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Multimedia Systems
Erschienen in: Multimedia Systems 6/2022

16.03.2021 | Special Issue Paper

Deep learning based cyber bullying early detection using distributed denial of service flow

verfasst von: Muhammad Hassan Zaib, Faisal Bashir, Kashif Naseer Qureshi, Sumaira Kausar, Muhammad Rizwan, Gwanggil Jeon

Erschienen in: Multimedia Systems | Ausgabe 6/2022

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Abstract

Cyber-bullying has been on the rise especially after the explosive widespread of various cyber-attacks. Various types of techniques have been used to tackle cyber-bullying. These techniques focused primarily on data traffic for monitoring malicious activities. This research proposes a methodology where we can detect early Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks. First, we formulate the problem in a practical scenario by comparing flow and non-flow-based datasets using Mann Whitney U statistical test. Flow and non-flow-based datasets and Artificial Neural Network (ANN) and Support Vector Machine (SVM) is used for classification. To keep original features, we use variance, correlation, ¾ quartile method to eliminate the unimportant features. The forward selection wrapper method for feature selection is used to find out the best features. To validate the proposed methodology, we take multiple DoS and DDoS single flow and validate it on 10%, 20%, 30%, 40%, and 50%. For validation, the experimental results show + 90% accuracy on the early 10% flow.
Vorheriger Artikel CyberBERT: BERT for cyberbullying identification
Nächster Artikel Abusive language detection from social media comments using conventional machine learning and deep learning approaches

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Literatur
1.
Aliero, M.S., Ghani, I., Qureshi, K.N., Rohani, M.F.A.: An algorithm for detecting SQL injection vulnerability using black-box testing. J Ambient Intell Humaniz Comput 11(1), 249–266 (2020)CrossRef
2.
Aliero, M.S., Qureshi, K.N., Pasha, M.F., Ghani, I., Yauri, R.A.: Systematic review analysis on SQLIA detection and prevention approaches. Wirel Pers Commun 112(4), 2297–2333 (2020)CrossRef
3.
Hasan, A., Qureshi, K.: Internet of things device authentication scheme using hardware serialization. In: 2018 International conference on applied and engineering mathematics (ICAEM), pp. 109–114. IEEE (2018)
4.
Iqbal, S., Qureshi, K.N., Kanwal, N., Jeon, G.: Collaborative energy efficient zone‐based routing protocol for multihop internet of things. Trans Emerg Telecommun Technol, p. e3885 (2020)
5.
Nafea, H., Kifayat, K., Shi, Q., Qureshi, K.N., Askwith, B.: Efficient non-linear covert channel detection in TCP data streams. IEEE Access (2019)
6.
Qureshi, K.N., Bashir, M.U., Lloret, J., Leon, A.: Optimized cluster-based dynamic energy-aware routing protocol for wireless sensor networks in agriculture precision. J. Sens. 2020, (2020)
7.
Angelov, P.P., Gu, X.: Brief introduction to statistical machine learning. In: Empirical approach to machine learning, pp. 17–67. Springer, Berlin (2019)CrossRef
8.
Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Fut. Gener. Comput. Syst. 29(7), 1645–1660 (2013)CrossRef
9.
Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. 18(2), 1153–1176 (2015)CrossRef
10.
Qureshi, K.N., Bashir, F., Abdullah, A.H.: Provision of security in vehicular ad hoc networks through an intelligent secure routing scheme. In: 2017 International conference on frontiers of information technology (FIT), pp. 200–205, IEEE (2017)
11.
Wang, Q., Balasingham, I.: Wireless sensor networks–an introduction, wireless sensor networks: application-centric design. In: Tan, Y.K. (Ed.) InTech, ISBN: 978-953-307-321-7 (2010)
12.
Satpute, K., Agrawal, S., Agrawal, J., Sharma, S.: A survey on anomaly detection in network intrusion detection system using particle swarm optimization based machine learning techniques. In: Proceedings of the international conference on frontiers of intelligent computing: theory and applications (FICTA), pp. 441–452, Springer (2013)
13.
Cashell, B., Jackson, W.D., Jickling, M., Webel, B.: The economic impact of cyber-attacks. Congressional research service documents, CRS RL32331, Washington, DC, vol. 2 (2004)
14.
Wang, P., Wen, G., Yu, X., Yu, W., Wan, Y., Synchronization of resilient complex networks under attacks. In: IEEE transactions on systems, man, and cybernetics: systems (2019)
15.
Radanliev, P., De Roure, D.C., Maple, C., Nurse, J.R., Nicolescu, R., Ani, U.: Cyber risk in IoT systems (2019)
16.
Tirado, E., Turpin, B., Beltz, C., Roshon, P., Judge, R., Gagneja, K., A new distributed brute-force password cracking technique. In: International conference on future network systems and security, pp. 117–127, Springer (2018)
17.
Gligor, V.D.: A note on denial-of-service in operating systems. IEEE Trans. Softw. Eng. 3, 320–324 (1984)CrossRef
18.
Mirkovic, J., Reiher, P.: A taxonomy of DDoS attack and DDoS defense mechanisms. ACM SIGCOMM Comput. Commun. Rev. 34(2), 39–53 (2004)CrossRef
19.
Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., Vázquez, E.: Anomaly-based network intrusion detection: techniques, systems and challenges. Comput Secur 28(1–2), 18–28 (2009)CrossRef
20.
Guenther, N., Schonlau, M.: Support vector machines. Stata J. 16(4), 917–937 (2016)CrossRef
21.
Ghatak, A.: Introduction to neural networks. In: Deep learning with R, pp. 23–63. Springer, Berlin (2019)MATH
22.
Jadhav, S.D., Channe, H.: Comparative study of K-NN, naive Bayes and decision tree classification techniques. Int. J. Sci. Res. (IJSR) 5(1), 1842–1845 (2016)CrossRef
23.
Pal, S.K., Mitra, S.: Multilayer perceptron, fuzzy sets, and classification. IEEE Trans. Neural Netw. 3(5), 683–697 (1992)CrossRef
24.
Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: ICISSP, pp. 108–116 (2018)
25.
Yin, C., Zhu, Y., Fei, J., He, X.: A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5, 21954–21961 (2017)CrossRef
26.
Wang, Z.: Deep learning-based intrusion detection with adversaries. IEEE Access 6, 38367–38384 (2018)CrossRef
27.
Shone, N., Ngoc, T.N., Phai, V.D., Shi, Q.: A deep learning approach to network intrusion detection. IEEE Trans. Emerg. Top. Comput. Intell. 2(1), 41–50 (2018)CrossRef
28.
Javaid, A., Niyaz, Q., Sun, W., Alam, M.: A deep learning approach for network intrusion detection system. In: Proceedings of the 9th EAI international conference on bio-inspired information and communications technologies (formerly BIONETICS), pp. 21–26 (2016)
29.
Draper-Gil, G., Lashkari, A.H., Mamun, M.S.I., Ghorbani, A.A.: Characterization of encrypted and vpn traffic using time-related. In: Proceedings of the 2nd international conference on information systems security and privacy (ICISSP), pp. 407–414 (2016)
30.
Lashkari, A.H., Kadir, A.F.A., Gonzalez, H., Mbah, K.F., Ghorbani, A.A.: Towards a network-based framework for android malware detection and characterization. In: 2017 15th Annual Conference on Privacy, Security and Trust (PST), pp. 233–23309, IEEE (2017)
31.
Lashkari, A.H., Draper-Gil, G., Mamun, M.S.I., Ghorbani, A.A.: Characterization of tor traffic using time based features. In: ICISSP, pp. 253–262 (2017)
32.
Pektaş, A., Acarman, T.: A deep learning method to detect network intrusion through flow-based features. Int. J. Netw. Manag. 29(3), e2050 (2019)CrossRef
33.
Ustebay, S., Turgut, Z., Aydin, M.A.: Cyber attack detection by using neural network approaches: shallow neural network, deep neural network and autoencoder. In: International conference on computer networks, pp. 144–155, Springer (2019)
34.
Farhana, K., Rahman, M., Ahmed, M.T.: An intrusion detection system for packet and flow based networks using deep neural network approach. Int. J. Electr. Comput. Eng. 10(5), 5514 (2020)
35.
Nadiammai, G., Hemalatha, M.: Effective approach toward intrusion detection system using data mining techniques. Egypt. Inf. J. 15(1), 37–50 (2014)
36.
Mazraeh, S., Ghanavati, M., Neysi, S.H.N.: Intrusion detection system with decision tree and combine method algorithm. Int. Acad. J. Sci. Eng. 3(8), 21–31 (2016)
37.
Guo, C., Ping, Y., Liu, N., Luo, S.-S.: A two-level hybrid approach for intrusion detection. Neurocomputing 214, 391–400 (2016)CrossRef
38.
Neupane, N., Shakya, S.: Comparative analysis of backpropagation algorithm variants for network intrusion detection. In: 2017 International conference on computing, communication and automation (ICCCA), pp. 726–729, IEEE (2017)
39.
Ashfaq, R.A.R., Wang, X.-Z., Huang, J.Z., Abbas, H., He, Y.-L.: Fuzziness based semi-supervised learning approach for intrusion detection system. Inf. Sci. 378, 484–497 (2017)CrossRef
40.
Al-Yaseen, W.L., Othman, Z.A., Nazri, M.Z.A.: Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system. Expert Syst. Appl. 67, 296–303 (2017)CrossRef
41.
Kevric, J., Jukic, S., Subasi, A.: An effective combining classifier approach using tree algorithms for network intrusion detection. Neural Comput. Appl. 28(1), 1051–1058 (2017)CrossRef
42.
Mazini, M., Shirazi, B., Mahdavi, I.: Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms. J. King Saud Univ. Comput. Inf. Sci. 31(4), 541–553 (2019)
43.
Ali, M.H., Al Mohammed, B.A.D., Ismail, A., Zolkipli, M.F.: A new intrusion detection system based on fast learning network and particle swarm optimization. IEEE Access 6, 20255–20261 (2018)CrossRef
44.
Aljawarneh, S., Aldwairi, M., Yassein, M.B.: Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J. Comput. Sci. 25, 152–160 (2018)CrossRef
45.
Shenfield, A., Day, D., Ayesh, A.: Intelligent intrusion detection systems using artificial neural networks. ICT Express 4(2), 95–99 (2018)CrossRef
46.
Gurung, S., Ghose, M.K., Subedi, A.: Deep learning approach on network intrusion detection system using NSL-KDD dataset. Int. J. Comput. Netw. Inf. Secur. (IJCNIS) 11(3), 8–14 (2019)
47.
Su, T., Sun, H., Zhu, J., Wang, S., Li, Y.: BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset. IEEE Access 8, 29575–29585 (2020)CrossRef
48.
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)CrossRefMATH
49.
Revathi, S., Malathi, A.: A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. Int. J. Eng. Res. Technol. (IJERT) 2(12), 1848–1853 (2013)
50.
Aggarwal, P., Sharma, S.K.: Analysis of KDD dataset attributes-class wise for intrusion detection. Proc. Comput. Sci. 57, 842–851 (2015)CrossRef
51.
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetMATH
52.
Ahmed, I., Din, S., Jeon, G., Piccialli, F.: Exploring deep learning models for overhead view multiple object detection. IEEE Internet Things J. 7(7), 5737–5744 (2020)CrossRef
53.
Li, G., Zhou, Y., Bai, T., Pang, Y., Wu, W., Din, S., Jeon, G.: Performance analysis for low-complexity detection of MIMO vehicle-to-vehicle communication systems. Comput. Netw. 140, 92–100 (2018)CrossRef
54.
Qureshi, K.N., Rana, S.S., Ahmed, A., Jeon, G.: A novel and secure attacks detection framework for industrial internet of things. Sustain. Cities Soc. 61, 102343 (2020)CrossRef
55.
Wu, J., Li, J., Jeon, G.: Parallel binocular stereo vision-based GPU accelerated pedestrian detection and distance computation. J. Real Time Image Proc. 17(3), 447–457 (2020)CrossRef
56.
SaiduAliero, M., Qureshi, K.N., Pasha, M.F., Ahmad, A., Jeon, G.: Detection of structure query language injection vulnerability inweb driven database application. Concurrency and computation: practice and experience, p. e5936 (2020)
Metadaten
Titel
Deep learning based cyber bullying early detection using distributed denial of service flow
verfasst von
Muhammad Hassan Zaib
Faisal Bashir
Kashif Naseer Qureshi
Sumaira Kausar
Muhammad Rizwan
Gwanggil Jeon
Publikationsdatum
16.03.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Multimedia Systems / Ausgabe 6/2022
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-021-00771-z

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