Intrusion detection in cyber-physical systems using a generic and domain specific deep autoencoder model
The rapid growth of network-related services in the last decade has produced a huge amount of sensitive data on the internet. But networks are very much prone to intrusions where unauthorized users attempt to access sensitive information and even disrupt the system. Building a competent network intr...
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| Veröffentlicht in: | Computers & electrical engineering Jg. 91; S. 107044 |
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| Abstract | The rapid growth of network-related services in the last decade has produced a huge amount of sensitive data on the internet. But networks are very much prone to intrusions where unauthorized users attempt to access sensitive information and even disrupt the system. Building a competent network intrusion detection system (IDS) is necessary to prevent such attacks. IDSs generally use machine learning algorithms for classifying the attacks. But the features used for classification are not always suitable or sufficient. Besides, the number of intrusions is much less than the number of non-intrusions. Hence naive approaches may fail to provide acceptable performance due to this class imbalance. To counter this problem, in this paper, we propose a model that extracts useful features from the given features and then uses a deep learning algorithm to classify the intrusions. It is to be noted that underlying data points cannot be thought of as sampled from the same distribution, rather from two different distributions - one generic to all network intrusions, and the other specific to the domain. Keeping this fact in mind, we propose a unique Generic-Specific autoencoder architecture where the generic one learns the features that are common across all forms of network intrusions, and the specific ones learn features that are pertaining only to that domain. The model has been evaluated on the CICIDS2017 dataset, which is the largest dataset of this type available online, and we have set new benchmark results on this dataset. Source code of this work is available at: https://github.com/SoumyadeepThakur/Intrusion-AE |
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| AbstractList | The rapid growth of network-related services in the last decade has produced a huge amount of sensitive data on the internet. But networks are very much prone to intrusions where unauthorized users attempt to access sensitive information and even disrupt the system. Building a competent network intrusion detection system (IDS) is necessary to prevent such attacks. IDSs generally use machine learning algorithms for classifying the attacks. But the features used for classification are not always suitable or sufficient. Besides, the number of intrusions is much less than the number of non-intrusions. Hence naive approaches may fail to provide acceptable performance due to this class imbalance. To counter this problem, in this paper, we propose a model that extracts useful features from the given features and then uses a deep learning algorithm to classify the intrusions. It is to be noted that underlying data points cannot be thought of as sampled from the same distribution, rather from two different distributions - one generic to all network intrusions, and the other specific to the domain. Keeping this fact in mind, we propose a unique Generic-Specific autoencoder architecture where the generic one learns the features that are common across all forms of network intrusions, and the specific ones learn features that are pertaining only to that domain. The model has been evaluated on the CICIDS2017 dataset, which is the largest dataset of this type available online, and we have set new benchmark results on this dataset. Source code of this work is available at: https://github.com/SoumyadeepThakur/Intrusion-AE |
| ArticleNumber | 107044 |
| Author | Kumar, Neeraj Sarkar, Ram Thakur, Soumyadeep De, Rajonya Chakraborty, Anuran |
| Author_xml | – sequence: 1 givenname: Soumyadeep surname: Thakur fullname: Thakur, Soumyadeep email: sthakur@cse.iitb.ac.in organization: Department of Computer Science and Engineering, Indian Institute of Technology, Powai, Mumbai 400076, India – sequence: 2 givenname: Anuran surname: Chakraborty fullname: Chakraborty, Anuran organization: Department of Computer Science and Engineering, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata 700032, India – sequence: 3 givenname: Rajonya surname: De fullname: De, Rajonya organization: Department of Computer Science and Engineering, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata 700032, India – sequence: 4 givenname: Neeraj surname: Kumar fullname: Kumar, Neeraj email: neeraj.kumar@thapar.edu organization: Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology (Deemed University), Patiala, Punjab, India – sequence: 5 givenname: Ram surname: Sarkar fullname: Sarkar, Ram organization: Department of Computer Science and Engineering, Jadavpur University, 188, Raja S.C. Mallick Road, Kolkata 700032, India |
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| Cites_doi | 10.1016/j.asoc.2018.07.052 10.1145/1961189.1961199 10.1109/DCOSS.2019.00059 10.1007/s10618-009-0131-8 10.4108/eai.3-12-2015.2262516 10.1016/j.eswa.2017.08.002 10.23919/ICACT.2018.8323687 10.1109/ICCSN.2016.7586590 10.1016/j.jnca.2004.01.003 10.1109/WINCOM.2016.7777224 10.14722/ndss.2018.23204 10.1016/j.eswa.2009.05.029 10.13052/jsn2445-9739.2017.009 10.1109/TETCI.2017.2772792 10.1007/s00521-015-1964-2 10.1109/JSYST.2013.2257594 |
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| Keywords | Deep learning CICIDS2017 dataset Autoencoder Class imbalance Network intrusion Intrusion detection |
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start-page: 11994 issue: 10 year: 2009 ident: 10.1016/j.compeleceng.2021.107044_bib0016 article-title: Intrusion detection by machine learning: a review publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2009.05.029 – volume: 2017 start-page: 177 issue: 1 year: 2017 ident: 10.1016/j.compeleceng.2021.107044_bib0003 article-title: Towards a reliable intrusion detection benchmark dataset publication-title: Softw Netw doi: 10.13052/jsn2445-9739.2017.009 – volume: 2 start-page: 41 issue: 1 year: 2018 ident: 10.1016/j.compeleceng.2021.107044_bib0002 article-title: A deep learning approach to network intrusion detection publication-title: IEEE Trans Emerg Top Comput Intell doi: 10.1109/TETCI.2017.2772792 – start-page: 1565 year: 2018 ident: 10.1016/j.compeleceng.2021.107044_bib0024 article-title: ALDD: A Hybrid Traffic-User Behavior Detection Method for Application Layer DDoS – year: 2007 ident: 10.1016/j.compeleceng.2021.107044_bib0015 article-title: Network intrusion detection using naïve bayes publication-title: IJCSNS Int J Comput Sci Netw Secur – year: 2016 ident: 10.1016/j.compeleceng.2021.107044_bib0004 article-title: A hybrid method consisting of GA and SVM for intrusion detection system publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1964-2 – volume: 8 start-page: 1052 issue: 4 year: 2014 ident: 10.1016/j.compeleceng.2021.107044_bib0001 article-title: Intrusion detection in cyber-physical systems: techniques and challenges publication-title: IEEE Syst J doi: 10.1109/JSYST.2013.2257594 – start-page: 1 year: 2020 ident: 10.1016/j.compeleceng.2021.107044_bib0009 article-title: Secure V2V and V2I communication in intelligent transportation using cloudlets publication-title: IEEE Trans Serv Comput |
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| SubjectTerms | Algorithms Autoencoder CICIDS2017 dataset Class imbalance Classification Cyber-physical systems Data points Datasets Deep learning Feature extraction Intrusion detection Intrusion detection systems Machine learning Network intrusion Source code |
| Title | Intrusion detection in cyber-physical systems using a generic and domain specific deep autoencoder model |
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