Machine Learning Techniques for Intrusion Detection Systems in SDN-Recent Advances, Challenges and Future Directions

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.

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.

Today’s security attacks on computer networks are becoming more complex and severe, which has prompted security researchers to use a variety of machine learning techniques to safeguard the information and reputation of their clients. Detecting network infiltration has long been a difficult task. Machine learning advancements have raised the way for improving intrusion detection systems (IDS). This development has led to intrusion detection’s integration into network security. Using supervised machine learning techniques, intrusion detection has attained great detection accuracy. However, it is unlikely that a machine learning (ML) classifier will be able to correctly identify all attacks, particularly obscure ones.An approach based on deep learning is presented for more precise intrusion detection. This review article presents an extensive survey and classification of deep learning-based intrusion detection techniques with an emphasis on these approaches. The main background ideas about the IDS architecture and several machine and deep learning approaches are initially presented. Then, it categorizes these schemes based on the many types of methodologies each one employs. It explains how accurate intrusion detection is achieved through the use of machine and deep learning networks. The researched IDS frameworks are then fully analysed, with final thoughts and suggested directions for the future underlined.

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.

Cyber-security threats are a growing concern in networked environments. The development of Intrusion Detection Systems (IDSs) is fundamental in order to provide extra level of security. We have developed an unsupervised anomaly-based IDS that uses statistical techniques to conduct the detection process. Despite providing many advantages, anomaly-based IDSs tend to generate a high number of false alarms. Machine Learning (ML) techniques have gained wide interest in tasks of intrusion detection. In this work, Support Vector Machine (SVM) is deemed as an ML technique that could complement the performance of our IDS, providing a second line of detection to reduce the number of false alarms, or as an alternative detection technique. We assess the performance of our IDS against one-class and two-class SVMs, using linear and non- linear forms. The results that we present show that linear two-class SVM generates highly accurate results, and the accuracy of the linear one-class SVM is very comparable, and it does not need training datasets associated with malicious data. Similarly, the results evidence that our IDS could benefit from the use of ML techniques to increase its accuracy when analysing datasets comprising of non- homogeneous features.

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

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.

Attack-aware Security Function Management

The implementation of Software-Defined Network(SDN) across multiple data centers aims to simplify thecontrol and management of networks. However, the increasingpopularity of SDN has also attracted the attention of attackers.To tackle this problem, it is essential to have an intrusiondetection system (IDS) in place, which plays a crucial role incybersecurity by addressing external threats. The advantageof SDN’s centralized nature is that it facilitates the training ofan IDS based on machine learning. However, there is a scarcityof research specifically focused on intrusion detection in SDN.Existing literature often treats SDN intrusion detection assimilar to traditional computer systems and relies on intrusiondatasets generated for those systems. We explore the issueof intrusion detection in SDN using the most recent publicdataset (InSDN). However, InSDN is an imbalanced data set.In this paper, we have recommended a method to balance thedata as well as a method to find the best features to improvethe quality of IDS using Machine Learning. In addition, wealso suggest a method of classifying SDN network traffic andnormal network traffic. At the same time, we also evaluate theefficiency of the SDN system with the load balancing systemand without the load balancing system.

Data security is regarded to be one of the crucial challenges in this fast-growing internet world. Data generated through internet is exposed to various types of vulnerabilities and exploits. Security mechanisms such as Intrusion Detection System (IDS) are designed to detect various types of vulnerabilities and attacks. Various Machine Learning (ML) and Deep Learning (DL) techniques are used for building IDS. In this paper, we aim to build Deep Neural Network (DNN)-based IDS for attack detection and classification. DNN technique has certain challenges such as complex network structure, co-adaptation of feature vectors, over-fitting, to name a few. We aim to address these challenges by using AntiRectifier layer and variants of dropout namely, Standard dropout, Gaussian dropout, and Gaussian Noise. In this paper, we have evaluated DNN-based IDS using NSL-KDD, UNSW_NB-15 and CIC-IDS-2017 dataset. The experimental results show that DNN-based IDS with AntiRectifier layer outperforms compared to ML techniques such as Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), k-Nearest Neighbours (k-NN) and variants of dropout namely, Standard dropout, Gaussian dropout, and Gaussian noise in terms of accuracy, precision, recall, f-score, and false positive rate.

We are living in a connected world which encompasses various types of devices and networks. The most widely used network in modern age is Internet of Things (IoT). It is a type of network where IOT devices can communicate with each other without human intervention. The life of people is transformed with the evolution of smart city, smart home concepts. The areas are not limited. It ranges from healthcare, medical, transportation to banking, smart grid, agriculture. It has transformed the lives of people by providing ease and comfort of completing the tasks in less time with less overhead. However, there is a negative side of IoT and that is it lacks the implementation of sound and effective security measures. There are various risks associated with use of IoT. Some of them are risk of unauthorized access of data, node spoofing and different cyber-attacks like denial of service (DoS), Botnet, Ransom ware, eavesdropping, zero day attack. So, the main concern addressed here is how to detect intrusions into the network using Machine Learning. Machine Learning (ML) plays an important role in designing of intrusion detection systems to detect the anomalies in the network. The ML techniques are very efficient in detection of different types of cyber-attacks. This paper presents the review of different ML techniques in detection of intrusions into IoT networks. Different ML techniques like Random Forest, Neural Network, support vector machine etc. are useful in detection of anomalies in the network. Different datasets like UNSW-NB 15, CICIDS-2017, KDD-99, NSL-KDD, TON_IoT and ECU-IOHT are available which consists of data related to different types of cyber-attacks and are used to form intrusion detection systems. This paper presents the application of different ML algorithms in detection of abnormalities in the network, comparative analysis of different feature selection and machine learning techniques along with challenges and issues present in traditional intrusion detection systems(IDS) in IOT networks.

&lt;span lang="EN-US"&gt;The rapid development of information and communication technologies has increased the demand for internet-facing devices that require publicly accessible internet protocol (IP) addresses, resulting in the depletion of internet protocol version 4 (IPv4) address space. As a result, internet protocol version 6 (IPv6) was designed to address this issue. However, IPv6 is still not widely used because of security concerns. An intrusion detection system (IDS) is one example of a security mechanism used to secure networks. Lately, the use of machine learning (ML) or deep learning (DL) detection models in IDSs is gaining popularity due to their ability to detect threats on IPv6 networks accurately. However, there is an apparent lack of studies that review ML and DL in IDS. Even the existing reviews of ML and DL fail to compare those techniques. Thus, this paper comprehensively elucidates ML and DL techniques and IPv6-based distributed denial of service (DDoS) attacks. Additionally, this paper includes a qualitative comparison with other related works. Moreover, this work also thoroughly reviews the existing ML and DL-based IDSs for detecting IPv6 and IPv4 attacks. Lastly, researchers could use this review as a guide in the future to improve their work on DL and ML-based IDS.&lt;/span&gt;

Machine learning (ML) and deep learning (DL) are used in numerous fields, particularly to develop effective intrusion detection systems (IDS). Existing wireless network IDS, which rely on a single ML algorithm and have limitations. These include a high rate of false positives, difficulties in recognizing distinct attack patterns, and a high acquisition cost for annotated training datasets. However, hostile threats are always evolving, networks need a smart security solution. In comparison to other ML approaches, DL algorithms are more successful in intrusion detection. This paper presents a DL based ensemble model that combines Multi-verse through Chaotic Atom Search Optimization (MCA) for preprocessing, which eliminates unsolicited/recurrent information in the dataset. The process of optimized feature selection uses Principal Component Analysis (PCA), Chaotic Manta-ray Foraging Optimizations (CMFO), and a grounded grouping method to partition the optimized feature dataset into k-diverse clusters. The recommended model then stacks Support Vector Machine (SVM) as the ensemble model’s meta-learner classifier, pre-training the hybrid DL prototypes using the optimized feature dataset cluster. The CNN-LSTM and CNN-GRU models, which integrate Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), are the hybrid DL prototype’s key components. The suggested model’s performance has been enhanced and compared to six ML techniques: NB, SVM, J48, RF, MLP, and kNN models, utilizing measures such as accuracy, precision, recall, and F-measure. The public can access the Aegean Wi-Fi Intrusion Dataset (AWID) which is used for evaluating the recommended model and is outperformed the contemporary models in the literature.

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

A flexible SDN-based framework for slow-rate DDoS attack mitigation by using deep reinforcement learning