Network Traffic Feature Behaviour Augmentation Fusion based Attacks Classification for Intrusion Detection System in SDN Framework

An intrusion detection system (IDS) is an essential component of any modern network. The purpose of an IDS is to detect intrusion and generate appropriate alarms so that the intrusion can be mitigated. Implementing an IDS in a Software Defined Network (SDN) is easier since an SDN controller has a centralized view of the whole network. Researchers have made many efforts to use machine learning (ML) for developing network-based IDS in SDN. The network-based IDS analyzes different characteristics of incoming network traffic to detect intrusion. Early detection of intrusion is crucial for an IDS because if the intrusion is not detected quickly enough, it can cause severe damage, such as data breaches and service shutdowns. This paper focuses on detecting intrusion in SDN as early as possible using real-time flow-based features. Our aim is to detect intrusion with less amount of packets per flow, which not only facilitates early intrusion detection but also is useful when an intrusion flow has less number of packets. We show that although ML models perform well in offline training on a dataset, their performance decreases ~25% when fewer packets are used to generate features for the ML model. In all our experiments, a simple Random Forest (RF) algorithm outperforms a complex deep learning model on a publicly available dataset for intrusion detection in SDN.

The Internet has reached a breaking point, enabling ubiquitous accessibility to all digital demands. Though people have accelerated their use of overall digital demands due to the convenience of the Internet, where nearly everything is connected and accessible, it is becoming increasingly difficult to manage and modify networks. It is always a challenge to meet the emerging needs of network industry. SDN framework was proposed to overcome traditional network architecture (TNA) restrictions in order to cope with contemporary network management in terms of flexibility, agility, responding to faults, and dynamic changes. SDN assists network operators by uncoupling the control plane from its underlying ternary content-addressable memory (TCAM) hardware often called as forwarding plane by allowing the networks to programme as per the anticipated network functionality. Now, switching from traditional to software-defined networking styles, it is worth to check the robustness of the open flow (south bound interface)-based controller platform and the capability of its fault tolerance attribute to approach the successful SDN implementation. The performance of SDN open flow controllers plays a vital role in SDN implementation, which is meant for the separation between control and data plane to leverage the life of network industry. This paper comprehensively surveys the contrast between traditional network architecture (TNA) and software-defined network architecture. This research work discusses about the best feasible SDN implementation, as well as the selection of a useful controller platform for overcoming traditional network constraints and the performance assessment criteria for SDN open flow controllers. Further, the survey is continued with possible literature or work done by various researchers on performance analysis and evaluation of open flow-based SDN controllers as well as in a group of two or more controllers with respect to multifarious network condition, network criteria/parameter/metric, scaling of network load resources with most important varying network topology design. Ultimately, it strives to point ongoing research omission with respect to controller’s performance bottleneck benchmark suggesting optimized solution when it is well experimented.KeywordsTraditional network architecture (TNA)Software-defined networking (SDN)SDN open flow controllersOpen flow protocolPerformance evaluation/analysis

The evolution from conventional network frameworks to Software-Defined Networks (SDNs) marks a pivotal shift in network management. SDNs offer a unique architecture where a centralized controller orchestrates network traffic, separating the control plane from the data plane. This architecture not only brings enhanced flexibility, agility, and scalability to network operations but also introduces specific security challenges, primarily due to the centralization of control, which can be a potential Single Point of Failures (SPOFs). Addressing these security concerns, this paper introduces an innovative machine learning-based Intrusion Detection System (IDS) tailored for SDNs, by proposing a three-stage CatBoost Classifier (3CC). This classifier is specifically designed for efficient and effective intrusion and vulnerability detection within SDN environments. The multistage aspect of the classifier allows for refined and detailed analysis, catering to a diverse array of attack vectors. Another key feature of our approach is the utilization of real-world data from SDN environments, which ensures that our model is tested and validated under realistic and current conditions. Furthermore, the integration of GPU acceleration significantly enhances the training speed of our model, a critical factor given the voluminous nature of network data. The proposed model, 3CC, demonstrates remarkable performance in our evaluations, achieving an accuracy of 99.8966%. This high level of precision in detecting and identifying various network intrusions and vulnerabilities underlines the efficacy of our approach. Our paper contributes significantly to the field of SDN security, offering a sophisticated, multistage classification solution that leverages the latest in machine learning techniques and real-world applicability.

Attack-aware Security Function Management

Software-defined networking (SDN) is an innovative network technology. It changed the world of computer networking by providing solutions to many challenges. SDN provides programmability, easy and centralized network management, dynamic configuration, and improved security. Although SDN offers remarkable benefits but it provides centralized network management which is prone to attacks. So, intrusion detection systems (IDS) are essential to detect and prevent security attacks in SDN. Traditional IDS follow a centralized machine learning approach which causes vulnerabilities in IDS. Old-style IDS lack data privacy preservation, and solution for training data unavailability due to privacy. Federated learning (FL) is a distributed machine learning approach which provides a collaborative training approach without data sharing. In FL, training is performed on multiple nodes creating a global model without sharing the data. To address challenges and the limitations of traditional IDS, we proposed a FL based multi class classification IDS for SDN. FL delivers an efficient and scalable solution to address challenges of traditional IDS. The proposed model enhances security of SDN by not requiring the centralization of data. To test the impact and efficiency of proposed model, we used a latest and realistic cybersecurity dataset. We also compared the proposed model with state of art existing multi class classification studies. The results and their comparison with existing studies highlight the potential of proposed model to enhance network security while providing a privacypreserving learning environment for intrusion detection.

Software-Defined Networking (SDN) enables flexibility in developing security tools that can effectively and efficiently analyze and detect malicious network traffic for detecting intrusions. Recently Machine Learning (ML) techniques have attracted lots of attention from researchers and industry for developing intrusion detection systems (IDSs) considering logically centralized control and global view of the network provided by SDN. Many IDSs have developed using advances in machine learning and deep learning. This study presents a comprehensive review of recent work of ML-based IDS in context to SDN. It presents a comprehensive study of the existing review papers in the field. It is followed by introducing intrusion detection, ML techniques and their types. Specifically, we present a systematic study of recent works, discuss ongoing research challenges for effective implementation of ML-based intrusion detection in SDN, and promising future works in this field.

Distributed denial-of-service (DDoS) attacks continue to grow at a rapid rate plaguing Internet Service Providers (ISPs) and individuals in a stealthy way. Thus, intrusion detection systems (IDSs) must evolve to cope with these increasingly sophisticated and challenging security threats. Traditional IDSs are prone to zero-day attacks since they are usually signature-based detection systems. The recent advent of machine learning and deep learning (ML/DL) techniques can help strengthen these IDSs. However, the lack of up-to-date labeled training datasets makes these ML/DL based IDSs inefficient. The privacy nature of these datasets and widespread emergence of adversarial attacks make it difficult for major organizations to share their sensitive data. Federated Learning (FL) is gaining momentum from both academia and industry as a new sub-field of ML that aims to train a global statistical model across multiple distributed users, referred to as collaborators, without sharing their private data. Due to its privacy-preserving nature, FL has the potential to enable privacy-aware learning between a large number of collaborators. This paper presents a novel framework, called MiTFed, that allows multiple software defined networks (SDN) domains ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$i.e.,$</tex-math></inline-formula> collaborators) to collaboratively build a global intrusion detection model without sharing their sensitive datasets. In particular, MiTFed consists of: (1) a novel distributed architecture that allows multiple SDN based domains to securely collaborate in order to cope with sophisticated security threats while preserving the privacy of each SDN domain; (2) a novel Secure Multiparty Computation (SMPC) scheme to securely aggregate local model updates; and (3) a blockchain based scheme that uses Ethereum smart contracts to maintain the collaboration in a fully decentralized, trustworthy, flexible, and efficient manner. To the best of our knowledge, MiTFed is the first framework that leverages FL, blockchain and SDN technologies to mitigate the new emerging security threats in large scale. To evaluate MiTFed, we conduct several experiments using real-world network attacks; the experimental results using the well-known public network security dataset NSL-KDD show that MiTFed achieves efficiency and high accuracy in detecting the new emerging security threats in both binary and multi-class classification while preserving the privacy of collaborators, making it a promising framework to cope with the new emerging security threats in SDN.

Although Software-defined networking (SDN) is a promising architecture that simplifies network management and control, it also faces security problems that may affect the whole network. Hence, protecting strategies, such as intrusion detection and prevention system (IDPS), are in need in the SDN context. The potential of machine learning-based solutions can become the motivation of cut-edge deep learning-based intrusion detection system that can leverage the centralized control and view of the controller to secure the underlying infrastructure. However, performing additional IDPS functions in the controller, which needs to process enormous traffic amounts, can overload this component, and slow down the network. This paper introduces an approach of Big Data analysis for intrusion detection system in SDN, named BIDSDN to enhance the classification perfor-mance with a massive amount of network traffic data. Specifically, we leverage Apache Spark to deploy the distributed deep learning – based detector to reduce the processing time on complex algorithms. The experiments conducted on CICIDS2018 dataset with distributed cluster prove the efficacy in tackling the Big Data-related issues in the large-scale network like SDN.

The philosophy of the IoT world is becoming important for a projected, always-connected world. The 5G networks will significantly improve the value of 4G networks in the day-to-day world, making them fundamental to the next-generation IoT device networks. This article presents the current advances in the improvement of the standards, which simulate 5G networks. This article evaluates the experience that the authors gained when implementing Vodafone Romania 5G network services, illustrates the experience gained in context by analyzing relevant peer-to-peer work and used technologies, and outlines the relevant research areas and challenges that are likely to affect the design and implementation of large 5G data networks. This paper presents a machine learning-based real-time intrusion detection system with the corresponding intrusion prevention system. The convolutional neural network (CNN) is used to train the model. The system was evaluated in the context of the 5G data network. The smart intrusion detection system (IDS) takes the creation of software-defined networks into account. It uses models based on artificial intelligence. The system is capable to reveal not previously detected intrusions using software components based on machine learning, using the convolutional neural network. The intrusion prevention system (IPS) blocks the malicious traffic. This system was evaluated, and the results confirmed that it provides higher efficiencies compared to less overhead-like approaches, allowing for real-time deployment in 5G networks. The offered system can be used for symmetric and asymmetric communication scenarios.

The increasing complexity and connectivity of networks lead to new and challenging vulnerabilities to be addressed for network security. As networks expand and diverse networks become interrelated, attackers leverage the broad scope of the global Internet to perpetrate large-scale attacks. Cloud environments, which are widely adopted, are fundamentally provisioned via the Internet bringing new challenges regarding security and privacy. However, network programmability and emerging technologies such as software-defined networking (SDN) and network functions virtualization (NFV) can advance techniques for intrusion detection and protection. In this chapter, we review the state of the art in scalable and collaborative intrusion detection systems (IDSs). Combining data plane programming with control level collaboration, we propose a model to simplify the detection of large-scale, distributed network attacks. This model simultaneously reduces the system overhead through direct mitigation at the network edge and enriches the detection process with corroboration of evidence from distributed sources.

In recent years, SDN has evolved as a promising alternative to the traditional networking paradigm. This is because the traditional network is decentralized, making it difficult to manage and provide trouble-shooting. SDN is an approach to network management whereby the forwarding process of the data plane (DP) is disassociated from the control plane (CP). The CP is responsible for decision-making. SDN centrally control the network infrastructure and make it programmable. Communication between the two planes (data and control) is carried out by the OpenFlow (OF) protocol. The deployment of SDN was initiated by Mr. Matrin Casado and his research team in 2008. The original projects developed at Stanford University were SANE and ETHANE. Security reports and web articles suggest that SDN (Software Defined Network) has the caliber to defend DDoS attacks. The year 2017 has been declared as the year of SDN adaption and DDoS mitigation. The initial effort in the direction of compiling the detection and mitigation approaches has been done in (Dayal et al., 2016). Recently a similar effort has been done in (Rochak et al., 2019). The CP of SDN has a complete visibility of the network which makes traffic monitoring feasible. Moreover, SDN open flow switches contain forwarding logic only; decision-making capability is softwarized at 58controller. This helps the controller to instruct and configure the switches with new flow rules which the switches follow. This facilitates the network admins to detect and mitigate these attacks easily. SDN have gained momentum among the possible solutions of DDoS attacks due to its decentralized nature of network management, but solutions come with its repercussions. The centralization and open architecture framework makes SDN difficult for widespread adaptation. The chapter begins with an introduction to DDoS attacks and its classification such as volumetric attack, protocol exploitation attack and application attack. In the next section, we discuss the possible DDoS attacks over SDN framework. We have categorized the vulnerability into three classes: DDoS attack on data, control, and application planes (APs). The DDoS attack on DP can further be classified into TCAM exhaustion, switch DDoS, ICMP flood, TCP flood, TCP_SYN flood, etc. We would discuss Resource depletion, OF bandwidth exhaustion, amplification attacks in DDoS attack over CP. HTTP flooding and Slowloris attack has been discussed in DDoS attack over AP. The next section highlights the working of attack tools used in performing DDoS attacks. Various DDoS tools have been widely exploited to render DDoS attacks and facilitate attackers to carry out dangerous attack on target; here target may be network resources, server resources, or applications. The fourth section focuses on the state-of-the-art for detection and mitigation approaches of DDoS attacks. Detection approaches are classified into statistical, policy, and machine learning-based approaches. DDoS mitigation OF switches, including Alcatel Lucent’s OmniSwitch, Brocade’s MLXe series routers, and applications such as DefensePro by Radware that are available in market; also depend on the statistical threshold to detect the DDoS attack. In addition, this section also incorporates a brief analysis of the previous defense mechanism of the last 10 years and analyzes and compares the products of different security vendors like Cloudflare, Radware, Akamai, Arbor, Nexusguard, etc.

Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey

The development of information technology and the digitalization of higher education demand a more efficient, flexible, and secure network infrastructure. Software Defined Networking (SDN) offers a new paradigm in network management by separating the control plane and data plane, enabling more centralized and adaptive network management. This study aims to analyze the implementation of SDN in campus networks, specifically at Ichsan Sidenreng Rappang University, and identify its benefits, challenges, and implementation strategies. The method used is a systematic literature review of 15 scientific publications from 2020-2025 that discuss the implementation of SDN in the context of campus networks. The results show that SDN provides significant network performance improvements, with a decrease in latency from 37.7 ms to 18 ms, jitter from 40.3 ms to 2.7 ms, and an increase in throughput from 95 Mbps to 98.2 Mbps compared to conventional networks. SDN also increases flexibility through centralized management and automation, strengthens security with adaptive firewall integration, and improves service redundancy and availability. Key implementation challenges include the need for human resource training, the development of comprehensive security policies, and testing on more complex topologies. This study concludes that implementing SDN on campus networks is strategic for supporting the digitalization of higher education, with recommendations for thorough planning, ongoing human resource training, and the development of policies that holistically integrate technical and security aspects.

The complexity of handling modern apps is causing older network architectures to struggle. As a result, Software Defined Networking (SDN) has developed into a paradigm-shifting network framework that enables administrators to exercise programmable control over whole network infrastructures. This development makes it easier to configure and operate large networks, but it also adds vulnerabilities, including the possibility of Distributed Denial of Service (DDoS) assaults that could take down the SDN controller. Integrating security tools like firewalls, intrusion detection and prevention systems, and anti-DDoS solutions is crucial to reducing this danger. In the context of SDN, leveraging the analytical power of Machine Learning (ML) algorithms to analyse network traffic patterns is a key tactic for fending off DDoS attacks. Effective defence systems are supported by these ML models, which can distinguish between normal network behaviour and DDoS assault traffic with ease. Decision trees (DT), support vector machines (SVM), and artificial neural networks (ANN) are examples of supervised ML techniques that have evolved into crucial tools in this field. These algorithms are developed utilising datasets with annotations that include examples of both legitimate and malicious network traffic. Once trained, the models may be used to instantly categorise incoming traffic, making it easier to identify and contain any DDoS attacks in real-time.

Nature-inspired intrusion detection system for protecting software-defined networks controller