Proposed algorithm for smart grid DDoS detection based on deep learning

Modern communication systems are probable to surface new challenges while introducing innovative fronts concerning context consciousness in wireless networks. The main outcome behind this expected technological jump will be a whole novel set of intuiting aptitudes forecasted for Fifth Generation (5G) enabled devices. In line with 5G, Software Defined Networking (SDN) is also rising as an intrinsically novel phenomenon. SDN is an unconventional methodology and key technology in modern communication. SDN maintains favorable novelty about network programmability, where network administration is permitted with extraordinary intellections. SDN architecture has the prospective to allow, simplify or augment security implementations in network through instantly reprogrammable centralized view of the data plane. In the near future, 5G and SDN will ripe mobile communication through the development of state-of-the-art implementations such as a smart city, advanced military security, modern national defense, intelligent traffic, etc.; thereby, these emerging mobile communication concepts invoke various significant topics, where security is a paramount implication. Therefore, we initiate our focus from basic architecture of 5G and SDN. Next, we analyze security requirements, solutions and challenges in joint paradigm of 5G and SDN. Further, considering the modern communication technological shift, we discuss future trends and research directions in the joint era of 5G and SDN technologies. 

Survey on Joint Paradigm of 5G and SDN Emerging Mobile Technologies: Architecture, Security, Challenges and Research Directions

The development of the intelligent transport space (ITS) comes with the challenge of securing transportation data. As the vehicular network is highly dynamic, the network architecture is susceptible to distributed malicious attacks irrespective of the emergence and integration of enabling technologies. A Software Defined Network (SDN) based VANET is an improvement over the traditional VANET and may be susceptible to attacks as a result of its centralized structure, resulting in dangerous circumstances. SDN-based VANET security for 5G deployments is essential and calls for integrating artificial intelligence tools for threat detection. An intrusion detection system was suggested in this work to detect Distributed Denial of Service (DDoS) attacks in the vehicular space. The proposed solution uses the Grid Search Cross-Validation (GSCV) exhaustive parameter search technique and the Radial Basis Function Kernel of the Support Vector Machine (RBF-SVM) algorithm. The performance of other ML algorithms implemented was compared using key performance metrics. Experimental simulations to validate the proposed framework’s efficacy against DDoS intrusion show that the proposed scheme demonstrated an overall accuracy of 99.40% and a mean absolute error of 0.006. 

RBF-SVM kernel-based model for detecting DDoS attacks in SDN integrated vehicular network

A novel model watermarking for protecting generative adversarial network

With the development of Software Defined Networking (SDN), its security is becoming increasingly important. Since SDN has the characteristics of centralized management and programmable, attackers can easily take advantage of the security vulnerabilities of SDN to carry out distributed denial of service (DDoS) attacks, which will cause the memory of controllers and switches to be occupied, network bandwidth and server resources to be exhausted, affecting the use of normal users. To solve this problem, this paper designs and implements an online attack detection and mitigation SDN defense system. The SDN defense system consists of two modules: anomaly detection module and mitigation module. The anomaly detection model uses a lightweight hybrid deep learning method—Convolutional Neural Network and Extreme Learning Machine (CNN-ELM) for anomaly detection of traffic. The mitigation model uses IP traceback to locate the attacker and effectively filters out abnormal traffic by sending flow rule commands from the controller. Finally, we evaluate the SDN defense system. The experimental results show that the SDN defense system can accurately identify and effectively mitigate DDoS attack flows in real-time.

/pdf/sdn-defend-a-lightweight-online-attack-detection-and-10mnwalm.pdf

SDN-Defend: A Lightweight Online Attack Detection and Mitigation System for DDoS Attacks in SDN

Software Defined Networking provides new functionalities to easily manage, configure, and optimize network resources by introducing a clear separation between the control entity and the forwarding devices. Such functionalities also help network operators detect and mitigate the security attacks to the network and provide better security level when compared to the traditional networks. However, some security threats, particularly distributed denial of service (DDoS) attacks, keep their effectiveness in degrading the availability of the networks even if the networking paradigm have changed. Existing DDoS attack detection approaches for SDN are mainly based on statistical (threshold-based) and Machine Learning-based (ML) approaches. Considering the dynamic characteristics of the network traffic, finding a dynamic threshold is somehow problematic. On the other hand, finding an appropriate feature that can discriminate DDoS attack from normal traffic is challenging for ML-based approaches. Therefore, to address the aforementioned issues, in this work, we propose a DDoS attack detection and countermeasure scheme based on discrete wavelet transform (DWT) and auto-encoder neural network for SDN. The proposed scheme extracts statistical features from the wavelet transform to be processed by an auto-encoder neural network to detect samples of DDoS attack traffic. Later, to reduce the computational burden imposed by the neural network model, the average hit rate in the flow table of the switches is used to activate the DDoS detection of the scheme. We also provide a detailed performance analysis by considering the computational cost complexity of the algorithms proposed in scheme and the evaluation of the successful detection rate with simulations. Our experimental results show that the proposed scheme achieves high detection rate against DNS amplification, Network Time Protocol and TCP SYN flood attacks with a remarkably low false alarm rate. 

A DDoS attack detection and countermeasure scheme based on DWT and auto-encoder neural network for SDN

After many years of work, 5G standards are still under development and the corresponding technical specifications continue to evolve on the fly. At this moment, several countries have started to deploy 5G networks, and most of them have been following a Non-Standalone (NSA) path to incorporate the existing 4G and other legacy networks. Despite all the advertisement efforts, many people still do not have a clear view on how 5G can power all the promised mission-critical applications in a secure manner. In this paper, we bridge this gap by providing a concise review of some 5G’s new features, including the Service Based Architecture (SBA) and key Network Functions (NFs), the new security features in User Equipment (UE) and Radio Access Network (RAN), the new trust model and security mechanisms (e.g. the 5G AKA protocol), and the newly introduced common API framework (CAPIF). Along with the review of new features, we provide our observations on the potential security concerns accompanied with the relevant research results in the literature. We finally point out some new research directions.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09709835.pdf

A Systematic Analysis of 5G Networks With a Focus on 5G Core Security

With the increasing development of machine learning models in daily businesses, a strong need for intellectual property protection arised. For this purpose, current works suggest to leverage backdoor techniques to embed a watermark into the model, by overfitting to a set of particularly crafted and secret input-output pairs called triggers. By sending verification queries containing triggers, the model owner can analyse the behavior of any suspect model on the queries to claim its ownership. However, when it comes to scenarios where frequent monitoring is needed, the computational overhead of these verification queries in terms of volume demonstrates that backdoor-based watermarking appears to be too sensitive to outlier detection attacks and cannot guarantee the secrecy of the triggers. To solve this issue, we introduce BlindSpot, to watermark machine learning models through fairness. Our trigger-less approach is compatible with a high number of verification queries while being robust to outlier detection attacks. We show on Fashion-MNIST and CIFAR-10 datasets that BlindSpot is efficiently watermarking models while robust to outlier detection attacks, at a performance cost on the accuracy of 2%.

BlindSpot: Watermarking Through Fairness

A CNN-Based Semi-supervised Learning Approach for the Detection of SS7 Attacks

Software Defined Networking (SDN) paradigm is the redefinition of conventional networks based on the use of programmable entities together with a clear separation between the data plane and the control plane. The idea behind this new paradigm is to achieve a more flexible network architecture and better management capabilities. However, with all these advantages, it has been experienced that SDNs are open to new security threats and unfortunately, current technologies are not mature enough to overcome those vulnerabilities. As an example, we can consider the detection of the compromised switches in the network. Since switches are programmable entities, in SDN, they are the potential targets for attackers. When a switch is compromised, the attacker can use this switch to deploy incorrect packet forwarding and unsubstantiated packet dropping attacks. Current SDN protocols are not able to detect such kinds of attacks in the network and hence the whole network traffic can be affected in the end. One particular assumption is to use an approach that reflects the trust level of switches in the network for the detection of a compromised one. Therefore, in this paper, we propose Trust Enhanced Security (TES) for routing in SDN. The proposed approach provides three different trust computations to find the most suitable trust level for different states of a network. To show the applicability of the proposed approach, we demonstrate a set of simulations based on the detection of compromised switches. Simulation results show that the proposed model operates effectively to detect and eliminate compromised nodes while selecting secure paths.

Trust Enhanced Security for Routing in SDN

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