, A., “Contributions to the study of sms spam filtering: New collection and results”, in Proceedings of the 11th ACM symposium on Document engineering. (2011), 259-262.

III. Amrutkar C, Kim YS, Traynor P. Detecting mobile malicious webpages in real time. IEEE Trans Mobile Comput 2017;16(8):2184–97.

IV. APWG, APWG. “Phishing Activity Trends Report: 4th Quarter 2019.” Anti-Phishing Working Group. Retrieved December 12 (2019): 2019.

V. Arachchilage, Nalin, Steve Love, and Michael Scott. “Designing a mobile game to teach conceptual knowledge of avoiding’phishing attacks’.” International Journal for e-Learning Security 2, no. 1 (2012): 127-132.

VI. Arachchilage, NalinAsankaGamagedara, and Melissa Cole. “Design a mobile game for home computer users to prevent from “phishing attacks”.” In International Conference on Information Society (i-Society 2011), pp. 485-489. IEEE, 2011.

VII. Arachchilage, NalinAsankaGamagedara, and Mumtaz Abdul Harmeed. “Integrating self-efficacy into a gamified approach to thwart phishing attacks.” arXiv preprint arXiv: 1706.07748 (2017).

VIII. Arachchilage, NalinAsankaGamagedara, and Steve Love. “A game design framework for avoiding phishing attacks.” Computers in Human Behavior 29, no. 3 (2013): 706-714.

IX. Basnet, Ram B., and TenzinDoleck. “Towards developing a tool to detect phishing URLs: a machine learning approach.” In 2015 IEEE International Conference on Computational Intelligence & Communication Technology, pp. 220-223. IEEE, 2015.

X. Besch, Matthias, and Hans Werner Pohl. “Flexible data parallel training of neural networks using MIMD-computers.” In Proceedings Euromicro

XI. CAPEC. CAPEC-164: mobile phishing; 2017. Available from https:// capec.mitre.org/data/definitions/164.html. [Accessed June 2017].

XII. Chan, P.P., Yang, C., Yeung, D.S. and Ng, W.W., “Spam filtering for short messages in adversarial environment”, Neurocomputing, Vol. 155, (2015), 167-176.

XIII. Choudhary, N. and Jain, A.K., “Towards filtering of spam messages using machine learning based technique”, in International Conference on Advanced Informatics for Computing Research, Springer. (2017), 18-30.

XIV. Cirecsan, D.; Meier, U.; Gambardella, L.M.; Schmidhuber, J. Deep big simple neural nets excel on hand-written digit recognition. arXiv: 1003.0358 v1 2010.

XV. Cormack, G.V., “Email spam filtering: A systematic review”, Foundations and Trends® in Information Retrieval, Vol. 1, No. 4, (2008), 335-455.

XVI. Cui, Qian, Guy-Vincent Jourdan, Gregor V. Bochmann, Russell Couturier, and Iosif-ViorelOnut. “Tracking phishing attacks over time.” In Proceedings of the 26th International Conference on World Wide Web, pp. 667-676. 2017.

XVII. D. Povey, A. Ghoshal, G.Boulianne, L. Burget, O.Glembek, N. Goel, M. Hannermann, P.Motl´ıˇcek, Y. Qian, P. Schwartz, J. Silovsk´y, G. Stemmer, and K. Vesel´y, “The kaldi speech recognition toolkit,” in ASRU. IEEE, 2011.

XVIII. Daniel Povey, Xiaohui Zhang, and SanjeevKhudanpur, “Parallel training of deep neural networks with natural gradient and parameter averaging,” arXiv preprint arXiv:1410.7455, 2014.

XIX. Dua, D. and Graff, C., Uci machine learning repository. 2017.

XX. El-Alfy, E.-S.M. and AlHasan, A.A., “Spam filtering framework for multimodal mobile communication based on dendritic cell algorithm”, Future Generation Computer Systems, Vol. 64, (2016), 98-107.

XXI. Fan, Chun-I., Han-Wei Hsiao, Chun-Han Chou, and Yi-Fan Tseng. “Malware detection systems based on API log data mining.” In 2015 IEEE 39th annual computer software and applications conference, vol. 3, pp. 255-260. IEEE, 2015.

XXII. Geoffrey E Hinton, Simon Osindero, and Yee-WhyeTeh, “A fast learning algorithm for deep belief nets,” Neural computation, vol. 18, no. 7, pp. 1527–1554, 2006.

XXIII. Gharvirian, F. and Bohloli, A., “Neural network based protection of software defined network controller against distributed denial of service attacks”, International Journal of Engineering, Transactions B: Applications, Vol. 30, No. 11, (2017), 1714-1722.
XXIV. Gholami, M., “Islanding detection method of distributed generation based on wavenet”, International Journal of Engineering, Transactions B: Applications, Vol. 32, No. 2, (2019), 242-248.

XXV. Goel, Diksha, and Ankit Kumar Jain. “Mobile phishing attacks and defence mechanisms: State of art and open research challenges.” Computers & Security 73 (2018): 519-544.

XXVI. Gómez Hidalgo, J.M., Bringas, G.C., Sánz, E.P. and García, F.C., “Content based sms spam filtering”, in Proceedings of the 2006 ACM symposium on Document engineering., (2006), 107-114.

XXVII. Grace, Michael, Yajin Zhou, Qiang Zhang, ShihongZou, and Xuxian Jiang. “Riskranker: scalable and accurate zero-day android malware detection.” In Proceedings of the 10th international conference on Mobile systems, applications, and services, pp. 281-294. 2012.

XXVIII. https://en.wikipedia.org/wiki/Phishing#History

XXIX. IMPERVA. Cross site scripting attacks; 2017. Available from https://www.incapsula.com/web-application-security/cross-site-scripting-xss-attacks.html. [Accessed June 2017].

XXX. Ji, H. and Zhang, H., “Analysis on the content features and their correlation of web pages for spam detection”, China Communications, Vol. 12, No. 3, (2015), 84-94.

XXXI. Junaid, M.B. and Farooq, M., “Using evolutionary learning classifiers to do mobilespam (SMS) filtering”, in Proceedings of the 13th annual conference on Genetic and evolutionary computation, (2011), 1795-1802.

XXXII. KarelVesel`y, ArnabGhoshal, Luk´asBurget, and Daniel Povey, “Sequence-discriminative training of deep neural networks,” in INTERSPEECH, 2013, pp. 2345–2349.

XXXIII. Kim, S.-E., Jo, J.-T. and Choi, S.-H., “Sms spam filterinig using keyword frequency ratio”, International Journal of Security and Its Applications, Vol. 9, No. 1, (2015), 329-336.

XXXIV. Klöckner, A. PyCuda: Even simpler GPU programming with Python. GPU Technology Conf. Proceedings, Sep. 2010, 2010.

XXXV. Klöckner, Andreas, Nicolas Pinto, Yunsup Lee, Bryan Catanzaro, Paul Ivanov, Ahmed Fasih, A. D. Sarma, D. Nanongkai, G. Pandurangan, and P. Tetali. “PyCUDA: GPU run-time code generation for high-performance computing.” Arxiv preprint arXiv 911 (2009).

XXXVI. Owens, John D., David Luebke, Naga Govindaraju, Mark Harris, Jens Krüger, Aaron E. Lefohn, and Timothy J. Purcell. “A survey of general‐purpose computation on graphics hardware.” In Computer graphics forum, vol. 26, no. 1, pp. 80-113. Oxford, UK: Blackwell Publishing Ltd, 2007.

XXXVII. ParandehMotlagh, F. and KhatibiBardsiri, A., “Detecting fake websites using swarm intelligence mechanism in human learning”, International Journal of Engineering, Transactions A: Basics, Vol. 31, No. 10, (2018), 1642-1650.

XXXVIII. Rekouche, Koceilah. “Early phishing.” arXiv preprint arXiv: 1106.4692 (2011).

XXXIX. Serrano, J.M.B., Palancar, J.H. and Cumplido, R., “The evaluation of ordered features for sms spam filtering”, in Iberoamerican Congress on Pattern Recognition, Springer., (2014), 383-390.

XL. Shahriar, Hossain, TulinKlintic, and Victor Clincy. “Mobile phishing attacks and mitigation techniques.” Journal of Information Security 6, no. 03 (2015): 206.

XLI. Sheikhi, S., M. T. Kheirabadi, and A. Bazzazi. “An Effective Model for SMS Spam Detection Using Content-based Features and Averaged Neural Network.” International Journal of Engineering 33, no. 2 (2020): 221-228.

XLII. Steinkraus, D.; Buck, I.; Simard, P. Using GPUs for machine learning algorithms. Eighth International Conference on Document Analysis and Recognition (ICDAR’05). IEEE, 2005, pp. 1115–1120.

XLIII. Su, Hang, and Haoyu Chen. “Experiments on parallel training of deep neural network using model averaging.” arXiv preprint arXiv:1507.01239 (2015).

XLIV. Sudarshan, E., and K. SeenaNaik. “A Parallel Approach for Maximum Quantization of Descendants Of Wavelet Trees.”
XLV. Suleiman, D. and Al-Naymat, G., “Sms spam detection using h2o framework”, Procedia Computer Science, Vol. 113, (2017), 154-161.

XLVI. Symantec. Symantec internet security threat report 2014, Vol. 19; 2017a.

XLVII. TaufiqNuruzzaman, M., Lee, C., Abdullah, M.F.A.b. and Choi, D., “Simple sms spam filtering on independent mobile phone”, Security and Communication Networks, Vol. 5, No. 10, (2012), 1209-1220.

XLVIII. Tewari A, Jain AK, Gupta BB. Recent survey of various defense mechanisms against phishing attacks. J Info Privacy Sec 2016;12(1):3–13.

XLIX. Uysal, A.K., Gunal, S., Ergin, S. and Gunal, E.S., “A novel framework for sms spam filtering”, in 2012 International Symposium on Innovations in Intelligent Systems and Applications, IEEE., (2012), 1-4.

L. Wardman, Brad, Michael Weideman, JakubBurgis, Nicole Harris, Blake Butler, and Nate Pratt. “A practical analysis of the rise in mobile phishing.” In Cyber Threat Intelligence, pp. 155-168. Springer, Charm, 2018.

LI. Xiaohui Zhang, Jan Trmal, Daniel Povey, and SanjeevKhudanpur, “Improving deep neural network acoustic models using generalized maxout networks,” in Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on. IEEE, 2014, pp. 215–219.

LII. Yang, Weining, AipingXiong, Jing Chen, Robert W. Proctor, and Ninghui Li. “Use of phishing training to improve security warning compliance: evidence from a field experiment.” In Proceedings of the hot topics in science of security: symposium and bootcamp, pp. 52-61. 2017.

LIII. Zainal, K., Sulaiman, N. and Jali, M., “An analysis of various algorithms for text spam classification and clustering using rapidminer and weka”, International Journal of Computer Science and Information Security, Vol. 13, No. 3, (2015), 66.

View Download