SDN-based Internet Of Things Research Articles

A new intelligent scheduler to improve reactive OpenFlow communication in SDN-based IoT data streams

The significant adoption of the Internet of Things (IoT) has increased the challenges in providing adequate IoT infrastructures meeting essential requirements, such as dynamicity networks and low latency. In this context, the Software-Defined Networking (SDN) paradigm and the OpenFlow protocol provide new possibilities for IoT networks. Based on the global view of network elements enabled by the Controller, SDN allows the programmability and control of the infrastructure according to the actual demands of applications. The OpenFlow protocol defines the exchange of messages between controllers and switches, enabling communication and network control. OpenFlow implements three operation modes: proactive, reactive, and hybrid. Due to the dynamic characteristic of the IoT data stream, the reactive mode is mainly used and indicated for IoT environments. As the OpenFlow controller installs rules dynamically, there is no need to know the network’s sources, destinations, and paths in advance. Although reactive mode introduces dynamicity, it can generate additional delay due to switch-controller communication. This delay increases the response time of the IoT data stream. We propose an SDN-IoT scheduler based on Deep Neural Networks (DNN) to predict the time between data stream changes from IoT devices and install rules in advance, suppressing the existing delay in reactive mode. The proposal automatically uses previous data from the IoT data stream to calculate the time of the following communication from IoT devices. Our results indicate that predicting IoT data stream changes and installing OpenFlow rules in advance reduced about 51% of communications response time.

A comprehensive survey on DDoS attacks detection & mitigation in SDN-IoT network

The Internet of Things (IoT) has transformed our lives by introducing new services and enhancing productivity. However, the widespread adoption of IoT devices and communication units has posed challenges in network management. In response, there is a growing necessity to rethink and redesign IoT network control. Software-defined networking (SDN) has emerged as a promising solution, leveraging its programmability and centralized management capabilities. SDN can simplify network management, offer network abstraction, facilitate development, and efficiently handle the complexities of IoT networks. Despite these advantages, security concerns, particularly the threat of Distributed Denial of Service (DDoS) attacks, persist in the IoT landscape. This survey focuses on exploring the collaboration between SDN and IoT. It investigates various types of DDoS attacks and highlights different types of defense, detection, and mitigation methods employed to address DDoS threats in SDN-based IoT (SDN-IoT) networks.

An Optimized Routing Scheme in SDN-Based IoT Using the Lion Optimization Algorithm

The Internet of Things (IoT) speaks to the current and future state of the Internet. Numerous things (objects) related to the Internet generate a large amount of information that requires a ton of labor and prepare tasks to move to valuable data. In addition, for the association and control of this enormous information, the original ideas in the project and the executives of the IoT system need to expedite and improve its presentation. A software-defined networking (SDN) system is another worldview that has recently emerged to cover all the complexities in the traditional system architecture by summarizing all the controls and management functions from the basic devices (things in IoT). The open-flow protocol is used to make messages between switches and controllers. It is a correspondence conference, through which the controller integrates the path of the network pocket with switches. Therefore, data packets should be directed to the optimum path to improve system operation. The best or optimal path for packet routing in the SDN data plane is found using the Lion Optimization Algorithm (LOA), which is proposed in this study. Response times and packet loss rates can both be decreased by choosing the best route between the hosts. The suggested LOA outperforms current optimal path detection methods in terms of performance, delay, packet delivery rate, performance, and pocket loss rate, according to simulation findings.

Predictive mobility and cost-aware flow placement in SDN-based IoT networks: a Q-learning approach

Software-Defined Networking (SDN) has emerged as an innovative networking method that offers effective management and remarkable flexibility. However, current SDN-based solutions primarily focus on static networks or concentrate on backbone networks, where network dynamics have minimal impact. The existing methods for placing flow entries in Software-Defined Networking (SDN)-based Internet of Things (IoT) systems exhibit shortcomings in accurately predicting outcomes and efficiently reducing table misses and associated performance metrics. This research introduces a new approach, specifically the mobility-aware adaptive flow entry placement scheme for SDN-based Internet of Things (IoT) environments, to address the mobility aspect of networks. The proposed scheme utilizes the Q-learning algorithm to predict the next possible location of end devices, while the cost-sensitive AdaBoost algorithm is employed to select heavy and active flows. As a result, efficient flow rules for incoming flows can be dynamically implemented without controller intervention. Extensive computer simulations demonstrate that this approach significantly enhances match probability and prediction accuracy while concurrently reducing the number of table misses and resource expenditure compared to existing schemes.

Privacy-Preserving and Security in SDN-Based IoT: A Survey

In recent years, the use of Software Defined Networking (SDN) has increased due to various network management requirements. Using SDN in computer network applications has brought several benefits to users, including lower operational costs, better hardware management, flexibility, and centralized network deployment. On the other hand, the Internet of Things (IoT) is another rapidly growing technology. Distributed and dynamic infrastructures are two critical characteristics of IoT. These characteristics lead to some challenges while using SDN in IoT in terms of security and privacy. In this paper, we address security and privacy issues and solutions for SDN-based IoT systems. We analyze the techniques used for defense in previous works to achieve an acceptable level of security and privacy protection in SDN-based IoT systems. In the data plane, SDN-based IoT papers have considered hashing and encryption techniques, in the control plane, certificate authority and access control have been analyzed, and in the application plane, attack detection, and authentication have been discussed. We also provide a statistical analysis of the existing work. This analysis shows that researchers have focused on certain areas more than others in recent years. The final analysis also highlights issues that previous researchers have ignored.

Countermeasure SDN-based IoT threats using blockchain multicontroller

The Internet of Things (IoT) is making significant progress in various fields; software-defined networks with multiple controllers have become popular because they make it easier to manage large networks. But they are open to several attacks, which makes controller topologies inconsistent. To solve this problem, we suggest a multi-controller blockchain for software-defined networking (SDN) network. This security architecture combines blockchain and multi-controller SDN and divides the network into several domains. We put forth a blockchain-based solution. This paper proposed a model blockchain-enabled SDN multi-controller architecture for IoT networks that uses a clustering algorithm and a new routing protocol that is both secure and energy-efficient. Experimental results indicate that the cluster-based routing protocol has a greater capacity, a shorter response time, and a lower overall power requirement than other protocols. It has been shown that our proposed architecture outperforms the classic blockchain.

An adaptive data coding scheme for energy consumption reduction in SDN-based Internet of Things

In recent decades, most internet-connected devices were personal computers. However, everything is connecting to the Internet over time. Today, the Internet of Things (IoT) is expanding rapidly, including everything from clothing to agricultural equipment. The Internet of Everything (IoE) is very complex and heterogeneous due to the independent development of its various sections. Hence, the coordination and management of its different equipment are challenging, and the energy consumption efficiency is very low, which is the bottleneck of battery-operated IoT equipment. So far, various methods have been proposed to reduce energy consumption in IoT devices. However, these methods are used independently and individually on a single IoT node, and none of them consider all objects in the IoT hierarchy together. Whereas the IoT network arises when various objects interact with each other. Therefore, focusing on energy consumption in a single node should be transformed into energy management at the whole network administrative level. In this paper, considering the IoT network features and the data exchange all over the network, a data manipulation method is proposed to reduce energy consumption and network traffic by decreasing the amount of data exchanged. Furthermore, we take advantage of Software-Defined Networking (SDN) to enhance the efficiency of this method by adapting our energy management decisions to the environmental conditions’ dynamicity. Simulation results using real data workloads demonstrate that the proposed method decreases energy consumption by up to 38.1% without an SDN. This reduction is significantly improved by up to 80.0% when utilizing SDN. We also experimentally evaluate the proposed method by its hardware implementation in the real environment and illustrate that a maximum of only 1.3% difference is observed between the simulation results and the experiments, which indicates the consistency of the results.

A Review of Ddos Attack Detection in Iot Networks

The security risks and vulnerabilities associated with these resource-constrained Internet of Things (IoT) devices grow as their usability rises. Distributed Denial of Service is one of the main dangers to Internet of Things devices (DDoS). Continuous monitoring, early detection, and adaptive decision-making are necessary for IoT device security to be robust and effective. With software-defined networking (SDN), these issues can be solved, giving IoT devices the chance to manage DDoS threats in an efficient manner. This study suggests a unique SDN-based secure IoT framework that uses IP Payload Analysis and session IP counters to identify IoT device vulnerabilities and malicious traffic sent by IoT devices. The proposed methods can readily identify the DDoS attack in the SD-IoT network by analyzing several metrics, even with high traffic volumes, thanks to the DDoS attack detection module of the framework. By creating a lot of traffic from a compromised node, which is later identified and alerted, these tactics are tested on an SDN controller. The results and comparison analysis show that the suggested framework effectively and accurately detects DDoS attacks in their early stages, with a detection rate ranging from 98% to 100% and a low false-positive rate. Keywords; IoT networks, DDoS, DDoS attacks, DDoS Detection and IoT security

TD-RA policy-enforcement framework for an SDN-based IoT architecture

Internet of Things (IoT) has been suffering from increasing security threats since many years which compromise the whole network security. Automating the management of IoT devices helps in implementing security measures within communication systems. Software Defined Networking (SDN) has been introduced as a new networking approach that enables this automation. Many approaches were developed to mitigate IoT attacks in SDN-based IoT networks. Some studies investigated the prevention of flooding attacks, while others tried to cover broader attack surfaces. However, their proposed methods are time consuming and resource-exhausting as they use complex algorithms. In this paper, we propose a lightweight secure Threat Detection (TD) and Rule Automation (RA) framework namely “TD-RA” to effectively detect and mitigate different cyber-security threats in an SDN-based IoT environment. The proposed solution is composed of a Binary and Multi-class Classification Modules (BCM/MCM) for IoT threat detection and a Policy-Enforcement Module (PEM) for attack mitigation. Different machine learning methods have been implemented and compared to solve the classification problems. It is shown that for binary classification, the Decision Tree method achieves the highest accuracy which is around 98.7%, while for multi-class classification, Random Forest achieves the highest accuracy which is around 91.1%. The experimental results show that the proposed framework can successfully detect abnormal traffic and prevent IoT threats through SDN with smaller network overhead and high performance. Moreover, the overall processing time of our security modules is significantly smaller than that of existing solutions by reaching a mean value of 6 ms. This paper also introduces a large-scale architecture that comprises clusters of controllers to maintain high availability of network services. Such an integrated security approach, including detection and mitigation techniques, provides IT industries with reliable security measures that can be implemented to increase SDN-based IoT system responsiveness to different IoT attacks.

Multi‐hop similarity‐based‐clustering framework for IoT‐Oriented Software‐Defined wireless sensor networks

The performance of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) depends on the routing protocol and the deployment technique in modern applications. In a plethora of IoT-WSNs applications, the IoT nodes are essential equipment to prolong the network lifetime with limited resources. Data similarity-based clustering protocols exploit the temporal correlation among the neighbouring sensor nodes through the subset of data. In bendy supervision, IoT-based Software Defined WSNs provide an optimistic resolution by allowing the control logic to be separated from the sensor nodes. The benefit of this SDN-based IoT architecture, allows the unified control of the entire IoT network, making it easier to implement on-demand network management protocols and applications. To this end, in this paper, we design a Multi-hop Similarity-based Clustering framework for IoT-oriented Software-Defined wireless sensor Networks (MSCSDNs). In particular, we construct data-similar application-aware clusters in order to minimise the communication overhead. Also, we adapt inter-cluster and intra-cluster multi-hop communication using adaptive normalised least mean square and merged them with the proposed MSCSDN framework that helps prolong the network lifespan. The proposed framework is compared with the state-of-the-art approaches in terms of network lifespan, stability period, instability period, report delay, report delivery, and cluster leader nodes generations. The MSCSDN achieves optimal data accuracy concerning the collected data.

A Time-Efficient Approach Toward DDoS Attack Detection in IoT Network Using SDN

As the usability of Internet of Things (IoT) devices increases, the security threats and vulnerabilities associated with these resource-constrained IoT devices also rise. One of the major threats to IoT devices is Distributed Denial of Service (DDoS). To make the security of IoT devices effective and resilient, continuous monitoring and early detection, along with adaptive decision making, are required. These challenges can be addressed with software-defined networking (SDN), which provides an opportunity for effectively managing the DDoS threats faced by IoT devices. This research proposes a novel SDN-based secure IoT framework that can detect the vulnerabilities in IoT devices or malicious traffic generated by IoT devices using the session IP counter and IP Payload analysis. The framework’s DDoS attack detection module consisting of the proposed algorithms can easily detect the DDoS attack in the SD-IoT network by analyzing different parameters even with a large traffic volume. These techniques are implemented on an SDN controller and tested by generating a large volume of traffic from a compromised node, which is then detected and notified. According to the results and comparative analysis, the proposed framework detects DDoS attacks in the early stage with high accuracy and detection rate from 98% to 100%, having a low false-positive rate.

Toward Software-Defined Networking-Based IoT Frameworks: A Systematic Literature Review, Taxonomy, Open Challenges and Prospects

Internet of Things (IoT) is characterized as one of the leading actors for the next evolutionary stage in the computing world. IoT-based applications have already produced a plethora of novel services and are improving the living standard by enabling innovative and smart solutions. However, along with its rapid adoption, IoT technology also creates complex challenges regarding the management of IoT networks due to its resource limitations (computational power, energy, and security). Hence, it is urgently needed to refine the IoT-based application’s architectures to robustly manage the overall IoT infrastructure. Software-defined networking (SDN) has emerged as a paradigm that offers software-based controllers to manage hardware infrastructure and traffic flow on a network effectively. SDN architecture has the potential to provide efficient and reliable IoT network management. This research provides a comprehensive survey investigating the published studies on SDN-based frameworks to address IoT management issues in the dimensions of fault tolerance, energy management, scalability, load balancing, and security service provisioning within the IoT networks. We conducted a Systematic Literature Review (SLR) on the research studies (published from 2010 to 2022) focusing on SDN-based IoT management frameworks. We provide an extensive discussion on various aspects of SDN-based IoT solutions and architectures. We elaborate a taxonomy of the existing SDN-based IoT frameworks and solutions by classifying them into categories such as network function virtualization, middleware, OpenFlow adaptation, and blockchain-based management. We present the research gaps by identifying and analyzing the key architectural requirements and management issues in IoT infrastructures. Finally, we highlight various challenges and a range of promising opportunities for future research to provide a roadmap for addressing the weaknesses and identifying the benefits from the potentials offered by SDN-based IoT solutions.

Low-rate DDoS attack Detection using Deep Learning for SDN-enabled IoT Networks

Software Defined Networks (SDN) can logically route traffic and utilize underutilized network resources, which has enabled the deployment of SDN-enabled Internet of Things (IoT) architecture in many industrial systems. SDN also removes bottlenecks and helps process IoT data efficiently without overloading the network. An SDN-based IoT in an evolving environment is vulnerable to various types of distributed denial of service (DDoS) attacks. Many research papers focus on high-rate DDoS attacks, while few address low-rate DDoS attacks in SDN-based IoT networks. There’s a need to enhance the accuracy of LDDoS attack detection in SDN-based IoT networks and OpenFlow communication channel. In this paper, we propose LDDoS attack detection approach based on deep learning (DL) model that consists of an activation function of the Long-Short Term Memory (LSTM) to detect different types of LDDoS attacks in IoT networks by analyzing the characteristic values of different types of LDDoS attacks and natural traffic, improve the accuracy of LDDoS attack detection, and reduce the malicious traffic flow. The experiment result shows that the model achieved an accuracy of 98.88%. In addition, the model has been tested and validated using benchmark Edge IIoTset dataset which consist of cyber security attacks.

SDN for IoT Environment: A Survey and Research Challenges

Recently, it has emerged that both Internet of Things (IoT), and Software Defined Network (SDN) are becoming popular technologies. The main goal of IoT is to link electronic devices via the internet, meanwhile SDN facilitates network arrangement for management of a network by distinguishing the control layer and the data layer from each other. The number of electronic devices over the internet is increasing constantly, therefore it is a complicated process to manage and control especially over the huge distributed network. IoT network can be reasonably flexible and programmable through The SDN without introducing any trouble to the previously implemented network infrastructure. This paper reviews various IoT domains and applications such as cellular network, wireless Sensor, IoT management, security and smart city framework and common IoT SDN solutions. Moreover, The IoT and SDN notion has been explored critically, with assessing the current contributions in the research field. Lastly, analyzing current available solutions for SDN-based IoT implementations comparatively helps easily understanding the emerging trends view.

Efficient Intrusion Detection System for SDN Orchestrated Internet of Things

Internet of Things (IoT) can simply be defined as an extension of the current Internet system. It extends the human to human interconnection and intercommunication scenario of the Internet by including things, to bring anytime, anywhere, and anything communication. A discipline in networking evolving in parallel with IoT is Software Defined Networking (SDN). It is an important technology that is aimed to solve the different problems existing in the traditional network systems. It provides a new convenient home to address the different challenges existing in different network-based systems including IoT. One important security challenge prevailing in such SDN-based IoT (SDIoT) systems is guarantying service availability. The ever-increasing denial of service (DoS) attacks are responsible for such service denials. A centralized signature-based intrusion detection system (IDS) is proposed and developed in this work. Random Forest (RF) classifier is used for training the model. A very popular and recent benchmark dataset, CICIDS2017, has been used for training and validating the machine learning (ML) models. An accuracy result of 99.968% has been achieved by using only 12 features on Wednesday’s release of the dataset. This result is higher than the achieved accuracy results of related works considering the original CICIDS2017 dataset. A maximum cross-validated accuracy result of 99.713% has been achieved on the same release of the dataset. These developed models meet the basic requirement of a supervised IDS system developed for smart environments and can effectively be used in different IoT service scenarios.

A QoS Optimization Technique with Deep Reinforcement Learning in SDN-Based IoT

In recent years, exponential growth of communication devices in Internet of Things (IoT) has become an emerging technology which facilitates heterogeneous devices to connect with each other in heterogeneous networks. This communication requires different level of Quality-of-Service (QoS) and policies depending on the device type and location. To provide a specific level of QoS, we can utilize emerging new technological concepts in IoT infrastructure, software-defined network (SDN) and, machine learning algorithms. We use deep reinforcement learning in the process of resource management and allocation in control plane. We present an algorithm that aims to optimize resource allocation. Simulation results show that the proposed algorithm improved network performances in terms of QoS parameters, including delay and throughput compared to Random and Round Robin methods. Compared to similar methods the performance of the proposed method is also as good as the fuzzy and predictive methods.

FUPE: A security driven task scheduling approach for SDN-based IoT–Fog networks

Fog computing is a paradigm to overcome the cloud computing limitations which provides low latency to the users’ applications for the Internet of Things (IoT). Software-defined networking (SDN) is a practical networking infrastructure that provides a great capability in managing network flows. SDN switches are powerful devices, which can be used as fog devices/fog gateways simultaneously. Hence, fog devices are more vulnerable to several attacks. TCP SYN flood attack is one of the most common denial of service attacks, in which a malicious node produces many half-open TCP connections on the targeted computational nodes so as to break them down. Motivated by this, in this paper, we apply SDN concepts to address TCP SYN flood attacks in IoT–fog networks . We propose FUPE, a security-aware task scheduler in IoT–fog networks. FUPE puts forward a fuzzy-based multi-objective particle swarm Optimization approach to aggregate optimal computing resources and providing a proper level of security protection into one synthetic objective to find a single proper answer. We perform extensive simulations on IoT-based scenario to show that the FUPE algorithm significantly outperforms state-of-the-art algorithms. The simulation results indicate that, by varying the attack rates, the number of fog devices, and the number of jobs, the average response time of FUPE improved by 11% and 17%, and the network utilization of FUPE improved by 10% and 22% in comparison with Genetic and Particle Swarm Optimization algorithms, respectively.

Dynamic adjustment for proactive flow installation mechanism in SDN-based IoT

To satisfy various network requirements in the Internet of Things (IoT), software-defined networking (SDN) is viewed as an indispensable technology. Given the flow table resources in switches are limited, we proposed the periodic subflow-based proactive flow installation mechanism (PSPFIM) in our previous work. By separating subflows, PSPFIM can accurately detect the transmission periods and preinstall flow entry into switches before the transmissions arrive to reduce flow table occupancy. PSPFIM, however, is dysfunctional if transmission periods are at the millisecond level or subflows cannot be separated. In this paper, we propose the dynamic adjustment for PFIM (DAPFIM). DAPFIM is more widely applicable because the enhanced detection algorithm is less affected by noises and can detect transmission periods at the millisecond level. Besides, the adjustment algorithm can correct the error between an expected transmission period and a real transmission period. Through simulations, DAPFIM can detect transmission periods that cannot be detected by PSPFIM and other related work. Besides, DAPFIM averagely reduces up to 78% flow entry duration compared to other related work by changing the unit of the idle timeout from seconds to milliseconds, which saves much more flow table resources in switches.

Quality of Service Evaluation of Software Defined Internet of Things Network

With the exponential growth of the Internet of Things (IoT) devices connected to the internet, resource provisioning for such the heterogeneous network is a challenging task for the traditional network architecture. In this context, the Software-Defined Networking (SDN) introduces many opportunities and provides the potential to overcome challenges associated with traditional network architecture. This work presents a Software-Defined IoT (SDIoT) architecture. The main focus of this research is to design a control plane (CP) for the SDIoT. The scope of this work is limited to the introduction of an overlay SDN CP in the traditional IoT network architecture. The proposed architecture focuses on resource provisioning while ensuring the quality of service (QoS) satisfaction for the network. A comparative analysis between the traditional and the SDN network approach was done in terms of Jitter, Latency and Throughput. From the latency, delay and throughput performance results, the SDN-based IoT network improves network efficiency by reducing network overheads generated from frequent communication between the nodes and the controllers. Precisely, the average latency and average jitter percentile improvement from the traditional IoT network to the SDIoT for all the nodes is 574% and 600% respectively. Also, an overall throughput improvement is recorded for the SDIoT when compared to traditional IoT network for all the nodes.

Mitigation of DDOS and MiTM Attacks using Belief Based Secure Correlation Approach in SDN-Based IoT Networks

In recent years the domain of Internet of Things (IoT) has acquired great interest from the ICT community. Environmental observation and collecting information is one of the key reasons that IoT infrastructure facilitates the creation of many varieties of the latest business methods and applications. There are however still issues about security measures to be resolved to ensure adequate operation of devices. Distributed Denial of Service (DDoS) attacks are currently the most severe virtual threats that are causing serious damage to many IoT devices. With this in mind, numerous research projects were carried out to discover new methods and develop Novel techniques and solutions for DDOS attacks prevention. The use of new technology, such as software-defined networking (SDN) along with IoT devices has proven to be an innovative solution to mitigate DDoS attacks. In this article, we are using a novel data sharing system in IoT units that link IoT units with the SDN controller and encrypt information from IoT unit. We use conventional Redstone cryptographic algorithms to encrypt information from IoT devices in this framework. The Proposed Belief Based Secure Correlation methodology supports the prevention of DDOS attacks and other forms of data attacks. The system proposes new routes for transmission through the controller and communicates with approved switches for the safe transmission of data. To simulate our entire scenario, we proposed the algorithm Belief Based Secure Correlation (BBSC) implemented in SDN–IoT Testbed and verified IoT data is secure during transmission in the network.