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3. An Efficient Internet Traffic Classification System Using Deep Learning for IoT

Author

Umair, Muhammad Basit, Iqbal, Zeshan, Bilal, Muhammad, Almohamad, Tarik Adnan, Nebhen, Jamel, and Mehmood, Raja Majid

Subjects
Computer Science - Networking and Internet Architecture, 62M45, 68T07, C.2.3
Abstract

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique., Comment: 14 pages, 4 figures, 11 tables, Accepted for publication in CMC-Computers, Materials & Continua

Published
2021
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10. Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors

Author

Ali, Nouman, Mazhar, Danish Ali, Iqbal, Zeshan, Ashraf, Rehan, Ahmed, Jawad, and Khan, Farrukh Zeeshan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

One of the challenges in Content-Based Image Retrieval (CBIR) is to reduce the semantic gaps between low-level features and high-level semantic concepts. In CBIR, the images are represented in the feature space and the performance of CBIR depends on the type of selected feature representation. Late fusion also known as visual words integration is applied to enhance the performance of image retrieval. The recent advances in image retrieval diverted the focus of research towards the use of binary descriptors as they are reported computationally efficient. In this paper, we aim to investigate the late fusion of Fast Retina Keypoint (FREAK) and Scale Invariant Feature Transform (SIFT). The late fusion of binary and local descriptor is selected because among binary descriptors, FREAK has shown good results in classification-based problems while SIFT is robust to translation, scaling, rotation and small distortions. The late fusion of FREAK and SIFT integrates the performance of both feature descriptors for an effective image retrieval. Experimental results and comparisons show that the proposed late fusion enhances the performances of image retrieval.

Published
2017

15. An Efficient Scheduling of User Appliances Using Multi Objective Optimization in Smart Grid

Author

Faisal, Hafiz Muhammad, Javaid, Nadeem, Qasim, Umar, Habib, Shujaat, Iqbal, Zeshan, Mubarak, Hasnain, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Takizawa, Makoto, editor, Xhafa, Fatos, editor, and Enokido, Tomoya, editor

Published
2019
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20. An IoT Environment Based Framework for Intelligent Intrusion Detection.

Author

Safwan, Hamza, Iqbal, Zeshan, Amin, Rashid, Khan, Muhammad Attique, Alhaisoni, Majed, Alqahtani, Abdullah, Ye Jin Kim, and Chang, Byoungchol

Subjects
MACHINE learning, INTRUSION detection systems (Computer security), SOFTWARE-defined networking, DEEP learning, FEATURE selection, INTERNET of things
Abstract

Software-defined networking (SDN) represents a paradigm shift in network traffic management. It distinguishes between the data and control planes. APIs are then used to communicate between these planes. The controller is central to the management of an SDN network and is subject to security concerns. This research shows how a deep learning algorithm can detect intrusions in SDN-based IoT networks. Overfitting, low accuracy, and efficient feature selection is all discussed. We propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory (LSTM). In this study, a new dataset based specifically on Software Defined Networks is used in SDN. To obtain the best and most relevant features, a feature selection technique is used. Several experiments have revealed that the proposed solution is a superior method for detecting flow-based anomalies. The performance of our proposed model is also measured in terms of accuracy, recall, and precision. F1 rating and detection time Furthermore, a lightweight model for training is proposed, which selects fewer features while maintaining the model’s performance. Experiments show that the adopted methodology outperforms existing models. [ABSTRACT FROM AUTHOR]

Published
2023
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32. An IoT-Based Network for Smart Urbanization

Author

Saeed, Sabeeh Ahmad, primary, Khan, Farrukh Zeeshan, additional, Iqbal, Zeshan, additional, Alroobaea, Roobaea, additional, Ahmad, Muneer, additional, Talha, Muhammad, additional, Raza, Muhammad Ahsan, additional, and Ali, Ihsan, additional

Published
2021
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34. An Efficient Internet Traffic Classification System Using Deep Learning for IoT.

Author

Umair, Muhammad Basit, Iqbal, Zeshan, Bilal, Muhammad, Nebhen, Jamel, Almohamad, Tarik Adnan, and Mehmood, Raja Majid

Subjects
DEEP learning, INTERNET traffic, INTERNET of things, TRAFFIC engineering, COMPUTER network security, SUPPORT vector machines
Abstract

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique. [ABSTRACT FROM AUTHOR]

Published
2022
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45. (2 S,3 S)-2,6-Dimethylheptane-1,3-diol, the oxygenated side chain of 22( S)-hydroxycholestrol, and its synthetic precursor ( R)-4-benzyl-3-[(2 R,3 S)-3-hydroxy-2,6-dimethylheptanoyl]-1,3-oxazolidin-2-one.

Author

Høgmoen Åstrand, Ove Alexander, Iqbal, Zeshan, Sandberg, Marcel, Kase, Eili T., Görbitz, Carl Henrik, and Rongved, Pål

Subjects
*HEPTANE, *KETONES, *OXAZOLIDINES, *CHIRALITY, *CARBON, *HYDROGEN bonding, *HYDROXYL group
Abstract

(2 S,3 S)-2,6-Dimethylheptane-1,3-diol, C9H20O2, (I), was synthesized from the ketone ( R)-4-benzyl-3-[(2 R,3 S)-3-hydroxy-2,6-dimethylheptanoyl]-1,3-oxazolidin-2-one, C19H27NO4, (II), containing C atoms of known chirality. In both structures, strong hydrogen bonds between the hydroxy groups form tape motifs. The contribution from weaker C-H...O hydrogen bonds is much more evident in the structure of (II), which furthermore contains an example of a direct short O sp3...C sp2 contact that represents a usually unrecognized type of intermolecular interaction. [ABSTRACT FROM AUTHOR]

Published
2013
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46. Electrochemical modulation of sickle cell haemoglobin polymerisation

Author

Iqbal, Zeshan, McKendry, Rachel, Horton, Michael, and Caruana, Daren J.

Abstract

Sickle cell haemoglobin (HbS) differs from normal haemoglobin by a single amino acid in its β chain. This amino acid replacement, from glutamic acid to valine, causes polymerisation of proteins into defined long insoluble fibres with a typical diameter of 21.5 nm. The polymerisation is triggered by the formation of deoxyhaemoglobin (deoxyHb) from oxyhaemoglobin (oxyHb) in low oxygen partial pressures, which results in a conformational change in the secondary structure of the protein. We describe an electrochemical method to modulate the oxygen concentration in an optically transparent thin layer cell to produce deoxyhaemoglobin whilst monitoring the extent of polymerisation using turbidity measurements. The oxygen is depleted in the vicinity of the electrode and triggers the polymerisation. The kinetics of polymerisation were investigated using a model for fibrillogenesis describing a two-step process of nucleation followed by elongation. Rate constants describing the nucleation and growth at monomer concentration of 300 mg cm−3 (4.65 × 10−3 M) were determined to be 9.45 (±0.08) × 10−6 s−1 and 1.22 (±0.03) × 10−3 s−1 respectively, showing that nucleation was far slower than the growth. A similar difference between the rate constants for the nucleation (2.99 (±0.4) × 10−8 s−1) and growth (1.08 (±0.2) × 10−3 s−1) was seen at monomer concentration of 50 mg cm−3 (7.75 × 10−4 M). These results show that nucleation was monomer concentration dependent; however growth was largely independent of monomer concentration. In this study we present a methodology that may be used as a screening method for substances that effect the fibre nucleation and or growth that could be valuable to the pharmaceutical industry for treating sickle cell disease.

Published
2006
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47. Comparative Evaluation of Header vs. Payload based Network Anomaly Detectors.

Author

Cheema, Faisal M., Akram, Adeel, and Iqbal, Zeshan

Subjects
*COMPUTER networks, *BROADBAND communication systems, *COMPUTER security, *COMPUTER crime prevention, *ELECTRONIC data processing
Abstract

With the immense growth of services offered by Internet, the requirement of broadband connectivity has increased significantly in past few years. Organizations and individuals are relying heavily on the internet for their daily communication needs. Consequently, networks have become more prone to different types of network attacks. Intrusion Detection Systems (IDS) offer a method to protect networks against many such attacks. Numerous IDS have been proposed in literature, which employ different techniques to identify attack patterns as well as abrupt changes in network traffic flows. Anomaly detection is a type of Intrusion Detection corresponding to a suite of techniques that can be used to identify novel or "zero-day" attacks against computers and network infrastructure. Different Anomaly-based Intrusion Detection Systems (ADS) work on different principles e.g., a few take into account the packet headers only, where as others operate on payload as well as packet headers. In this paper we evaluate six different ADS; three of them work on packet header only, while remaining three works on both header and payload. We aim to provide a conclusive comparison of these ADS (header only or both header and payload) in terms of accuracy, complexity and detection delay to highlight factors that must be considered while designing IDS in future. The comparison is performed using two real-world labeled datasets to enable cross-reference for future research in this field. In the end of this paper we will conclude that anomaly detectors which work on both header and payload perform better than those ADS which consider only header for intrusion detection. [ABSTRACT FROM AUTHOR]

Published
2009

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