Toward Secure Software-Defined Networks Using Machine Learning: A Review, Research Challenges, and Future Directions

Over the past few years,rapid advancements in the internet and communication technologies have led to increasingly intricate and diverse networking systems.As a result,greater intelligence is necessary to effectively manage,optimize,and maintain these systems.Due to their distributed nature,machine learning models are challenging to deploy in traditional networks.However,Software-Defined Networking(SDN)presents an opportunity to integrate intelligence into networks by offering a programmable architecture that separates data and control planes.SDN provides a centralized network view and allows for dynamic updates of flow rules and softwarebased traffic analysis.While the programmable nature of SDN makes it easier to deploy machine learning techniques,the centralized control logic also makes it vulnerable to cyberattacks.To address these issues,recent research has focused on developing powerful machine-learning methods for detecting and mitigating attacks in SDN environments.This paper highlighted the countermeasures for cyberattacks on SDN and how current machine learningbased solutions can overcome these emerging issues.We also discuss the pros and cons of using machine learning algorithms for detecting and mitigating these attacks.Finally,we highlighted research issues,gaps,and challenges in developing machine learning-based solutions to secure the SDN controller,to help the research and network community to develop more robust and reliable solutions.

DDoS Detection in SDN using Machine Learning Techniques

Software-defined network(SDN)becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure.The SDN controller is considered as the operating system of the SDN based network infrastructure,and it is responsible for executing the different network applications and maintaining the network services and functionalities.Despite all its tremendous capabilities,the SDN face many security issues due to the complexity of the SDN architecture.Distributed denial of services(DDoS)is a common attack on SDN due to its centralized architecture,especially at the control layer of the SDN that has a network-wide impact.Machine learning is now widely used for fast detection of these attacks.In this paper,some important feature selection methods for machine learning on DDoS detection are evaluated.The selection of optimal features reflects the classification accuracy of the machine learning techniques and the performance of the SDN controller.A comparative analysis of feature selection and machine learning classifiers is also derived to detect SDN attacks.The experimental results show that the Random forest(RF)classifier trains the more accurate model with 99.97%accuracy using features subset by the Recursive feature elimination(RFE)method.

Fusion-Based Machine Learning Architecture for Heart Disease Prediction

The contemporary evolution in healthcare technologies plays a considerable and signicant role to improve medical services and save human lives.Heart disease or cardiovascular disease is the most fatal and complex disease which it is hardly to be detected through our naked eyes,as numerous people have been suffering from this disease globally.Heart attacks occur when the ranges of vital signs such as blood pressure,pulse rate,and body temperature exceed their normal values.The efcient diagnosis of heart diseases could play a substantial role in the eld of cardiology,while diagnostic time could be reduced.It has been a key challenge for researchers and medical experts to diagnose heart diseases accurately and timely.Therefore,machine learning-based techniques are used for the diagnosis with higher accuracy,using datasets compiled from former medical patients’reports.In recent years,numerous studies have been presented in the literature propose machine learning techniques for diagnosing heart diseases.However,the existing techniques have some limitations in terms of their accuracy.In this paper,a novel Support Vector Machine(SVM)based architecture for heart disease prediction,empowered with a fuzzy based decision level fusion,is presented.The SVMbased architecture has improved the accuracy signicantly as compared to existing solutions,where 96.23%accuracy has been achieved.

Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review

Trabecular bone holds the utmost importance due to its significance regarding early bone loss.Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture.The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging(MRI)technique.These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis.The things that were considered before the selection of the articles for the systematic review were language,research field,and electronic sources.Only those articles written in the English language were selected as it is the most prominent language used in scientific,engineering,computer science,and biomedical researches.This literature review was conducted on the articles published between 2006 and 2020.A total of 62 research papers out of 1050 papers were extracted which were according to our topic of review after screening abstract and article content for the title and abstract screening.The findings from those researches were compiled at the end of the result section.This systematic literature review presents a comprehensive report on scientific researches and studies that have been done in the medical area concerning trabecular bone.

An Energy-Efcient Mobile-Sink Path-Finding Strategy for UAV WSNs

Data collection using a mobile sink in a Wireless Sensor Network(WSN)has received much attention in recent years owing to its potential to reduce the energy consumption of sensor nodes and thus enhancing the lifetime of the WSN.However,a critical issue of this approach is the latency of data to reach the base station.Although many data collection algorithms have been introduced in the literature to reduce delays in data delivery,their performances are affected by the ight trajectory taken by the mobile sink,which might not be optimized yet.This paper proposes a new path-nding strategy,called Energy-efciency Path-nding Strategy(EPS)in the Air-Ground Collaborative Wireless Sensor Network(AGCWSN).The proposed approach is able to greatly enhance the efciency of data collection.The performance of the proposed strategy is simulated and compared with the existing strategies over several parameters.The simulation results show that the mobile sink with EPS can collects data with lower data delivery delay as compared to other existing strategies.The number of data retransmissions between sensor nodes and mobile sink in EPS is also the lowest in EPS among several existing strategies.The data delivery delay is 66%and 120%lower than Rest Center Tractor Scanning(RCTS)and Non-stop Center Tractor Scanning(NCTS)in irregular and grid topology respectively.The data delivery delay is 62%lower than Two Row Scanning(TRS)in grid topology and 120%lower than RkM in irregular topology.The packet loss of EPS-2 is 1.3%lower than RkM.

Antenna and Base-Station Diversity for WSN Livestock Monitoring

Antenna and base-station diversity have been applied to a wireless sensor network for the monitoring of live-stock. A field trial has been described and the advantage to be gained in a practical environment has been assessed.