Machine learning based low-rate DDoS attack detection for SDN enabled IoT networks

Cheng, Haosu; Liu, Jianwei; Xu, Tongge; Ren, Bohan; Mao, Jian; Zhang, Wei

doi:10.1504/ijsnet.2020.109720

Cited by 36 publications

(14 citation statements)

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“…The host machine would permit the administrator to monitor the attacker and their activities and alert the data owner in the cloud [16]. The proposed method has enhanced efficacy over the overall system's performance [17], [18].…”

Section: Review Of Existing Workmentioning

confidence: 99%

An SDN-based Decision Tree Detection (DTD) Model for Detecting DDoS Attacks in Cloud Environment

J¹,

Sridaran²

2022

IJACSA

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Detecting Distributed Denial of Service (DDoS) attacks has become a significant security issue for various network technologies. This attack has to be detected to increase the system's reliability. Though various traditional studies are present, they suffer from data shift issues and accuracy. Hence, this study intends to detect DDoS attacks by classifying the normal and malicious traffic. The study aims to solve the data shift issues by using the introduced Decision Tree Detection (DTD) model encompassing of Greedy Feature Selection (GFS) algorithm and Decision Tree Algorithm (DTA). It also attempts to enhance the proposed model's detection rate (accuracy) above 90%. Various processes are involved in DDoS attack detection. Initially, the gureKddcup dataset is loaded to perform preprocessing. This process is essential for removing noisy data. After this, feature selection is performed to select only the relevant features, removing the irrelevant data. This is then fed into the train and test split. Following this, Software Defined Networking (SDN) based DTA is used to classify the normal and malicious traffic, then given to the trained model for predicting this attack. Performance analysis is undertaken by comparing the proposed model with existing systems in terms of accuracy, MCC (Matthew's Correlation Coefficient), sensitivity, specificity, error rate, FAR (False Alarm Rate), and AUC (Area under Curve). This analysis is carried out to evaluate the efficacy of the proposed model, which is verified through the results.

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Section: Review Of Existing Workmentioning

confidence: 99%

An SDN-based Decision Tree Detection (DTD) Model for Detecting DDoS Attacks in Cloud Environment

J¹,

Sridaran²

2022

IJACSA

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“…Attacks are almost in every cyber or physical related field [26]. Our research interests focus on attacks on ships and the functions of ships.…”

Section: Introductionmentioning

confidence: 99%

Impacts of GPS Spoofing on Path Planning of Unmanned Surface Ships

Wang

Xiao

et al. 2022

Electronics

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The Artificial Potential Field (APF) method is a classical path planning method for unmanned ships, relying on Global Positioning System (GPS) positioning information for path planning. Unfortunately, once the path planning algorithm uses inaccurate or even fake data, it will lead to ship collision, grounding, or deviation from the course, causing severe economic losses and causing significant security risks to other sailing ships. This paper aims to study the impacts of GPS spoofing on the path planning of unmanned ships. We propose a GPS attack and study GPS spoofing of path planning based on the APF method for an unmanned ship by a low-cost software-defined radio, which causes the unmanned ship to deviate from the course. Our simulation tests show that this method has significant impacts on the path planning results of the APF method.

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“…The SD-IoT controller can be easily programmed according to the needs of network administrators. Although this layered network architecture provides more flexible configuration and capabilities than traditional networks, it increases the likelihood of attacks, especially the controller [9]. One of the attacks that could impact the SD-IoT integration model is DDoS.…”

Section: Introductionmentioning

confidence: 99%

“…One of the attacks that could impact the SD-IoT integration model is DDoS. In general, DDoS attacks are classified into two types, namely High Rate Distributed Denial Of Service (HRDDoS) and Low Rate Distributed Denial Of Service (LRDDoS) [9], [10]. LRDDoS attacks initiated on data fields are characterized by low speed, concealment, and persistence, which make them difficult to detect.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed algorithm was compared with the other two algorithms and had good results in mitigating attacks. Research [9] proposed a machine learning-based low rate DDoS attack detection system (MLDD) by using the stateful and stateless features of the Openflow package to identify attack traffic on the SDN controller. Researchers used datasets generated in real time from the experimental network environment.…”

Section: Introductionmentioning

confidence: 99%

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Low-Rate Attack Detection on SD-IoT Using SVM Combined with Feature Importance Logistic Regression Coefficient

Azmi

Sumadi

2022

KINETIK

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The evolution of computer network technology is now experiencing substantial changes, particularly with the introduction of a new paradigm, Software Defined Networking (SDN). The SDN architecture has been applied in a variety of networks, including the Internet of Things (IoT), which is known as SD-IoT. IoT is made up of billions of networking devices that are interconnected and linked to the Internet. Since the SD-IoT was considered as a complex entity, several types of attack on vulnerabilities vary greatly and can be exploited by careless individuals. Low-Rate Distributed Denial of Service (LRDDoS) is one of the availability-based attack that may affect the SD-IoT integration paradigm. Therefore, it is necessary to have an Intrusion Detection System (IDS) to overcome the security hole caused by LRDDoS. The main objective of this research was the establishment of an IDS application for resolving LRDDoS attack using the SVM algorithm combined with the Feature Importance method, namely the Logistic Regression Coefficient. The implemented approach was developed to reduce the complexity or resource’s consumption during the classification process as well as increasing the accuracy. It could be concluded that the Linear kernel SVM algorithm acquired the highest results on the test schemes at 100% accuracy, but the training time required for this model was longer, about 23.6 seconds compared to the Radial Basis Function model which only takes about 1.5 seconds.

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Machine learning based low-rate DDoS attack detection for SDN enabled IoT networks

Cited by 36 publications

References 0 publications

An SDN-based Decision Tree Detection (DTD) Model for Detecting DDoS Attacks in Cloud Environment

An SDN-based Decision Tree Detection (DTD) Model for Detecting DDoS Attacks in Cloud Environment

Impacts of GPS Spoofing on Path Planning of Unmanned Surface Ships

Low-Rate Attack Detection on SD-IoT Using SVM Combined with Feature Importance Logistic Regression Coefficient

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