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

High-performance, Platform-Independent DDoS Detection for IoT Ecosystems

Most Distributed Denial of Service (DDoS) detection and mitigation strategies for Internet of Things (IoT) are based on a remote cloud server or purpose-built middlebox executing complex intrusion detection methods, that impose stringent scalability and performance requirements on the IoT due to the vast amounts of traffic and devices to be handled. In this paper, we present an edge-based detection scheme using BPFabric, a high-speed, programmable data-plane switch architecture, and lightweight network functions to execute upstream anomaly detection. The proposed detection scheme ensures fast detection of DDoS attacks originated from IoT devices, while guaranteeing minimum resource usage and processing overhead. Our solution was compared against two widespread coarse-grained detection techniques, showing detection delays under 5ms, an overall accuracy of 93 − 95% and a bandwidth overhead of less than 1%

Entropy based features distribution for anti-ddos model in SDN

In modern network infrastructure, Distributed Denial of Service (DDoS) attacks are considered as severe network security threats. For conventional network security tools it is extremely difficult to distinguish between the higher traffic volume of a DDoS attack and large number of legitimate users accessing a targeted network service or a resource. Although these attacks have been widely studied, there are few works which collect and analyse truly representative characteristics of DDoS traffic. The current research mostly focuses on DDoS detection and mitigation with predefined DDoS data-sets which are often hard to generalise for various network services and legitimate users’ traffic patterns. In order to deal with considerably large DDoS traffic flow in a Software Defined Networking (SDN), in this work we proposed a fast and an effective entropy-based DDoS detection. We deployed generalised entropy calculation by combining Shannon and Renyi entropy to identify distributed features of DDoS traffic—it also helped SDN controller to effectively deal with heavy malicious traffic. To lower down the network traffic overhead, we collected data-plane traffic with signature-based Snort detection. We then analysed the collected traffic for entropy-based features to improve the detection accuracy of deep learning models: Stacked Auto Encoder (SAE) and Convolutional Neural Network (CNN). This work also investigated the trade-off between SAE and CNN classifiers by using accuracy and false-positive results. Quantitative results demonstrated SAE achieved relatively higher detection accuracy of 94% with only 6% of false-positive alerts, whereas the CNN classifier achieved an average accuracy of 93%

Evaluation of machine learning techniques for intrusion detection in software defined networking

Abstract. The widespread growth of the Internet paved the way for the need of a new network architecture which was filled by Software Defined Networking (SDN). SDN separated the control and data planes to overcome the challenges that came along with the rapid growth and complexity of the network architecture. However, centralizing the new architecture also introduced new security challenges and created the demand for stronger security measures. The focus is on the Intrusion Detection System (IDS) for a Distributed Denial of Service (DDoS) attack which is a serious threat to the network system. There are several ways of detecting an attack and with the rapid growth of machine learning (ML) and artificial intelligence, the study evaluates several ML algorithms for detecting DDoS attacks on the system. Several factors have an effect on the performance of ML based IDS in SDN. Feature selection, training dataset, and implementation of the classifying models are some of the important factors. The balance between usage of resources and the performance of the implemented model is important. The model implemented in the thesis uses a dataset created from the traffic flow within the system and models being used are Support Vector Machine (SVM), Naive-Bayes, Decision Tree and Logistic Regression. The accuracy of the models has been over 95% apart from Logistic Regression which has 90% accuracy. The ML based algorithm has been more accurate than the non-ML based algorithm. It learns from different features of the traffic flow to differentiate between normal traffic and attack traffic. Most of the previously implemented ML based IDS are based on public datasets. Using a dataset created from the flow of the experimental environment allows training of the model from a real-time dataset. However, the experiment only detects the traffic and does not take any action. However, these promising results can be used for further development of the model

Detection and Mitigation of DoS and DDoS Attacks in IoT-Based Stateful SDN: An Experimental Approach

The expected advent of the Internet of Things (IoT) has triggered a large demand of embedded devices, which envisions the autonomous interaction of sensors and actuators while offering all sort of smart services. However, these IoT devices are limited in computation, storage, and network capacity, which makes them easy to hack and compromise. To achieve secure development of IoT, it is necessary to engineer scalable security solutions optimized for the IoT ecosystem. To this end, Software Defined Networking (SDN) is a promising paradigm that serves as a pillar in the fifth generation of mobile systems (5G) that could help to detect and mitigate Denial of Service (DoS) and Distributed DoS (DDoS) threats. In this work, we propose to experimentally evaluate an entropy-based solution to detect and mitigate DoS and DDoS attacks in IoT scenarios using a stateful SDN data plane. The obtained results demonstrate for the first time the effectiveness of this technique targeting real IoT data traffic.This research was funded by EU, European Regional Development Fund, and the regional government of Extremadura, Spain, grant number IB18003, the Spanish Ministry of Science, Innovation and Universities grant numbers TIN2016-75097-P, RTI2018-102002-A-I00, PEJ2018-003648-A and FEDER and Junta de Andalucía grant number B-TIC-402-UGR18

Encountering distributed denial of service attack utilizing federated software defined network

This research defines the distributed denial of service (DDoS) problem in software-defined-networks (SDN) environments. The proposes solution uses Software defined networks capabilities to reduce risk, introduces a collaborative, distributed defense mechanism rather than server-side filtration. Our proposed network detection and prevention agent (NDPA) algorithm negotiates the maximum amount of traffic allowed to be passed to server by reconfiguring network switches and routers to reduce the ports' throughput of the network devices by the specified limit ratio. When the passed traffic is back to normal, NDPA starts network recovery to normal throughput levels, increasing ports' throughput by adding back the limit ratio gradually each time cycle. The simulation results showed that the proposed algorithms successfully detected and prevented a DDoS attack from overwhelming the targeted server. The server was able to coordinate its operations with the SDN controllers through a communication mechanism created specifically for this purpose. The system was also able to determine when the attack was over and utilize traffic engineering to improve the quality of service (QoS). The solution was designed with a sophisticated way and high level of separation of duties between components so it would not be affected by the design aspect of the network architecture

Q-learning based distributed denial of service detection

Distributed denial of service (DDoS) attacks the target service providers by sending a huge amount of traffic to prevent legitimate users from getting the service. These attacks become more challenging in the software-defined network paradigm, due to the separation of the control plane from the data plane. Centralized software defined networks are more vulnerable to DDoS attacks that may cause the failure of all networks. In this work, a new approach is proposed based on q-learning to enhance the detection of DDoS attacks and reduce false positives and false negatives. The results of this work are compared with entropy detection in terms of the number of received packets to detect the attack and also the continuity of service for legitimate users. Moreover, these results indicate that the proposed system detects the DDoS attack from flash crowds and redirects the traffic to the edge of the data center. A second controller is used to redirect traffic to a honeypot server that works as a mirror server. This guarantees the continuity of service for both normal and suspected traffic until further analysis is done. The results indicate an increase of up to 50% in the throughput compared to other approaches

Preemptive modelling towards classifying vulnerability of DDoS attack in SDN environment

Software-Defined Networking (SDN) has become an essential networking concept towards escalating the networking capabilities that are highly demanded future internet system, which is immensely distributed in nature. Owing to the novel concept in the field of network, it is still shrouded with security problems. It is also found that the Distributed Denial-of-Service (DDoS) attack is one of the prominent problems in the SDN environment. After reviewing existing research solutions towards resisting DDoS attack in SDN, it is found that still there are many open-end issues. Therefore, these issues are identified and are addressed in this paper in the form of a preemptive model of security. Different from existing approaches, this model is capable of identifying any malicious activity that leads to a DDoS attack by performing a correct classification of attack strategy using a machine learning approach. The paper also discusses the applicability of best classifiers using machine learning that is effective against DDoS attack