Zeshan Iqbal | University of Engineering and Technology Taxila, Pakistan (original) (raw)
Papers by Zeshan Iqbal
Information Technology and Control, Mar 26, 2022
In modern networks and cloud evolution, as new nodes to internetwork growing rapidly and use of g... more In modern networks and cloud evolution, as new nodes to internetwork growing rapidly and use of gaming and video streaming over the network require high availability with very small latency rate. 5G networks provides much faster services than 4G but link failure occurrence can affect the quality of service. In 5G networks environmental factors also affect the efficiency of wireless signals. To overcome such type of issues, base stations are placed in distributed manner around urban areas when decision is required for the placement. However, in some scenarios we can have few similarities, as in general, highways are same. In existed systems signals distribution is performed homogenously, so it will be generating issues like fractal and environmental. It will be cause of great economic loss. However, being an emerging network paradigm, Software defined Network (SDN) is easy to manage due to logical separation of control plane and data plane. SDN supports numerous advantages, one of them is link robustness to avoid service unavailability. To cope with network link failures there are many mechanisms existed like proactive and reactive mechanisms, but these mechanisms calculate multiple paths and stored in flow tables without considering reliability of link. Therefore, it will be cause of high latency rate due to calculation of many multiple paths and increased traffic overhead too. To overcome these issues, we proposed a new approach in which multipath numbers depend on reliability of primary path. Number of alternative paths decreased as reliability of link increased it leads to less time required to calculate alternative path. In addition, traffic overhead decreases as compared to existing approaches. Secondly, we also integrate the shortest distance factor with reliability factor, and we get better results than existing approaches. Our proposed system will be helpful in increasing the availability of services in 5G network due to low latency rate and small traffic overhead required in link failure recovery.
Information Processing & Management, 2021
Computers, Materials & Continua, 2022
Internet of Things (IoT) defines a network of devices connected to the internet and sharing a mas... more 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.
Advances in Intelligent Systems and Computing, 2019
Electricity is the basic demand of consumers. With the passage of time this demand is increasing ... more Electricity is the basic demand of consumers. With the passage of time this demand is increasing day by day. Smart grid (SG) trying to fulfill the demand of customers. When demand increases then load is also high. To maintain load from on peak hours to off peak hours, consumer needs to manage their appliances by home energy management system (HEMS). HEMS schedule the appliances according to customer’s needs. In this paper, scheme is proposed which is used to minimize the electricity cost and also maximize the user comfort. The proposed scheme is performed better than existing meta heuristic techniques. The proposed scheme is used real time price (RTP) price signal. Simulation results shows that the algorithm has met the objective of DSM. Moreover, the proposed algorithm outperforms earth worm algorithm (EWA) and single swam optimization (SSO) in terms of electricity cost and user comfort.
One of the challenges in Content-Based Image Retrieval (CBIR) is to reduce the semantic gaps betw... more 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 feat...
PLOS ONE, 2016
With the recent evolution of technology, the number of image archives has increased exponentially... more With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration.
The Smart Computing Review, 2012
Vehicular communication systems are a key part of an intelligent transportation system, while veh... more Vehicular communication systems are a key part of an intelligent transportation system, while vehicle safety communication is a major target of vehicular communication. Other features that augment vehicular ad hoc networks are enhanced driving experience, including but not limited to, active navigation and weather information, real-time traffic information and a plethora of other autonomous and automated systems. However, our focus will be warning generation systems, which can help reduce fatalities if deployed in an efficient and fail-safe manner on motorways and highways. This paper describes a collective information system for collection and delivery of traffic information aimed at supporting fast, efficient and secure travel of people and transport of goods. Based on that information, authorities are able to assess vehicles' sudden motion and movement changes and can generate warning/alert messages (for emergency/police vehicles) for post-accident scenarios. In this paper, the use of a radio frequency identification (RFID) system for vehicular communication has been proposed and an extended RFID system and infrastructure for vehicle safety communication through emergency phone towers (EPTs) and cell phones is suggested. In order to communicate, vehicles may be equipped with a cellular phone, RFID, and/or a global positioning system (GPS), whereas RFID readers may be mounted on EPTs, which are already installed on motorways. It also provides a demonstration on flow of information within the system; the simulation results are also included.
Contrast Media & Molecular Imaging, 2022
Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 e... more Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019–22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.
Wireless Communications and Mobile Computing, 2022
Advancement in technology has led to innovation in equipment, and the number of devices is increa... more Advancement in technology has led to innovation in equipment, and the number of devices is increasing every day. Industries are introducing new devices every day and predicting 50 billion connected devices by 2022. These devices are deployed through the Internet, called the Internet of Things (IoT). Applications of IoT devices are weather prediction, monitoring surgery in hospitals, identification of animals using biochips, providing tracking connectivity in automobiles, smart home appliances, etc. IoT devices have limitations related to security at both the software and hardware ends. Secure user interfaces can overcome software-level limitations like front-end-user interfaces are accessed easily through public and private networks. The front-end interfaces are connected to the localized storage to contain data produced by the IoT devices. Localized storage deployed in a closed environment connected to IoT devices is more efficient than online servers from a security perspective. B...
ArXiv, 2021
Internet of Things (IoT) defines a network of devices connected to the internet and sharing a mas... more 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 propos...
Autonomic network management systems are currently in focus seen in the enterprises and are one o... more Autonomic network management systems are currently in focus seen in the enterprises and are one of the prime areas of research in this field. Achieving the complete functionality from the self-management perspective is still unachievable and opens many research areas. In this paper, we introduce ASAALI management system, a comprehensive learning-based intelligent network management system model for autonomous, self-managed, adaptive and secure network management. This novel network management system model is more efficient in all aspects from existing architectures. KEYWORD: Autonomous, network management system, self-directed system, self-managing, self-learning, self-protecting, secure system model
Complex., 2021
Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan Facult... more Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia Department of Information Technology, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia Department of Information Technology, Bahauddin Zakariya University, Multan 60000, Pakistan
With the immense growth of services offered by Internet, the requirement of broadband connectivit... more 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....
Vehicular Ad hoc Networks (VANETs) are the networks of next generation embedded in vehicles getti... more Vehicular Ad hoc Networks (VANETs) are the networks of next generation embedded in vehicles getting focus of researchers due to their wide applicability. VANETs are ad hoc but they are different from MANETs in many aspects like there is no power and computational constraints issue in VANETs. VANETs may be very large scale and nodes move in some organized fashion. In future VANETs communication capabilities will be available almost in all vehicles to make them part of existing network. So people are expecting and efforts are being made to provide all kind of services to VANETs users belonging to safety and comfort. As VANETs are different from MANETs in their nature, so existing communication and routing protocols of MANETs are not entirely suitable for this new kind of mobile networks and new communications and routing protocols are being optimized. In this paper we have made analysis of different existing routing protocols that in case of congestion, these protocols are how much re...
Comput. Electr. Eng., 2021
Cloud computing is a computing paradigm which meets the computational and storage demands of end ... more Cloud computing is a computing paradigm which meets the computational and storage demands of end users. Cloud-based data centers need to continually improve their performance due to exponential increase in service demands. Efficient task scheduling is essential part of cloud computing to achieve maximum throughput, minimum response time, reduced energy consumption and optimal utilization of resources. Bio-inspired algorithms can solve task scheduling difficulties effectively, but they need a lot of computational power and time due to high workload and complexity of the cloud environment. In this research work, Hybrid ant genetic algorithm for task scheduling is proposed. The proposed algorithm adopts features of genetic algorithm and ant colony algorithm and divides tasks and virtual machines into smaller groups. After allocation of tasks, pheromone is added to virtual machines. The proposed algorithm effectively reduces solution space by dividing tasks into groups and by detecting ...
An Autonomous, Self-directed, Ant-optimized Adaptive Learning-based Intelligent Network Architect... more An Autonomous, Self-directed, Ant-optimized Adaptive Learning-based Intelligent Network Architecture (ASAALI) is a self-learning network management system [1] in which the collection and analysis of data from all Autonomous Nodes (AN), for generation of rule-sets was a significantly important but time consuming process. As a solution for efficient analysis and creation of optimized rule-sets an Ant Colony Optimization (ACO) based classifier AntMiner-CC [2] is used based on its performance comparison with other well-known learning based classifiers. Rule-sets are later used by Adaptation and Planning Network layer of ASAALI for imposing decisions over the heterogeneous network environment. Keywords: ASAALI, autonomous network management, Ant-Miner-CC, ACO, learning, classification.
Cloud computing provides multiple services such as computational services, data processing, and r... more Cloud computing provides multiple services such as computational services, data processing, and resource sharing through multiple nodes. These nodes collaborate for all prementioned services in the data center through the head/leader node. This head node is responsible for reliability, higher performance, latency, and deadlock handling and enables the user to access cost-effective computational services. However, the optimal head nodes’ selection is a challenging problem due to consideration of resources such as memory, CPU-MIPS, and bandwidth. The existing methods are monolithic, as they select the head nodes without taking the resources of the nodes. Still, there is a need for the candidate node which can be selected as a head node in case of head node failure. Therefore, in this paper, we proposed a technique, i.e., Head Node Selection Algorithm (HNSA), for optimal head node selection from the data center, which is based on the genetic algorithm (GA). In our proposed method, ther...
Vehicular ad hoc network is a pretty research vibrant area since last decade. It has been success... more Vehicular ad hoc network is a pretty research vibrant area since last decade. It has been successfully used for intelligent transportation system and entertainment purposes for realization of smart cities. However, intermittent connectivity, high routing overhead, inflexible communication infrastructure, unscalable networks, and high packet collision are the key challenges that put hindrances on the wide applications of vehicular ad hoc network. The severity of these challenges become even more intensified when deployed in urban areas. To overcome these hurdles, integrating micro unmanned aerial vehicles with vehicular ad hoc network provides a viable solution. In this article, we proposed an unmanned aerial vehicle–assisted vehicular ad hoc network communication architecture in which unmanned aerial vehicles fly over the deployed area and provide communication services to underlying coverage area. Unmanned aerial vehicle–assisted vehicular ad hoc network avails the advantages of li...
2020 14th International Conference on Open Source Systems and Technologies (ICOSST)
Cloud computing is the backbone of the modern information technology industry. Due to the increas... more Cloud computing is the backbone of the modern information technology industry. Due to the increase in internet usage, social media, and smart phones, a large amount of data is producing. Cloud datacenters can provide resources to handle this data. If data is not properly handled, it can cause overhead on cloud servers and can increase operational costs. Genetic algorithm is used to solve scheduling problem efficiently, but they take a lot of time to find an optimal solution. In this paper, we proposed Flexible Genetic Algorithm Operators (FGAO) for Task Scheduling in Cloud Datacenters. This algorithm changes crossover and mutation operators according to the quality of scheduling solutions. Instead of giving a fixed stopping criteria algorithm uses flexible crossover and mutation operators as a stopping criterion. Experimental results show that the proposed FGAO algorithm reduces 40% execution time and 33% iterations as compared to the genetic algorithm.
Information Technology and Control, Mar 26, 2022
In modern networks and cloud evolution, as new nodes to internetwork growing rapidly and use of g... more In modern networks and cloud evolution, as new nodes to internetwork growing rapidly and use of gaming and video streaming over the network require high availability with very small latency rate. 5G networks provides much faster services than 4G but link failure occurrence can affect the quality of service. In 5G networks environmental factors also affect the efficiency of wireless signals. To overcome such type of issues, base stations are placed in distributed manner around urban areas when decision is required for the placement. However, in some scenarios we can have few similarities, as in general, highways are same. In existed systems signals distribution is performed homogenously, so it will be generating issues like fractal and environmental. It will be cause of great economic loss. However, being an emerging network paradigm, Software defined Network (SDN) is easy to manage due to logical separation of control plane and data plane. SDN supports numerous advantages, one of them is link robustness to avoid service unavailability. To cope with network link failures there are many mechanisms existed like proactive and reactive mechanisms, but these mechanisms calculate multiple paths and stored in flow tables without considering reliability of link. Therefore, it will be cause of high latency rate due to calculation of many multiple paths and increased traffic overhead too. To overcome these issues, we proposed a new approach in which multipath numbers depend on reliability of primary path. Number of alternative paths decreased as reliability of link increased it leads to less time required to calculate alternative path. In addition, traffic overhead decreases as compared to existing approaches. Secondly, we also integrate the shortest distance factor with reliability factor, and we get better results than existing approaches. Our proposed system will be helpful in increasing the availability of services in 5G network due to low latency rate and small traffic overhead required in link failure recovery.
Information Processing & Management, 2021
Computers, Materials & Continua, 2022
Internet of Things (IoT) defines a network of devices connected to the internet and sharing a mas... more 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.
Advances in Intelligent Systems and Computing, 2019
Electricity is the basic demand of consumers. With the passage of time this demand is increasing ... more Electricity is the basic demand of consumers. With the passage of time this demand is increasing day by day. Smart grid (SG) trying to fulfill the demand of customers. When demand increases then load is also high. To maintain load from on peak hours to off peak hours, consumer needs to manage their appliances by home energy management system (HEMS). HEMS schedule the appliances according to customer’s needs. In this paper, scheme is proposed which is used to minimize the electricity cost and also maximize the user comfort. The proposed scheme is performed better than existing meta heuristic techniques. The proposed scheme is used real time price (RTP) price signal. Simulation results shows that the algorithm has met the objective of DSM. Moreover, the proposed algorithm outperforms earth worm algorithm (EWA) and single swam optimization (SSO) in terms of electricity cost and user comfort.
One of the challenges in Content-Based Image Retrieval (CBIR) is to reduce the semantic gaps betw... more 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 feat...
PLOS ONE, 2016
With the recent evolution of technology, the number of image archives has increased exponentially... more With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration.
The Smart Computing Review, 2012
Vehicular communication systems are a key part of an intelligent transportation system, while veh... more Vehicular communication systems are a key part of an intelligent transportation system, while vehicle safety communication is a major target of vehicular communication. Other features that augment vehicular ad hoc networks are enhanced driving experience, including but not limited to, active navigation and weather information, real-time traffic information and a plethora of other autonomous and automated systems. However, our focus will be warning generation systems, which can help reduce fatalities if deployed in an efficient and fail-safe manner on motorways and highways. This paper describes a collective information system for collection and delivery of traffic information aimed at supporting fast, efficient and secure travel of people and transport of goods. Based on that information, authorities are able to assess vehicles' sudden motion and movement changes and can generate warning/alert messages (for emergency/police vehicles) for post-accident scenarios. In this paper, the use of a radio frequency identification (RFID) system for vehicular communication has been proposed and an extended RFID system and infrastructure for vehicle safety communication through emergency phone towers (EPTs) and cell phones is suggested. In order to communicate, vehicles may be equipped with a cellular phone, RFID, and/or a global positioning system (GPS), whereas RFID readers may be mounted on EPTs, which are already installed on motorways. It also provides a demonstration on flow of information within the system; the simulation results are also included.
Contrast Media & Molecular Imaging, 2022
Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 e... more Coronavirus (COVID-19) is a deadly virus that initially starts with flu-like symptoms. COVID-19 emerged in China and quickly spread around the globe, resulting in the coronavirus epidemic of 2019–22. As this virus is very similar to influenza in its early stages, its accurate detection is challenging. Several techniques for detecting the virus in its early stages are being developed. Deep learning techniques are a handy tool for detecting various diseases. For the classification of COVID-19 and influenza, we proposed tailored deep learning models. A publicly available dataset of X-ray images was used to develop proposed models. According to test results, deep learning models can accurately diagnose normal, influenza, and COVID-19 cases. Our proposed long short-term memory (LSTM) technique outperformed the CNN model in the evaluation phase on chest X-ray images, achieving 98% accuracy.
Wireless Communications and Mobile Computing, 2022
Advancement in technology has led to innovation in equipment, and the number of devices is increa... more Advancement in technology has led to innovation in equipment, and the number of devices is increasing every day. Industries are introducing new devices every day and predicting 50 billion connected devices by 2022. These devices are deployed through the Internet, called the Internet of Things (IoT). Applications of IoT devices are weather prediction, monitoring surgery in hospitals, identification of animals using biochips, providing tracking connectivity in automobiles, smart home appliances, etc. IoT devices have limitations related to security at both the software and hardware ends. Secure user interfaces can overcome software-level limitations like front-end-user interfaces are accessed easily through public and private networks. The front-end interfaces are connected to the localized storage to contain data produced by the IoT devices. Localized storage deployed in a closed environment connected to IoT devices is more efficient than online servers from a security perspective. B...
ArXiv, 2021
Internet of Things (IoT) defines a network of devices connected to the internet and sharing a mas... more 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 propos...
Autonomic network management systems are currently in focus seen in the enterprises and are one o... more Autonomic network management systems are currently in focus seen in the enterprises and are one of the prime areas of research in this field. Achieving the complete functionality from the self-management perspective is still unachievable and opens many research areas. In this paper, we introduce ASAALI management system, a comprehensive learning-based intelligent network management system model for autonomous, self-managed, adaptive and secure network management. This novel network management system model is more efficient in all aspects from existing architectures. KEYWORD: Autonomous, network management system, self-directed system, self-managing, self-learning, self-protecting, secure system model
Complex., 2021
Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan Facult... more Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur 50603, Malaysia Department of Information Technology, College of Computer and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia Department of Information Technology, Bahauddin Zakariya University, Multan 60000, Pakistan
With the immense growth of services offered by Internet, the requirement of broadband connectivit... more 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....
Vehicular Ad hoc Networks (VANETs) are the networks of next generation embedded in vehicles getti... more Vehicular Ad hoc Networks (VANETs) are the networks of next generation embedded in vehicles getting focus of researchers due to their wide applicability. VANETs are ad hoc but they are different from MANETs in many aspects like there is no power and computational constraints issue in VANETs. VANETs may be very large scale and nodes move in some organized fashion. In future VANETs communication capabilities will be available almost in all vehicles to make them part of existing network. So people are expecting and efforts are being made to provide all kind of services to VANETs users belonging to safety and comfort. As VANETs are different from MANETs in their nature, so existing communication and routing protocols of MANETs are not entirely suitable for this new kind of mobile networks and new communications and routing protocols are being optimized. In this paper we have made analysis of different existing routing protocols that in case of congestion, these protocols are how much re...
Comput. Electr. Eng., 2021
Cloud computing is a computing paradigm which meets the computational and storage demands of end ... more Cloud computing is a computing paradigm which meets the computational and storage demands of end users. Cloud-based data centers need to continually improve their performance due to exponential increase in service demands. Efficient task scheduling is essential part of cloud computing to achieve maximum throughput, minimum response time, reduced energy consumption and optimal utilization of resources. Bio-inspired algorithms can solve task scheduling difficulties effectively, but they need a lot of computational power and time due to high workload and complexity of the cloud environment. In this research work, Hybrid ant genetic algorithm for task scheduling is proposed. The proposed algorithm adopts features of genetic algorithm and ant colony algorithm and divides tasks and virtual machines into smaller groups. After allocation of tasks, pheromone is added to virtual machines. The proposed algorithm effectively reduces solution space by dividing tasks into groups and by detecting ...
An Autonomous, Self-directed, Ant-optimized Adaptive Learning-based Intelligent Network Architect... more An Autonomous, Self-directed, Ant-optimized Adaptive Learning-based Intelligent Network Architecture (ASAALI) is a self-learning network management system [1] in which the collection and analysis of data from all Autonomous Nodes (AN), for generation of rule-sets was a significantly important but time consuming process. As a solution for efficient analysis and creation of optimized rule-sets an Ant Colony Optimization (ACO) based classifier AntMiner-CC [2] is used based on its performance comparison with other well-known learning based classifiers. Rule-sets are later used by Adaptation and Planning Network layer of ASAALI for imposing decisions over the heterogeneous network environment. Keywords: ASAALI, autonomous network management, Ant-Miner-CC, ACO, learning, classification.
Cloud computing provides multiple services such as computational services, data processing, and r... more Cloud computing provides multiple services such as computational services, data processing, and resource sharing through multiple nodes. These nodes collaborate for all prementioned services in the data center through the head/leader node. This head node is responsible for reliability, higher performance, latency, and deadlock handling and enables the user to access cost-effective computational services. However, the optimal head nodes’ selection is a challenging problem due to consideration of resources such as memory, CPU-MIPS, and bandwidth. The existing methods are monolithic, as they select the head nodes without taking the resources of the nodes. Still, there is a need for the candidate node which can be selected as a head node in case of head node failure. Therefore, in this paper, we proposed a technique, i.e., Head Node Selection Algorithm (HNSA), for optimal head node selection from the data center, which is based on the genetic algorithm (GA). In our proposed method, ther...
Vehicular ad hoc network is a pretty research vibrant area since last decade. It has been success... more Vehicular ad hoc network is a pretty research vibrant area since last decade. It has been successfully used for intelligent transportation system and entertainment purposes for realization of smart cities. However, intermittent connectivity, high routing overhead, inflexible communication infrastructure, unscalable networks, and high packet collision are the key challenges that put hindrances on the wide applications of vehicular ad hoc network. The severity of these challenges become even more intensified when deployed in urban areas. To overcome these hurdles, integrating micro unmanned aerial vehicles with vehicular ad hoc network provides a viable solution. In this article, we proposed an unmanned aerial vehicle–assisted vehicular ad hoc network communication architecture in which unmanned aerial vehicles fly over the deployed area and provide communication services to underlying coverage area. Unmanned aerial vehicle–assisted vehicular ad hoc network avails the advantages of li...
2020 14th International Conference on Open Source Systems and Technologies (ICOSST)
Cloud computing is the backbone of the modern information technology industry. Due to the increas... more Cloud computing is the backbone of the modern information technology industry. Due to the increase in internet usage, social media, and smart phones, a large amount of data is producing. Cloud datacenters can provide resources to handle this data. If data is not properly handled, it can cause overhead on cloud servers and can increase operational costs. Genetic algorithm is used to solve scheduling problem efficiently, but they take a lot of time to find an optimal solution. In this paper, we proposed Flexible Genetic Algorithm Operators (FGAO) for Task Scheduling in Cloud Datacenters. This algorithm changes crossover and mutation operators according to the quality of scheduling solutions. Instead of giving a fixed stopping criteria algorithm uses flexible crossover and mutation operators as a stopping criterion. Experimental results show that the proposed FGAO algorithm reduces 40% execution time and 33% iterations as compared to the genetic algorithm.