C4.5: Programs for Machine Learning (書評)

Traffic analysis is a compound of strategies intended to find relationships, patterns, anomalies, and misconfigurations, among others things, in Internet traffic. In particular, traffic classification is a subgroup of strategies in this field that aims at identifying the application’s name or type of Internet traffic. Nowadays, traffic classification has become a challenging task due to the rise of new technologies, such as traffic encryption and encapsulation, which decrease the performance of classical traffic classification strategies. Machine learning (ML) gains interest as a new direction in this field, showing signs of future success, such as knowledge extraction from encrypted traffic, and more accurate Quality of Service management. ML is fast becoming a key tool to build traffic classification solutions in real network traffic scenarios; in this sense, the purpose of this investigation is to explore the elements that allow this technique to work in the traffic classification field. Therefore, a systematic review is introduced based on the steps to achieve traffic classification by using ML techniques. The main aim is to understand and to identify the procedures followed by the existing works to achieve their goals. As a result, this survey paper finds a set of trends derived from the analysis performed on this domain; in this manner, the authors expect to outline future directions for ML-based traffic classification.

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Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey

The main task of future networks is to build, as much as possible, intelligent networking architectures for intellectualization, activation, and customization. Software-defined networking (SDN) technology breaks the tight coupling between the control plane and the data plane in the traditional network architecture, making the controllability, security, and economy of network resources into a reality. As one of the important actualization methods of artificial intelligence (AI), machine learning (ML), combined with SDN architecture will have great potential in areas, such as network resource management, route planning, traffic scheduling, fault diagnosis, and network security. This paper presents the network applications combined with SDN concepts based on ML from two perspectives, namely the perspective of ML algorithms and SDN network applications. From the perspective of ML algorithms, this paper focuses on the applications of classical ML algorithms in SDN-based networks, after a characteristic analysis of algorithms. From the other perspective, after classifying the existing network applications based on the SDN architecture, the related ML solutions are introduced. Finally, the future development of the ML algorithms and SDN concepts is discussed and analyzed. This paper occupies the intersection of the AI, big data, computer networking, and other disciplines; the AI itself is a new and complex interdisciplinary field, which causes the researchers in this field to often have different professional backgrounds and, sometimes, divergent research purposes. This paper is necessary and helpful for researchers from different fields to accurately master the key issues.

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A Survey of Networking Applications Applying the Software Defined Networking Concept Based on Machine Learning

Proteins play important roles in living organisms, and their function is directly linked with their structure. Due to the growing gap between the number of proteins being discovered and their functional characterization (in particular as a result of experimental limitations), reliable prediction of protein function through computational means has become crucial. This paper reviews the machine learning techniques used in the literature, following their evolution from simple algorithms such as logistic regression to more advanced methods like support vector machines and modern deep neural networks. Hyperparameter optimization methods adopted to boost prediction performance are presented. In parallel, the metamorphosis in the features used by these algorithms from classical physicochemical properties and amino acid composition, up to text-derived features from biomedical literature and learned feature representations using autoencoders, together with feature selection and dimensionality reduction techniques, are also reviewed. The success stories in the application of these techniques to both general and specific protein function prediction are discussed.

Machine learning techniques for protein function prediction

Summary This paper has deeply investigated the performance signature of modulation techniques based low earth orbit (LEO)/medium earth orbit (MEO) intersatellite optical wireless communication syst...

Performance enhancement of overall LEO/MEO intersatellite optical wireless communication systems

Network traffic classification is important for QoS, Network management and security monitoring. Current method for traffic classification such as port based or payload based suffered many problems. Newly emerged application uses encryption and dynamic port numbers to avoid detection. So we use unsupervised machine learning approach to classify the network traffic. In this paper unsupervised K-means and Expectation Maximization algorithm are used to cluster the network traffic application based on similarity between them. Performance of these two algorithms is compared in terms of classification accuracy between them. The experiment results show that K-Means and EM perform well but accuracy of K-Means is better than EM and it form better cluster.

Performance Analysis of Unsupervised Machine Learning Techniques for Network Traffic Classification

In the era of Computerization Object Oriented Paradigm is becoming more and more pronounced. This has provoked the need of high quality object oriented software, as the traditional metrics cannot be applied on the object-oriented systems. Although CK suit of metric is the widely accepted metrics but when analyzed, according to their validation criteria on which these are based, these metrics can’t satisfy certain axioms. This paper gives the evaluation of CK suit of metrics and suggests the refinements and extensions to these metrics so that these metrics should reflect accurate and precise results for OO based systems.

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A Critical Suggestive Evaluation of CK Metric

Navigation systems are extensively used for location identification and route finding. The efficiency of navigation systems is highly affected by map matching algorithms. This paper provides a review of major map matching algorithms. The performance of reviewed algorithms was further evaluated with the help of an empirical study. A dataset of forty seven kilometers was collected to deploy various map matching algorithms so as to measure their performance. A comparison of geometric, topological and Kalman filter based map matching algorithms was performed on the same dataset. It was concluded that performance of Kalman filter algorithm provide better results in comparison to geometrical and topological algorithms.

Evaluating the performance of map matching algorithms for navigation systems: an empirical study

In this paper the bit error rate (BER) analysis of binary phase shift keying (BPSK) modulated signal detection is performed in presence of additive white Gaussian noise. Through this technique we have analyzed and calculated probability of error with systems operating with the additive white Gaussian noise and by doing so the performance of modulation techniques can be compared with theoretical values.

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Optimization Method for Analysis of Bit Error Rate with BPSK Modulation Technique

Drug discoverers need to predict the functions of proteins which are responsible for various diseases in human body. The proposed method is to use priority based packages of SDFs (Sequence Derived Features) so that decision tree may be created by their depth exploration rather than exclusion. This research work develops a new decision tree induction technique in which uncertainty measure is used for best attribute selection. The model creates better decision tree in terms of depth than the existing C4.5 technique. The tree with greater depth ensures more number of tests before functional class assignment and thus results in more accurate predictions than the existing prediction technique. For the same test data, the percentage accuracy of the new HPF (human protein function) predictor is 72% and that of the existing prediction technique is 44%.

Hardeep Singh

Papers

Performance Analysis of Unsupervised Machine Learning Techniques for Network Traffic Classification

A Critical Suggestive Evaluation of CK Metric

Evaluating the performance of map matching algorithms for navigation systems: an empirical study

Optimization Method for Analysis of Bit Error Rate with BPSK Modulation Technique

Decision Tree Classifier for Human Protein Function Prediction