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Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT

Muhammad Aslam, Dengpan Ye, Aqil Tariq, Muhammad Asad, Muhammad Hanif, David Ndzi, Samia Allaoua Chelloug, Mohamed Abd Elaziz, Mohammed A. A. Al-Qaness, Syeda Fizzah Jilani
2022 Sensors  
At the prelude stage of SDN-enabled IoT network infrastructure, the sampling based security approach currently results in low accuracy and low DDoS attack detection.  ...  In this paper, we propose an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework.  ...  In [34] , a machine learning-based DDoS detection system for SDN-enabled IoT is proposed, called LEDEM. The detection of LEDEM is based on a semi-supervised machine learning algorithm.  ... 
doi:10.3390/s22072697 pmid:35408312 pmcid:PMC9002783 fatcat:v6hfvmeorfayfo5ils5tvagiwa

A Taxonomy of DDoS Attack Mitigation Approaches Featured by SDN Technologies in IoT Scenarios

Felipe S. Dantas Silva, Esau Silva, Emidio P. Neto, Marcilio Lemos, Augusto J. Venancio Neto, Flavio Esposito
2020 Sensors  
The use of emerging technologies such as those based on the Software-Defined Networking (SDN) paradigm has proved to be a promising alternative as a means of mitigating DDoS attacks.  ...  This problem is exacerbated by the lack of benchmarks that can assist developers when designing new solutions for mitigating DDoS attacks for increasingly complex IoT scenarios.  ...  [61] were able to mitigate DDoS attacks with low rates through traffic filtering and by adopting machine learning algorithms.  ... 
doi:10.3390/s20113078 pmid:32485943 pmcid:PMC7309081 fatcat:v4dd357ednbkdewpvmue6xwdsa

Securing Smart Homes via Software-Defined Networking and Low-Cost Traffic Classification [article]

Holden Gordon, Christopher Batula, Bhagyashri Tushir, Behnam Dezfouli, Yuhong Liu
2021 arXiv   pre-print
Three popular machine learning algorithms, including K-Nearest-Neighbors, Random Forest, and Support Vector Machines, are used to classify IoT devices and detect different types of DDoS attacks, including  ...  IoT devices have become popular targets for various network attacks due to their lack of industry-wide security standards.  ...  RESULT AND DISCUSSION We test three machine learning models for IoT device classifications and DDoS detection. and DDoS detection [17] , [23] , [30] , [34] .  ... 
arXiv:2104.00296v2 fatcat:cb4qfnhnpvgifnoecol4z2zxaa

An Efficient Counter-Based DDoS Attack Detection Framework Leveraging Software Defined IoT (SD-IoT)

Jalal Bhayo, Sufian Hameed, Syed Attique Shah
2020 IEEE Access  
LEDEM uses a supervised machine learning algorithm to detect and mitigate the DDoS attack with an improved accuracy rate of 96.28% in detecting DDoS attack.  ...  traffic, it only detects the attack for traditional SDN based network 5 Wani et. al 2020 [45] MCOD SD-IoT Controller This research uses machine learning techniques to train the known features  ...  controller workload, and attack detection time.  ... 
doi:10.1109/access.2020.3043082 fatcat:lbcfegxxy5cszhd6eat5nwtxuq

SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT

Danish Javeed, Tianhan Gao, Muhammad Taimoor Khan
2021 Electronics  
We present an SDN-enabled architecture leveraging hybrid deep learning detection algorithms for the efficient detection of cyber threats and attacks while considering the resource-constrained IoT devices  ...  Despite offering numerous benefits, the prevalent nature of IoT makes it vulnerable and a possible target for the development of cyber-attacks.  ...  The authors in [40] present a machine learning-based attack detection approach in order to recognize significant threats in the IoT.  ... 
doi:10.3390/electronics10080918 fatcat:rj4h2wx3zvfedfp5ygzxhyxiya

Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments

Ahamed Aljuhani
2021 IEEE Access  
INDEX TERMS DDoS attacks and detection, Internet of Things (IoT), machine learning (ML), network functions virtualization (NFV), software-defined network (SDN).  ...  In recent years, machine learning (ML) techniques have been widely used to prevent DDoS attacks.  ...  DDoS DEFENSE SYSTEMS BASED ON ML TECHNIQUES IN SDN ENVIRONMENTS An SDN is a new network paradigm that enables the network topology to be controlled or programmed via various software applications [65]  ... 
doi:10.1109/access.2021.3062909 fatcat:xtj576lfsffrbpiqyk2kv5wuam

Mitigating DDoS Attacks in SDN-Based IoT Networks Leveraging Secure Control and Data Plane Algorithm

Song Wang, Karina Gomez, Kandeepan Sithamparanathan, Muhammad Rizwan Asghar, Giovanni Russello, Paul Zanna
2021 Applied Sciences  
Our results demonstrate that DDoS attacks in the SDN-based IoT network are easier to detect than in the traditional network due to IoT traffic predictability.  ...  However, there is a need to make a security enhancement in the SDN-based IoT network for mitigating attacks involving IoT devices.  ...  [30] also propose to use machine learning to detect DDoS attacks, they combine both cloud networks and wireless SDN to protect IoT networks against DDoS attacks.  ... 
doi:10.3390/app11030929 fatcat:xofgiq4yvvewjpgpsdrgfsvu6q

A Hybrid Deep Learning-Driven SDN Enabled Mechanism for Secure Communication in Internet of Things (IoT)

Danish Javeed, Tianhan Gao, Muhammad Taimoor Khan, Ijaz Ahmad
2021 Sensors  
There is a severe need to secure the IoT environment from such attacks. In this paper, an SDN-enabled deep-learning-driven framework is proposed for threats detection in an IoT environment.  ...  The state-of-the-art Cuda-deep neural network, gated recurrent unit (Cu- DNNGRU), and Cuda-bidirectional long short-term memory (Cu-BLSTM) classifiers are adopted for effective threat detection.  ...  DL-based codetection model along with Snort IDS is presented in [42] for detection of IoT-based DDoS attacks.  ... 
doi:10.3390/s21144884 fatcat:yxx6qkpuhrfs3k26q2agkjmx4i

The role of Blockchain in DDoS attacks mitigation: techniques, open challenges and future directions [article]

Rajasekhar Chaganti, Bharat Bhushan, Vinayakumar Ravi
2022 arXiv   pre-print
With the proliferation of new technologies such as Internet of Things (IOT) and Software-Defined Networking(SDN) in the recent years, the distributed denial of service (DDoS)attack vector has broadened  ...  The DDoS mitigation techniques are classified based on the solution deployment location i.e. network based, near attacker location, near victim location and hybrid solutions in the network architecture  ...  SDN and blockchain solution Network level DDoS detection AS legacy networks issue Kataoka et al. [67] IoT botnets detection using SDN and Attacker location based detection Not applicable to non SDN based  ... 
arXiv:2202.03617v1 fatcat:dlbmy6nwnvhmtoraon3azb3ysm

Machine Learning for Securing SDN based 5G Network

Hassan A. Alamri, Vijey Thayananthan, Javad Yazdani
2021 International Journal of Computer Applications  
The purpose of this research is to analyze the suitable machine learning (ML) for securing the SDN controller targeted by DDoS attacks.  ...  Throughout this research, ML and detection technique of DDoS is considered to improve the security solutions of SDN based 5G networks.  ...  Further, ML supports to develop a flexible SDN-based architecture for identifying and mitigating low-rate DDoS attacks.  ... 
doi:10.5120/ijca2021921027 fatcat:nhie4s6qdrc2bf4shq6lypnkqe

Distributed Denial-of-Service (DDoS) Attacks and Defence Mechanisms in Various Web-enabled Computing Platforms

2022 International Journal on Semantic Web and Information Systems (IJSWIS)  
The expeditious surge in the collaborative environments, like IoT, cloud computing and SDN, have provided attackers with countless new avenues to benefit from the distributed nature of DDoS attacks.  ...  In the end, we list prevalent DDoS attack tools and open challenges.  ...  DDoS Detection Based on Machine Learning Machine Learning is coming across as one of the major approaches to overcome DDoS attacks in recent years.  ... 
doi:10.4018/ijswis.297143 fatcat:imoau72665dxbmfdoxvntbyyiq

R-IDPS: Real Time SDN-Based IDPS System for IoT Security

Noman Mazhar, Rosli Saleh, Reza Zaba, Muhammad Zeeshan, M. Muzaffar Hameed, Nauman Khan
2022 Computers Materials & Continua  
Further, online machine learning model training has been an issue. All these challenges still question the IoT network security.  ...  There has been a lot of research for IoT based detection systems to secure the IoT devices such as centralized and distributed architecture-based detection systems.  ...  The detection module uses machine learning for the detection of attacks based on the baseline network profile. The baseline network profile is developed using normal network traffic statistics.  ... 
doi:10.32604/cmc.2022.028285 fatcat:a6njdvakc5b3viqhi4vdl3gwnm

Explainable Security in SDN-Based IoT Networks

Alper Kaan Sarica, Pelin Angin
2020 Sensors  
The prevalence of IoT and the large attack surface that it has created calls for SDN-based intelligent security solutions that achieve real-time, automated intrusion detection and mitigation.  ...  The experimental results demonstrate that the proposed security approach is promising for achieving real-time, highly accurate detection and mitigation of attacks in SDN-managed IoT networks.  ...  In [8] , a semi-supervised model was used to detect DDoS attacks in SDN-based IoT networks.  ... 
doi:10.3390/s20247326 pmid:33419302 fatcat:ulbzptgb3rh2ngutiy243yx5em

Novel three-Tier Intrusion Detection and Prevention System in Software Defined Network

Amir Ali, Muhammad Murtaza Yousaf
2020 IEEE Access  
Software Defined Network (SDN) is a flexible paradigm that provides support for a variety of data-intensive applications with real-world smart Internet of Things (IoT) devices.  ...  Distributed Denial of Service (DDoS) is a targeted attack that develops malicious traffic is flooded into a particular network device.  ...  The learning parameters are tuned based on the features and then applied for attack prediction. Extreme Learning Machine (ELM) was also incorporated for attack detection [26] .  ... 
doi:10.1109/access.2020.3002333 fatcat:4ijmba3l55amroazlz6vopei4i

An investigation of different DDOS attack detection methods in software-defined networks

Gaganjot Kaur, Prinima Gupta
2022 International Journal of Health Sciences  
Detection of DDoS attacks requires different classification techniques that provide accurate and efficient decision-making.  ...  Software-Defined Network is more vulnerable to more frequent and severe security attacks.  ...  Data mining (DM) as well as Machine learning (ML) approaches are developed and hit immensely for identifying detection.  ... 
doi:10.53730/ijhs.v6ns1.4863 fatcat:hwhfiwntt5hhdc3afvsslfz2de
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