A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks

Maimó, Lorenzo Fernández; Gómez, Ángel Luis Perales; Clemente, Félix J. García; Pérez, Manuel Gil; Pérez, Gregorio Martínez

doi:10.1109/access.2018.2803446

Cited by 213 publications

(93 citation statements)

References 17 publications

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“…The introduction of fifth-generation (5G) mobile technology may very well make existing intrusion detection defenses obsolete. Maimó et al [158] proposes a novel way of quickly adapting to new data while using minimal computation resources. For their experiment, they used the CTU dataset [159] of different, real botnet attacks and labels.…”

Section: Network Intrusion Detectionmentioning

confidence: 99%

A Survey of Deep Learning Methods for Cyber Security

et al. 2019

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This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets.

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Section: Network Intrusion Detectionmentioning

confidence: 99%

A Survey of Deep Learning Methods for Cyber Security

et al. 2019

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“…UK's 5G MNOs or service brokers can use AI to allow self-adaptation of security needs according to live 5G traffic flowing through VNF. The AI-based VNF can employ auto-scaling and deploy more 5G network resources, employ appropriate deep learning framework, or even the detection model, with a more suitable one to the given cyber-defense context [83]. c) Quantum Safety and Next Generation Ciphers for B5G Applications: In order to face the challenge of cryptographic vulnerability threats due to advancements in computing capabilities of adversaries, UK 5G rollout should consider quantum-resistant authentication and data distribution scheme, and lattice-based homomorphic encryption technology, which greatly reduces the network burden at the same time achieves strong security including privacy protection and anti-quantum attacks [84].…”

Section: Security Challengesmentioning

confidence: 99%

The Potential Short- and Long-Term Disruptions and Transformative Impacts of 5G and Beyond Wireless Networks: Lessons Learnt From the Development of a 5G Testbed Environment

et al. 2020

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The capacity and coverage requirements for 5G and beyond wireless connectivity will be significantly different from the predecessor networks. To meet these requirements, the anticipated deployment cost in the UK is predicted to be in between £30bn-£50bn, whereas the current annual capital expenditure (CapEX) of the mobile network operators (MNOs) is £2.5bn. This prospect has vastly impacted and has become one of the major delaying factors for building the 5G physical infrastructure, whereas other areas of 5G developments are progressing at their speeds. Due to the expensive and complicated nature of the physical network infrastructure and spectrum, the second-tier operators, widely known as mobile virtual network operators (MVNO), are entirely dependent on the MNOs. In this paper, an extensive study is conducted to explore the possibilities of reducing the 5G deployment cost and developing business models. This study suggests that the use of existing public infrastructure (e.g., streetlights, telephone poles, etc.) has a great potential to contribute to a reduction of about 40% to 60% anticipated cost for the 5G network. This paper also reviews the recent Ofcom initiatives to release location-based licenses of the 5G-compatible radio spectrum at a nominal cost. Our study suggests that simplification of infrastructure and spectrum will encourage the exponential growth of scenario-specific cellular networks and will potentially disrupt the current business models of telecommunication business stakeholders -specifically MNOs and TowerCos. These scenario-specific networks are expected to be: a) private networks, b) community networks, and c) microoperators. Furthermore, due to the feasibility of dense device connectivity with 5G, the resolution of traditional and nontraditional data availability will increase significantly. This will encourage extensive data harvesting as a business opportunity and function within small and medium-sized enterprises (SMEs) as well as within large social networks. Consequently, the rise of new infrastructures and spectrum stakeholders is anticipated. This will fuel the development of a 5G data exchange ecosystem where data transactions are deemed to be high-value business commodities. The privacy and security of such data, as well as definitions of the associated revenue models and ownership, are challenging areas -and these have yet to emerge and mature fully. In this direction, this paper proposes the development of a unified data hub with layered structured privacy and security along with blockchain and encrypted off-chain based ownership/royalty

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“…There are visible anomalies that appear directly in the incorrect operation of the information computer system. The anomalies may not have visible signs, but they can cause failures after a long time [6].…”

Section: Introductionmentioning

confidence: 99%

A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection

Song

Beshley

Przystupa

et al. 2020

Sensors

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In this paper, to solve the problem of detecting network anomalies, a method of forming a set of informative features formalizing the normal and anomalous behavior of the system on the basis of evaluating the Hurst (H) parameter of the network traffic has been proposed. Criteria to detect and prevent various types of network anomalies using the Three Sigma Rule and Hurst parameter have been defined. A rescaled range (RS) method to evaluate the Hurst parameter has been chosen. The practical value of the proposed method is conditioned by a set of the following factors: low time spent on calculations, short time required for monitoring, the possibility of self-training, as well as the possibility of observing a wide range of traffic types. For new DPI (Deep Packet Inspection) system implementation, algorithms for analyzing and captured traffic with protocol detection and determining statistical load parameters have been developed. In addition, algorithms that are responsible for flow regulation to ensure the QoS (Quality of Services) based on the conducted static analysis of flows and the proposed method of detection of anomalies using the parameter Hurst have been developed. We compared the proposed software DPI system with the existing SolarWinds Deep Packet Inspection for the possibility of network traffic anomaly detection and prevention. The created software components of the proposed DPI system increase the efficiency of using standard intrusion detection and prevention systems by identifying and taking into account new non-standard factors and dependencies. The use of the developed system in the IoT communication infrastructure will increase the level of information security and significantly reduce the risks of its loss.

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A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks

Cited by 213 publications

References 17 publications

A Survey of Deep Learning Methods for Cyber Security

A Survey of Deep Learning Methods for Cyber Security

The Potential Short- and Long-Term Disruptions and Transformative Impacts of 5G and Beyond Wireless Networks: Lessons Learnt From the Development of a 5G Testbed Environment

A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection

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