A DDoS Attack Detection Method Based on SVM in Software Defined Network

The detection of DDoS attacks is an important topic in the field of network security. The occurrence of software defined network (SDN) (Zhang et al., 2018) brings up some novel methods to this topic in which some deep learning algorithm is adopted to model the attack behavior based on collecting from the SDN controller. However, the existing methods such as neural network algorithm are not practical enough to be applied. In this paper, the SDN environment by mininet and floodlight (Ning et al., 2014) simulation platform is constructed, 6-tuple characteristic values of the switch flow table is extracted, and then DDoS attack model is built by combining the SVM classification algorithms. The experiments show that average accuracy rate of our method is 95.24% with a small amount of flow collecting. Our work is of good value for the detection of DDoS attack in SDN.

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An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

Technologies ◽

10.3390/technologies9010014 ◽

2021 ◽

Vol 9 (1) ◽

pp. 14

Author(s):

James Dzisi Gadze ◽

Akua Acheampomaa Bamfo-Asante ◽

Justice Owusu Agyemang ◽

Henry Nunoo-Mensah ◽

Kwasi Adu-Boahen Opare

Keyword(s):

Deep Learning ◽

Network Architecture ◽

Learning Algorithm ◽

Single Point ◽

Denial Of Service ◽

Specific Work ◽

Learning Models ◽

Ddos Attack ◽

Deep Learning Algorithm ◽

Proposed Model

Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios.

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Ensemble Deep Learning for Cervix Image Selection toward Improving Reliability in Automated Cervical Precancer Screening

Diagnostics ◽

10.3390/diagnostics10070451 ◽

2020 ◽

Vol 10 (7) ◽

pp. 451 ◽

Cited By ~ 2

Author(s):

Peng Guo ◽

Zhiyun Xue ◽

Zac Mtema ◽

Karen Yeates ◽

Ophira Ginsburg ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Binary Classification ◽

Digital Camera ◽

Deep Learning Algorithm ◽

Average Accuracy ◽

Acceptable Quality ◽

Cervical Precancer ◽

One Class Classification ◽

Learning Architectures

Automated Visual Examination (AVE) is a deep learning algorithm that aims to improve the effectiveness of cervical precancer screening, particularly in low- and medium-resource regions. It was trained on data from a large longitudinal study conducted by the National Cancer Institute (NCI) and has been shown to accurately identify cervices with early stages of cervical neoplasia for clinical evaluation and treatment. The algorithm processes images of the uterine cervix taken with a digital camera and alerts the user if the woman is a candidate for further evaluation. This requires that the algorithm be presented with images of the cervix, which is the object of interest, of acceptable quality, i.e., in sharp focus, with good illumination, without shadows or other occlusions, and showing the entire squamo-columnar transformation zone. Our prior work has addressed some of these constraints to help discard images that do not meet these criteria. In this work, we present a novel algorithm that determines that the image contains the cervix to a sufficient extent. Non-cervix or other inadequate images could lead to suboptimal or wrong results. Manual removal of such images is labor intensive and time-consuming, particularly in working with large retrospective collections acquired with inadequate quality control. In this work, we present a novel ensemble deep learning method to identify cervix images and non-cervix images in a smartphone-acquired cervical image dataset. The ensemble method combined the assessment of three deep learning architectures, RetinaNet, Deep SVDD, and a customized CNN (Convolutional Neural Network), each using a different strategy to arrive at its decision, i.e., object detection, one-class classification, and binary classification. We examined the performance of each individual architecture and an ensemble of all three architectures. An average accuracy and F-1 score of 91.6% and 0.890, respectively, were achieved on a separate test dataset consisting of more than 30,000 smartphone-captured images.

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Reinforcement based Multi-Model Deep Learning Algorithm for Classification

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b6465.129219 ◽

2020 ◽

Vol 9 (2) ◽

pp. 1704-1710

Keyword(s):

Deep Learning ◽

Maximum Entropy ◽

Learning Algorithm ◽

Dimensional Space ◽

Image Data ◽

Space Policy ◽

Model Classification ◽

Deep Learning Algorithm ◽

Robust Model ◽

Neural Network Algorithm

Nowadays researchers are focused on processing the multi-media data for classifying the queries of end users by using search engines. The hybrid combination of a powerful classifier and deep feature extractor are used to develop a robust model, which is performed in a high dimensional space. In this research, a three different types of algorithms are combined to attain a stochastic belief space policy, where these algorithms include generative adversary modelling, maximum entropy Reinforcement Learning (RL) and belief space planning which leads to develop a multi-model classification algorithm. In the simulation framework, different adversarial behaviours are used to minimize the agent's action predictability, which has resulted the proposed method to attain robustness, while comparing with unmodelled adversarial strategies. The proposed reinforcement based Deep Learning (DL) algorithm can be used as multi-model classification purpose. The single neural network algorithm can perform the classification on text data and image data. The RL learns the appropriate belief space policy from the feature extracted information of the text and image data, the belief space policy is generated based on the maximum entropy computation

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Mitigation of DDoS attack instigated by compromised switches on SDN controller by analyzing the flow rule request traffic

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.6.10065 ◽

2018 ◽

Vol 7 (2.6) ◽

pp. 46 ◽

Cited By ~ 2

Author(s):

Sanjeetha R ◽

Shikhar Srivastava ◽

Rishab Pokharna ◽

Syed Shafiq ◽

Dr Anita Kanavalli

Keyword(s):

Network Architecture ◽

Flow Rule ◽

Software Defined Network ◽

Ddos Attacks ◽

Control Plane ◽

Ddos Attack ◽

Flow Table ◽

Data Plane ◽

Ddos Detection ◽

Sdn Controller

Software Defined Network (SDN) is a new network architecture which separates the data plane from the control plane. The SDN controller implements the control plane and switches implement the data plane. Many papers discuss about DDoS attacks on primary servers present in SDN and how they can be mitigated with the help of controller. In our paper we show how DDoS attack can be instigated on the SDN controller by manipulating the flow table entries of switches, such that they send continuous requests to the controller and exhaust its resources. This is a new, but one of the possible way in which a DDoS attack can be performed on controller. We show the vulnerability of SDN for this kind of attack. We further propose a solution for mitigating it, by running a DDoS Detection module which uses variation of flow entry request traffic from all switches in the network to identify compromised switches and blocks them completely.

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Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections

Journal of X-Ray Science and Technology ◽

10.3233/xst-200735 ◽

2020 ◽

pp. 1-17

Author(s):

Yanhong Yang ◽

Fleming Y.M. Lure ◽

Hengyuan Miao ◽

Ziqi Zhang ◽

Stefan Jaeger ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Model Development ◽

Training Data ◽

Test Accuracy ◽

Pulmonary Infections ◽

Deep Learning Algorithm ◽

Average Accuracy ◽

Independent Test ◽

Comparable Performance

Background: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment. Purpose: In this study, a deep learning algorithm-based AI model using ResUNet network was developed to evaluate the performance of radiologists with and without AI assistance in distinguishing COVID-19 infected pneumonia patients from other pulmonary infections on CT scans. Methods: For model development and validation, a total number of 694 cases with 111,066 CT slides were retrospectively collected as training data and independent test data in the study. Among them, 118 are confirmed COVID-19 infected pneumonia cases and 576 are other pulmonary infections cases (e.g. tuberculosis cases, common pneumonia cases and non-COVID-19 viral pneumonia cases). The cases were divided into training and testing datasets. The independent test was performed by evaluating and comparing the performance of three radiologists with different years of practice experience in distinguishing COVID-19 infected pneumonia cases with and without the AI assistance. Results: Our final model achieved an overall test accuracy of 0.914 with an area of the receiver operating characteristic (ROC) curve (AUC) of 0.903 in which the sensitivity and specificity are 0.918 and 0.909, respectively. The deep learning-based model then achieved a comparable performance by improving the radiologists’ performance in distinguish COVOD-19 from other pulmonary infections, yielding better average accuracy and sensitivity, from 0.941 to 0.951 and from 0.895 to 0.942, respectively, when compared to radiologists without using AI assistance. Conclusion: A deep learning algorithm-based AI model developed in this study successfully improved radiologists’ performance in distinguishing COVID-19 from other pulmonary infections using chest CT images.

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Research on the Construction Path of Ideological and Political Education for Postgraduates in the Era of Artificial Intelligence

Journal of Educational Theory and Management ◽

10.26549/jetm.v5i1.5964 ◽

2021 ◽

Vol 5 (1) ◽

pp. 14

Author(s):

Liu Ting ◽

Wang Anping

Keyword(s):

Artificial Intelligence ◽

Learning Algorithm ◽

Evaluation Criteria ◽

Ideological And Political Education ◽

Political Education ◽

Intelligent Network ◽

Network Systems ◽

Deep Learning Algorithm ◽

Communication Links ◽

Neural Network Algorithm

Postgraduate ideological and political education is an important part of ideological and political education in colleges and universities. It is the core of implementing the party’s educational policy, comprehensively improving the quality of education, and building a modern socialist education power. In the era of artificial intelligence, innovate classroom education of ideological and political education for graduate students through intelligent network systems, natural language understanding systems, and knowledge processing systems; use intelligent search systems, symbol processing systems, and combined planning systems to deepen the communication links of graduate ideological and political education; through enhancing digital intelligence tools such as genetic algorithm, deep learning algorithm and artificial neural network algorithm to reconstruct the evaluation criteria of graduate ideological and political education is an important engine to promote the intelligent, contemporary and diversified development of graduate ideological and political education in my country.

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Improving Distributed Denial of Service (DDOS) Detection using Entropy Method in Software Defined Network (SDN)

ComTech Computer Mathematics and Engineering Applications ◽

10.21512/comtech.v8i4.3902 ◽

2017 ◽

Vol 8 (4) ◽

pp. 215

Author(s):

Maman Abdurohman ◽

Dani Prasetiawan ◽

Fazmah Arif Yulianto

Keyword(s):

Denial Of Service ◽

Entropy Method ◽

Attack Detection ◽

New Method ◽

Sudden Increase ◽

Software Defined Network ◽

Distributed Denial Of Service ◽

Ddos Attack ◽

Ddos Detection ◽

Ddos Attack Detection

This research proposed a new method to enhance Distributed Denial of Service (DDoS) detection attack on Software Defined Network (SDN) environment. This research utilized the OpenFlow controller of SDN for DDoS attack detection using modified method and regarding entropy value. The new method would check whether the traffic was a normal traffic or DDoS attack by measuring the randomness of the packets. This method consisted of two steps, detecting attack and checking the entropy. The result shows that the new method can reduce false positive when there is a temporary and sudden increase in normal traffic. The new method succeeds in not detecting this as a DDoS attack. Compared to previous methods, this proposed method can enhance DDoS attack detection on SDN environment.

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Malaria Detection System Using Convolutional Neural Network Algorithm

Machine Learning and Deep Learning in Real-Time Applications - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-7998-3095-5.ch010 ◽

2020 ◽

pp. 219-230

Author(s):

Kanika Gautam ◽

Sunil Kumar Jangir ◽

Manish Kumar ◽

Jay Sharma

Keyword(s):

Neural Network ◽

Deep Learning ◽

Learning Algorithm ◽

Detection System ◽

Learning Algorithms ◽

Anopheles Mosquito ◽

Deep Learning Algorithm ◽

Blood Smears ◽

Neural Network Algorithm ◽

Modern Technologies

Malaria is a disease caused when a female Anopheles mosquito bites. There are over 200 million cases recorded per year with more than 400,000 deaths. Current methods of diagnosis are effective; however, they work on technologies that do not produce higher accuracy results. Henceforth, to improve the prediction rate of the disease, modern technologies need to be performed for obtain accurate results. Deep learning algorithms are developed to detect, learn, and determine the containing parasites from the red blood smears. This chapter shows the implementation of a deep learning algorithm to identify the malaria parasites with higher accuracy.

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