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Anomaly Detection on Encrypted and High-Performance Data Networks by Means of Machine Learning Techniques
| Content Provider | Scilit |
|---|---|
| Author | Maimo, Lorenzo Fernandez Celdrán, Alberto Huertas Clemente, Félix J. García |
| Copyright Year | 2020 |
| Description | 168In recent years, the rapid expansion of the Internet of Things along with the recent 5G wireless communications technology has provided an excellent breeding ground for the proliferation of botnets. Similarly, the current ease with which new malware variations are created and the high effectiveness of the phishing campaigns, have carried out an alarming increase in the number of ransomware attacks against companies and institutions. In the 5G context, the high transfer rate and the huge volume of traffic prevent every packet from being examined. Additionally, the extensive use of network traffic encryption makes it impossible to analyze the packet payloads. Due to the difficulty of deep packet inspection in these scenarios, we present in this chapter an anomaly detection approach based on the aggregation of network flows. However, the use of flows makes the anomalous traffic patterns difficult to detect. To deal with that, our proposal utilizes machine learning detection methods which have proved to be effective in the classification of complex patterns. Our solution uses Software Defined Network to transparently capture the flows and isolate compromised devices. It also leverages Network Function Virtualization in order to allow self-adaptation to the traffic fluctuation of 5G, or replace a virtualized device infected. All this is done seamlessly, dynamically and in real time. Our results demonstrate the suitability of our proposal in two scenarios: 5G wireless networks and integrated clinical environments. Book Name: Recent Advances in Security, Privacy, and Trust for Internet of Things (IoT) and Cyber-Physical Systems (CPS) |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2018-0-96383-6&isbn=9780429270567&doi=10.1201/9780429270567-7&format=pdf |
| Ending Page | 190 |
| Page Count | 24 |
| Starting Page | 167 |
| DOI | 10.1201/9780429270567-7 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-12-16 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Recent Advances in Security, Privacy, and Trust for Internet of Things (iot) and Cyber-physical Systems (cps) Telecommunications Adaptation Machine Learning Anomaly Detection Scenarios Flows Packet 5g Wireless |
| Content Type | Text |
| Resource Type | Chapter |
