Distributed Collaborative Monitoring in Software Defined Networks

We propose a Distributed and Collaborative Monitoring system, DCM, with the following properties. First, DCM allow switches to collaboratively achieve flow monitoring tasks and balance measurement load. Second, DCM is able to perform per-flow monitoring, by which different groups of flows are monitored using different actions. Third, DCM is a memory-efficient solution for switch data plane and guarantees system scalability. DCM uses a novel two-stage Bloom filters to represent monitoring rules using small memory space. It utilizes the centralized SDN control to install, update, and reconstruct the two-stage Bloom filters in the switch data plane. We study how DCM performs two representative monitoring tasks, namely flow size counting and packet sampling, and evaluate its performance. Experiments using real data center and ISP traffic data on real network topologies show that DCM achieves highest measurement accuracy among existing solutions given the same memory budget of switches

Know Your Enemy: Stealth Configuration-Information Gathering in SDN

Software Defined Networking (SDN) is a network architecture that aims at providing high flexibility through the separation of the network logic from the forwarding functions. The industry has already widely adopted SDN and researchers thoroughly analyzed its vulnerabilities, proposing solutions to improve its security. However, we believe important security aspects of SDN are still left uninvestigated. In this paper, we raise the concern of the possibility for an attacker to obtain knowledge about an SDN network. In particular, we introduce a novel attack, named Know Your Enemy (KYE), by means of which an attacker can gather vital information about the configuration of the network. This information ranges from the configuration of security tools, such as attack detection thresholds for network scanning, to general network policies like QoS and network virtualization. Additionally, we show that an attacker can perform a KYE attack in a stealthy fashion, i.e., without the risk of being detected. We underline that the vulnerability exploited by the KYE attack is proper of SDN and is not present in legacy networks. To address the KYE attack, we also propose an active defense countermeasure based on network flows obfuscation, which considerably increases the complexity for a successful attack. Our solution offers provable security guarantees that can be tailored to the needs of the specific network under consideratio

On Efficiency of Selected Machine Learning Algorithms for Intrusion Detection in Software Defined Networks

We propose a concept of using Software Defined Network (SDN) technology and machine learning algorithms for monitoring and detection of malicious activities in the SDN data plane. The statistics and features of network traffic are generated by the native mechanisms of SDN technology. In order to conduct tests and a verification of the concept, it was necessary to obtain a set of network workload test data. We present virtual environment which enables generation of the SDN network traffic. The article examines the efficiency of selected  machine learning methods: Self Organizing Maps and Learning Vector Quantization and their enhanced versions. The results are compared with other SDN-based IDS

Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks

Software Defined Networking (SDN) has emerged as a key enabler for future agile Internet architecture. Nevertheless, the flexibility provided by SDN architecture manifests several new design issues in terms of network security. These issues must be addressed in a unified way to strengthen overall network security for future SDN deployments. Consequently, in this paper, we propose a Gated Recurrent Unit Recurrent Neural Network (GRU-RNN) enabled intrusion detection systems for SDNs. The proposed approach is tested using the NSL-KDD dataset, and we achieve an accuracy of 89% with only six raw features. Our experiment results also show that the proposed GRU-RNN does not deteriorate the network performance. Through extensive experiments, we conclude that the proposed approach exhibits a strong potential for intrusion detection in the SDN environments

Improving DDoS Detection Accuracy Using Six-Sigma in SDN Environment

This paper proposes the new method for improving the accuracy of detection of DDoS attacks on the SDN by utilizing control plane using Six-Sigma method. Software-Defined Networking (SDN) is a centralized network control system. This system offers flexibility on receiving, processing and forwarding packets between subnetworks. The centralized system of SDN, which separates control plane and data plan, has an immense number of advantages, but it also has the risk of becoming a single point of network failure. Distributed Denial of Service (DDoS) attack is the major issues faced in the security aspect of SDN. This attack can make network resources unreachable by the real packets. The widely known method has been implemented on SDN for avoiding a DDoS attack is Three-Sigma method. Three-Sigma method uses a threshold value to determine the existence of a DDoS attack. However, this method has drawbacks regarding accuracy in determining the DDoS attack. The main contribution of this paper is utilizing central control plane of SDN for improving accuracy on detecting the DDoS attack. Several experiments performed for proving the concept. The result shows the new method can improve the accuracy of detection of a DDoS attack, either in constant or fluctuating traffic, by reducing the false positive. The performance is about 50% more accurate than the previous method

DeepIDS: Deep Learning Approach for Intrusion Detection in Software Defined Networking

Software Defined Networking (SDN) is developing as a new solution for the development and innovation of the Internet. SDN is expected to be the ideal future for the Internet, since it can provide a controllable, dynamic, and cost-effective network. The emergence of SDN provides a unique opportunity to achieve network security in a more efficient and flexible manner. However, SDN also has original structural vulnerabilities, which are the centralized controller, the control-data interface and the control-application interface. These vulnerabilities can be exploited by intruders to conduct several types of attacks. In this paper, we propose a deep learning (DL) approach for a network intrusion detection system (DeepIDS) in the SDN architecture. Our models are trained and tested with the NSL-KDD dataset and achieved an accuracy of 80.7% and 90% for a Fully Connected Deep Neural Network (DNN) and a Gated Recurrent Neural Network (GRU-RNN), respectively. Through experiments, we confirm that the DL approach has the potential for flow-based anomaly detection in the SDN environment. We also evaluate the performance of our system in terms of throughput, latency, and resource utilization. Our test results show that DeepIDS does not affect the performance of the OpenFlow controller and so is a feasible approach

Implementing an intrusion detection and prevention system using software-defined networking : defending against port-scanning and denial-of-service attacks

Over recent years, we have observed a significant increase in the number and the sophistication of cyber attacks targeting home users, businesses, government organizations and even critical infrastructure. In many cases, it is important to detect attacks at the very early stages, before significant damage can be caused to networks and protected systems, including accessing sensitive data. To this end, cybersecurity researchers and professionals are exploring the use of Software-Defined Networking (SDN) technology for efficient and real-time defense against cyberattacks. SDN enables network control to be logically centralised by decoupling the control plane from the data plane. This feature enables network programmability and has the potential to almost instantly block network traffic when some malicious activity is detected. In this work, we design and implement an Intrusion Detection and Prevention System (IDPS) using SDN. Our IDPS is a software-application that monitors networks and systems for malicious activities or security policy violations and takes steps to mitigate such activity. We specifically focus on defending against port-scanning and Denial of Service (DoS) attacks. However, the proposed design and detection methodology has the potential to be expanded to a wide range of other malicious activities. We have implemented and tested two connection-based techniques as part of the IDPS, namely the Credit-Based Threshold Random Walk (CB-TRW) and Rate Limiting (RL). As a mechanism to defend against port-scanning, we outline and test our Port Bingo (PB) algorithm. Furthermore, we include QoS as a DoS attack mitigation, which relies on flow-statistics from a network switch. We conducted extensive experiments in a purpose-built testbed environment. The experimental results show that the launched port-scanning and DoS attacks can be detected and stopped in real-time. Finally, the rate of false positives can be kept sufficiently low by tuning the threshold parameters of the detection algorithms