Tahir Riaz

Bio: Tahir Riaz is an academic researcher from Aalborg University. The author has contributed to research in topics: Network topology & Network planning and design. The author has an hindex of 11, co-authored 63 publications receiving 567 citations.

A method for classification of network traffic based on C5.0 Machine Learning Algorithm

Abstract: Monitoring of the network performance in highspeed Internet infrastructure is a challenging task, as the requirements for the given quality level are service-dependent. Backbone QoS monitoring and analysis in Multi-hop Networks requires therefore knowledge about types of applications forming current network traffic. To overcome the drawbacks of existing methods for traffic classification, usage of C5.0 Machine Learning Algorithm (MLA) was proposed. On the basis of statistical traffic information received from volunteers and C5.0 algorithm we constructed a boosted classifier, which was shown to have ability to distinguish between 7 different applications in test set of 76,632–1,622,710 unknown cases with average accuracy of 99.3–99.9%. This high accuracy was achieved by using high quality training data collected by our system, a unique set of parameters used for both training and classification, an algorithm for recognizing flow direction and the C5.0 itself. Classified applications include Skype, FTP, torrent, web browser traffic, web radio, interactive gaming and SSH. We performed subsequent tries using different sets of parameters and both training and classification options. This paper shows how we collected accurate traffic data, presents arguments used in classification process, introduces the C5.0 classifier and its options, and finally evaluates and compares the obtained results.

Smart Waste Collection System Based on Location Intelligence

Abstract: Cities around the world are on the run to become smarter. Some of these have seen an opportunity on deploying dedicated municipal access networks to support all types of city management and maintenance services requiring a data connection. This paper practically demonstrates how Internet of Things (IoT) integration with data access networks, Geographic Information Systems (GIS), combinatorial optimization, and electronic engineering can contribute to improve cities’ management systems. We present a waste collection solution based on providing intelligence to trashcans, by using an IoT prototype embedded with sensors, which can read, collect, and transmit trash volume data over the Internet. This data put into a spatio-temporal context and processed by graph theory optimization algorithms can be used to dynamically and efficiently manage waste collection strategies. Experiments are carried out to investigate the benefits of such a system, in comparison to a traditional sectorial waste collection approaches, also including economic factors. A realistic scenario is set up by using Open Data from the city of Copenhagen, highlighting the opportunities created by this type of initiatives for third parties to contribute and develop Smart city solutions.

Advances in intelligent and soft computing

Abstract: LaVail (Eds), Retinal Degenerative Diseases (Advances in Experimental Medicine and Biology 723) ISBN 978-1-4614-0630-3 7 * € (D) 213,95 | € (A) 219,94 | sFr 266,50 7 € 199,95 | £180.00 Special_SpacerSpecial_Spacer Mylonakis (Eds), Recent Advances on Model Hosts (Advances in Experimental Medicine and Biology 710) ISBN 978-1-4419-5637-8 7 * € (D) 149,75 | € (A) 153,94 | sFr 201,00 7 € 139,95 | £126.00 Special_SpacerSpecial_Spacer

Assessing Measurements of QoS for Global Cloud Computing Services

Abstract: Many global distributed cloud computing applications and services running over the Internet, between globally dispersed clients and servers, will require certain levels of Quality of Service (QoS) in order to deliver and give a sufficiently smooth user experience. This would be essential for real-time streaming multimedia applications like online gaming and watching movies on a pay as you use basis hosted in a cloud computing environment. However, guaranteeing or even predicting QoS in global and diverse networks supporting complex hosting of application services is a very challenging issue that needs a stepwise refinement approach to be solved as the technology of cloud computing matures. In this paper, we investigate if latency in terms of simple Ping measurements can be used as an indicator for other QoS parameters such as jitter and throughput. The experiments were carried out on a global scale, between servers placed in universities in Denmark, Poland, Brazil and Malaysia. The results show some correlation between latency and throughput, and between latency and jitter, even though the results are not completely consistent. As a side result, we were able to monitor the changes in QoS parameters during a number of 24-hour periods. This is also a first step towards defining QoS parameters to be included in Service Level Agreements for cloud computing in the foreseeable future.

Volunteer-based system for classification of traffic in computer networks

Abstract: To overcome the drawbacks of existing methods for traffic classification (by ports, Deep Packet Inspection, statistical classification) a new system was developed, in which the data are collected from client machines. This paper presents design of the system, implementation, initial runs and obtained results. Furthermore, it proves that the system is feasible in terms of uptime and resource usage, assesses its performance and proposes future enhancements.

Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks

Abstract: In this paper, we introduce two independent hybrid mining algorithms to improve the classification accuracy rates of decision tree (DT) and naive Bayes (NB) classifiers for the classification of multi-class problems. Both DT and NB classifiers are useful, efficient and commonly used for solving classification problems in data mining. Since the presence of noisy contradictory instances in the training set may cause the generated decision tree suffers from overfitting and its accuracy may decrease, in our first proposed hybrid DT algorithm, we employ a naive Bayes (NB) classifier to remove the noisy troublesome instances from the training set before the DT induction. Moreover, it is extremely computationally expensive for a NB classifier to compute class conditional independence for a dataset with high dimensional attributes. Thus, in the second proposed hybrid NB classifier, we employ a DT induction to select a comparatively more important subset of attributes for the production of naive assumption of class conditional independence. We tested the performances of the two proposed hybrid algorithms against those of the existing DT and NB classifiers respectively using the classification accuracy, precision, sensitivity-specificity analysis, and 10-fold cross validation on 10 real benchmark datasets from UCI (University of California, Irvine) machine learning repository. The experimental results indicate that the proposed methods have produced impressive results in the classification of real life challenging multi-class problems. They are also able to automatically extract the most valuable training datasets and identify the most effective attributes for the description of instances from noisy complex training databases with large dimensions of attributes.

How myxobacteria glide

Abstract: BACKGROUND Many microorganisms, including myxobacteria, cyanobacteria, and flexibacteria, move by gliding. Although gliding always describes a slow surface-associated translocation in the direction of the cell's long axis, it can result from two very different propulsion mechanisms: social (S) motility and adventurous (A) motility. The force for S motility is generated by retraction of type 4 pili. A motility may be associated with the extrusion of slime, but evidence has been lacking, and how force might be generated has remained an enigma. Recently, nozzle-like structures were discovered in cyanobacteria from which slime emanated at the same rate at which the bacteria moved. This strongly implicates slime extrusion as a propulsion mechanism for gliding. RESULTS Here we show that similar but smaller nozzle-like structures are found in Myxococcus xanthus and that they are clustered at both cell poles, where one might expect propulsive organelles. Furthermore, light and electron microscopical observations show that slime is secreted in ribbons from the ends of cells. To test whether the slime propulsion hypothesis is physically reasonable, we construct a mathematical model of the slime nozzle to see if it can generate a force sufficient to propel M. xanthus at the observed velocities. The model assumes that the hydration of slime, a cationic polyelectrolyte, is the force-generating mechanism. CONCLUSIONS The discovery of nozzle-like organelles in various gliding bacteria suggests their role in prokaryotic gliding. Our calculations and our observations of slime trails demonstrate that slime extrusion from such nozzles can account for most of the observed properties of A motile gliding.

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

Abstract: Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.

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

Abstract: 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.