Detection of DDOS Attacks Using Machine Learning Techniques: A Hybrid Approach

Accurate and timely estimation of freeway traffic speeds by short segments plays an important role in traffic monitoring systems. In the literature, the ability of machine learning techniques to capture the stochastic characteristics of traffic has been proved. Also, the deployment of intelligent transportation systems (ITSs) has provided enriched traffic data, which enables the adoption of a variety of machine learning methods to estimate freeway traffic speeds. However, the limitation of data quality and coverage remain a big challenge in current traffic monitoring systems. To overcome this problem, this study aims to develop a hybrid machine learning approach, by creating a new training variable based on the second-order traffic flow model, to improve the accuracy of traffic speed estimation. Grounded on a novel integrated framework, the estimation is performed using three machine learning techniques, that is, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). All three models are trained with the integrated dataset including the traffic flow model estimates and the iPeMS and PeMS data from the Utah Department of Transportation (DOT). Further using the PeMS data as the ground truth for model evaluation, the comparisons between the hybrid approach and pure machine learning models show that the hybrid approach can effectively capture the time-varying pattern of the traffic and help improve the estimation accuracy.

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Arabic tweets sentiment analysis – a hybrid scheme

Journal of Information Science ◽

10.1177/0165551515610513 ◽

2016 ◽

Vol 42 (6) ◽

pp. 782-797 ◽

Cited By ~ 42

Author(s):

Haifa K. Aldayel ◽

Aqil M. Azmi

Keyword(s):

Machine Learning ◽

Saudi Arabia ◽

Hybrid Approach ◽

Training Data ◽

Machine Learning Techniques ◽

Good Source ◽

Learning Classifier ◽

Learning Techniques ◽

Semantic Orientation ◽

F Measure

The fact that people freely express their opinions and ideas in no more than 140 characters makes Twitter one of the most prevalent social networking websites in the world. Being popular in Saudi Arabia, we believe that tweets are a good source to capture the public’s sentiment, especially since the country is in a fractious region. Going over the challenges and the difficulties that the Arabic tweets present – using Saudi Arabia as a basis – we propose our solution. A typical problem is the practice of tweeting in dialectical Arabic. Based on our observation we recommend a hybrid approach that combines semantic orientation and machine learning techniques. Through this approach, the lexical-based classifier will label the training data, a time-consuming task often prepared manually. The output of the lexical classifier will be used as training data for the SVM machine learning classifier. The experiments show that our hybrid approach improved the F-measure of the lexical classifier by 5.76% while the accuracy jumped by 16.41%, achieving an overall F-measure and accuracy of 84 and 84.01% respectively.

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An Effective Hybrid Approach Based on Machine Learning Techniques for Auto-Translation: Japanese to English

10.1109/3ict53449.2021.9581629 ◽

2021 ◽

Author(s):

Mhd Saeed Sharif ◽

Bilyaminu Auwal Romo ◽

Harry Maltby ◽

Ali Al-Bayatti

Keyword(s):

Machine Learning ◽

Hybrid Approach ◽

Machine Learning Techniques ◽

Learning Techniques

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NETWORK ANOMALIES DETECTION APPROACH BASED ON WEIGHTED VOTING

International Journal of Information Security and Privacy ◽

10.4018/ijisp.2022010105 ◽

2022 ◽

Vol 16 (1) ◽

pp. 0-0

Keyword(s):

Machine Learning ◽

Information Systems ◽

Hybrid Approach ◽

Machine Learning Techniques ◽

Computationally Efficient ◽

Weighted Voting ◽

Detection Systems ◽

Learning Techniques ◽

Detection Approach ◽

Accuracy Increase

To avoid information systems malfunction, their integrity disruption, availability violation as well as data confidentiality, it is necessary to detect anomalies in information system operation as quickly as possible. The anomalies are usually caused by malicious activity – information systems attacks. However, the current approaches to detect anomalies in information systems functioning have never been perfect. In particular, statistical and signature-based techniques do not allow detection of anomalies based on modifications of well-known attacks, dynamic approaches based on machine learning techniques result in false responses and frequent anomaly miss-outs. Therefore, various hybrid solutions are being frequently offered on the basis of those two approaches. The paper suggests a hybrid approach to detect anomalies by combining computationally efficient classifiers of machine learning with accuracy increase due to weighted voting. Pilot evaluation of the developed approach proved its feasibility for anomaly detection systems.

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An Empirical Evaluation On Comparative Machine Learning Techniques For Detection of The Distributed Denial of Service (DDoS) Attacks

Journal of Applied Science, Engineering, Technology, and Education ◽

10.35877/454ri.asci2192 ◽

2020 ◽

Vol 2 (1) ◽

pp. 18-27

Author(s):

Arnold Ojugo ◽

Andrew Okonji Eboka

Keyword(s):

Machine Learning ◽

Denial Of Service ◽

Empirical Evaluation ◽

Machine Learning Techniques ◽

Distributed Denial Of Service ◽

Ddos Attacks ◽

Secure Systems ◽

Data Encoding ◽

Signature Detection ◽

Learning Techniques

The advent of the Internet that aided the efficient sharing of resources. Also, it has introduced adversaries whom are today restlessly in their continued efforts at an effective, non-detectable means to invade secure systems, either for fun or personal gains. They achieve these feats via the use of malware, which is both on the rise, wreaks havoc alongside causing loads of financial losses to users. With the upsurge to counter these escapades, users and businesses today seek means to detect these evolving behavior and pattern by these adversaries. It is also to worthy of note that adversaries have also evolved, changing their own structure to make signature detection somewhat unreliable and anomaly detection tedious to network administrators. Our study investigates the detection of the distributed denial of service (DDoS) attacks using machine learning techniques. Results shows that though evolutionary models have been successfully implemented in the detection DDoS, the search for optima is an inconclusive and continuous task. That no one method yields a better optima than hybrids. That with hybrids, users must adequately resolve the issues of data conflicts arising from the dataset to be used, conflict from the adapted statistical methods arising from data encoding, and conflicts in parameter selection to avoid model overtraining, over-fitting and over-parameterization.

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