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Papers by Wathiq Laftah Al-Yaseen
Karbala International Journal of Modern Science
Obviously, the increasing threats to network security, which led to devastating network attacks, ... more Obviously, the increasing threats to network security, which led to devastating network attacks, have taken a heavy toll on enterprises as a simple firewall cannot prevent complex and changing attacks. Therefore, companies should use intrusion detection systems in combination with other security devices to protect against corporate network security issues. In fact, intrusion detection is a system whose primary function is to protect network security by monitoring traffic, collecting and analyzing information, and then issuing an alert in cases where the output of the analysis represents a threat to network security. Intrusion Detection Systems (IDS) can stop unauthorized activity on a network or operating system, react automatically, stop the intrusion's source in time, record it, and alert the network administrator to ensure maximum system security. The process of detecting attacks using a single algorithm has not proven its worth. Therefore, several algorithms were used together by using ensemble learning. To elaborate, ensemble learning is a well-known predictive technique that involves training multiple algorithms to treat the same problem, after which the results are combined to produce a single, potent prediction that can provide performance better than that of a single algorithm. The primary goal of this study is to present an overview of the main ensemble techniques that are used to enhance the effectiveness of the intrusion detection system, as well as the research using these methods as published by Elsevier and Springer from 2018 until the time being. The results prove that the two easiest methods within ensemble learning to implement are majority voting and weighted averaging, which provide good results in terms of accuracy. In cases where the base models have a significant variance, the bagging method would be more beneficial, while the boosting method would be used in cases where the basic models are biased, and in order to lower bias by learning different algorithms, the stacking ensemble methods are used.
It is important to recognize protein classes in order to understand folding patterns. In this pap... more It is important to recognize protein classes in order to understand folding patterns. In this paper, we have proposed a method to extract the features based on secondary structure sequence and hydropathy profile. A feature selection algorithm that combines particle swarm optimization and extreme learning machine was employed to select a total of 25 features. The selected features were fed to the classifier in order to classify each protein to an appropriate class. The well-known data sets, i.e. ASTRALtraining, ASTRALtest, 25PDB, 640 and 1189 were used to evaluate the proposed method. Upon comparing the current approach against other approaches based on the same data, it is evident that the proposed method shows higher efficiency in the prediction of structural class of protein, and its overall accuracy reaches up to 1.5%. Moreover, the extracted secondary and hydropathy features are important for us to differentiate the α/β and α+β classes. تصلاخلا شًُُحو ىهف ٍف ٍسبسا رود بعهح ثبُُح...
2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019
In the Internet of Things (IoTs) future, the Wireless Sensor Networks (WSNs) represent one of the... more In the Internet of Things (IoTs) future, the Wireless Sensor Networks (WSNs) represent one of the big data contributors due to the wide range of real-life applications that use this type of networks. The data volume increases in unexpected ratio. The dense WSN can lead to an increase in the redundant data in the gathered measures of the sensor node. Therefore, it is essential to apply energy-efficient data aggregation to remove the data redundancy and maintain a suitable rate of accuracy. This paper proposes an Integrated Divide and Conquer with Enhanced K-means technique (IDiCoEK) for energy-saving data aggregation in WSNs. The IDiCoEK aggregates the measures in two levels: the node and cluster head levels. A divide and conquer algorithm is applied at the sensor node to remove the redundant data from the collected measures and then send it to the cluster head. The cluster head applies an enhanced K-means approach for clustering the received data sets from the sensor nodes into groups of near similar sets and then the best representative set will be sent to the base station from each group. The IDiCoEK performance is assessed using OMNeT++ network simulator with real data readings of sensor nodes. Results demonstrate that our IDiCoEK technique can save energy by decreasing the measures sent to the sink whilst conserving a suitable level of data accuracy at the sink node.
The nowadays growing of threads and intrusions on networks make the need for developing efficient... more The nowadays growing of threads and intrusions on networks make the need for developing efficient and effective intrusion detection systems a necessity. Powerful solutions of intrusion detection systems should be capable of dealing with central network issues such as huge data, high-speed traffic, and wide variety in threat types. This paper proposes a wrapper feature selection method that is based on firefly algorithm and support vector machine. The firefly optimization algorithm has been effectively employed in diverse combinatorial problems. The proposed method improves the performance of intrusion detection by removing the irrelevant features and reduces the time of classification by reducing the dimension of data. The SVM model was employed to evaluate each of the feature subsets produced from firefly technique. The main merit of the proposed method is its ability in modifying the firefly algorithm to become suitable for selection of features. To validate the proposed approach,...
International Review of Management and Marketing, 2016
One of the Business Intelligent solutions that are currently in use is the Multi-Agent System (MA... more One of the Business Intelligent solutions that are currently in use is the Multi-Agent System (MAS). Communication is one of the most important elements in MAS, especially for exchanging large low level data between distributed agents (physically). The Agent Communication Language in JADE has been offered as a secure method for sending data, whereby the data is defined as an object. However, the object cannot be used to send data to another agent in a different machine. Therefore, the aim of this paper was to propose a method for the exchange of large low level data as an object by creating a proxy agent known as a Delivery Agent, which temporarily imitates the Receiver Agent. The results showed that the proposed method is able to send large-sized data. The experiments were conducted using 16 datasets ranging from 100,000 to 7 million instances. However, for the proposed method, the RAM and the CPU machine had to be slightly increased for the Receiver Agent, but the latency time was...
The growth of network attacks has lengthened the intrusion detection system’s (IDS) processing ti... more The growth of network attacks has lengthened the intrusion detection system’s (IDS) processing time to detect these attacks. The demand for reducing the processing time has increased when dealing with real time IDS. Several methods were proposed, such as improving the algorithm, or improving the IDS’s architectural design; which includes distributed and parallel. However, this paper sought to present a Multi-agent System solution (MAS-IDS) to enhance the performance of IDS in order to reduce the analysis of the network’s traffic data processing time when detecting attacks. Numerous works of MAS improved the accuracy of IDS, however, only a few had focused on enhancing the processing time of IDS. The number of analysis agents that can be created in a system depends upon the size of traffic data and the availability of logical processors (cores) in the system, without affecting the performance of the hosts with less targeted time. The conducted experiments employed the dataset KDDCUP&...
2018 IEEE Middle East and North Africa Communications Conference (MENACOMM)
One of the big data provider in the future of the Internet of Things (IoT) is the Periodic Wirele... more One of the big data provider in the future of the Internet of Things (IoT) is the Periodic Wireless Sensor Networks (PWSNs) because of the widespread use of this type of networks in various real life applications. The amount of data clearly grows at an unexpected rate. The high-density deployment of the sensor nodes will lead to high data redundancy in the collected readings of the sensor nodes. An energy-saving data aggregation may be an essential way to remove the data redundancy. In this article, we propose a Distributed Data Aggregation based Modified K-means (DiDAMoK) Technique for enhancement the lifetime of the PWSNs. DiDAMoK is distributed inside each sensor node. It works into periods. Each period is composed of three stages. First, the sensor readings are collected and saved in the sensor node. Second, the modified K-means is employed on these readings to convert them into clusters of readings. The number of clusters is dynamic and depends on the nature of collected readings. In the third stage, One representative reading of each cluster will be transmitted to the sink. The performance of the DiDAMoK technique is evaluated using OMNeT++ network simulator and based on real sensed data of a WSN. Simulation results explain that our DiDAMoK technique can efficiently decrease the consumed energy of the whole PWSN due to reducing the sensed readings number transmitted to the sink node while keeping a suitable data accuracy at the sink.
International Journal of Advanced Intelligence Paradigms
International Journal of Intelligent Engineering and Systems
Indonesian Journal of Electrical Engineering and Computer Science
In general, multidimensional data (mobile application for example) contain a large number of unne... more In general, multidimensional data (mobile application for example) contain a large number of unnecessary information. Web app users find it difficult to get the information needed quickly and effectively due to the sheer volume of data (big data produced per second). In this paper, we tend to study the data mining in web personalization using blended deep learning model. So, one of the effective solutions to this problem is web personalization. As well as, explore how this model helps to analyze and estimate the huge amounts of operations. Providing personalized recommendations to improve reliability depends on the web application using useful information in the web application. The results of this research are important for the training and testing of large data sets for a map of deep mixed learning based on the model of back-spread neural network. The HADOOP framework was used to perform a number of experiments in a different environment with a learning rate between -1 and +1. Als...
Procedia Computer Science
International Journal of Advanced Intelligence Paradigms
Expert Systems with Applications, 2017
Communications in Computer and Information Science, 2015
The Scientific World Journal, 2015
Presently, the processing time and performance of intrusion detection systems are of great import... more Presently, the processing time and performance of intrusion detection systems are of great importance due to the increased speed of traffic data networks and a growing number of attacks on networks and computers. Several approaches have been proposed to address this issue, including hybridizing with several algorithms. However, this paper aims at proposing a hybrid of modifiedK-means with C4.5 intrusion detection system in a multiagent system (MAS-IDS). The MAS-IDS consists of three agents, namely, coordinator, analysis, and communication agent. The basic concept underpinning the utilized MAS is dividing the large captured network dataset into a number of subsets and distributing these to a number of agents depending on the data network size and core CPU availability. KDD Cup 1999 dataset is used for evaluation. The proposed hybrid modifiedK-means with C4.5 classification in MAS is developed in JADE platform. The results show that compared to the current methods, the MAS-IDS reduces...
Pattern Recognition Letters, 2017
Karbala International Journal of Modern Science
Obviously, the increasing threats to network security, which led to devastating network attacks, ... more Obviously, the increasing threats to network security, which led to devastating network attacks, have taken a heavy toll on enterprises as a simple firewall cannot prevent complex and changing attacks. Therefore, companies should use intrusion detection systems in combination with other security devices to protect against corporate network security issues. In fact, intrusion detection is a system whose primary function is to protect network security by monitoring traffic, collecting and analyzing information, and then issuing an alert in cases where the output of the analysis represents a threat to network security. Intrusion Detection Systems (IDS) can stop unauthorized activity on a network or operating system, react automatically, stop the intrusion's source in time, record it, and alert the network administrator to ensure maximum system security. The process of detecting attacks using a single algorithm has not proven its worth. Therefore, several algorithms were used together by using ensemble learning. To elaborate, ensemble learning is a well-known predictive technique that involves training multiple algorithms to treat the same problem, after which the results are combined to produce a single, potent prediction that can provide performance better than that of a single algorithm. The primary goal of this study is to present an overview of the main ensemble techniques that are used to enhance the effectiveness of the intrusion detection system, as well as the research using these methods as published by Elsevier and Springer from 2018 until the time being. The results prove that the two easiest methods within ensemble learning to implement are majority voting and weighted averaging, which provide good results in terms of accuracy. In cases where the base models have a significant variance, the bagging method would be more beneficial, while the boosting method would be used in cases where the basic models are biased, and in order to lower bias by learning different algorithms, the stacking ensemble methods are used.
It is important to recognize protein classes in order to understand folding patterns. In this pap... more It is important to recognize protein classes in order to understand folding patterns. In this paper, we have proposed a method to extract the features based on secondary structure sequence and hydropathy profile. A feature selection algorithm that combines particle swarm optimization and extreme learning machine was employed to select a total of 25 features. The selected features were fed to the classifier in order to classify each protein to an appropriate class. The well-known data sets, i.e. ASTRALtraining, ASTRALtest, 25PDB, 640 and 1189 were used to evaluate the proposed method. Upon comparing the current approach against other approaches based on the same data, it is evident that the proposed method shows higher efficiency in the prediction of structural class of protein, and its overall accuracy reaches up to 1.5%. Moreover, the extracted secondary and hydropathy features are important for us to differentiate the α/β and α+β classes. تصلاخلا شًُُحو ىهف ٍف ٍسبسا رود بعهح ثبُُح...
2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), 2019
In the Internet of Things (IoTs) future, the Wireless Sensor Networks (WSNs) represent one of the... more In the Internet of Things (IoTs) future, the Wireless Sensor Networks (WSNs) represent one of the big data contributors due to the wide range of real-life applications that use this type of networks. The data volume increases in unexpected ratio. The dense WSN can lead to an increase in the redundant data in the gathered measures of the sensor node. Therefore, it is essential to apply energy-efficient data aggregation to remove the data redundancy and maintain a suitable rate of accuracy. This paper proposes an Integrated Divide and Conquer with Enhanced K-means technique (IDiCoEK) for energy-saving data aggregation in WSNs. The IDiCoEK aggregates the measures in two levels: the node and cluster head levels. A divide and conquer algorithm is applied at the sensor node to remove the redundant data from the collected measures and then send it to the cluster head. The cluster head applies an enhanced K-means approach for clustering the received data sets from the sensor nodes into groups of near similar sets and then the best representative set will be sent to the base station from each group. The IDiCoEK performance is assessed using OMNeT++ network simulator with real data readings of sensor nodes. Results demonstrate that our IDiCoEK technique can save energy by decreasing the measures sent to the sink whilst conserving a suitable level of data accuracy at the sink node.
The nowadays growing of threads and intrusions on networks make the need for developing efficient... more The nowadays growing of threads and intrusions on networks make the need for developing efficient and effective intrusion detection systems a necessity. Powerful solutions of intrusion detection systems should be capable of dealing with central network issues such as huge data, high-speed traffic, and wide variety in threat types. This paper proposes a wrapper feature selection method that is based on firefly algorithm and support vector machine. The firefly optimization algorithm has been effectively employed in diverse combinatorial problems. The proposed method improves the performance of intrusion detection by removing the irrelevant features and reduces the time of classification by reducing the dimension of data. The SVM model was employed to evaluate each of the feature subsets produced from firefly technique. The main merit of the proposed method is its ability in modifying the firefly algorithm to become suitable for selection of features. To validate the proposed approach,...
International Review of Management and Marketing, 2016
One of the Business Intelligent solutions that are currently in use is the Multi-Agent System (MA... more One of the Business Intelligent solutions that are currently in use is the Multi-Agent System (MAS). Communication is one of the most important elements in MAS, especially for exchanging large low level data between distributed agents (physically). The Agent Communication Language in JADE has been offered as a secure method for sending data, whereby the data is defined as an object. However, the object cannot be used to send data to another agent in a different machine. Therefore, the aim of this paper was to propose a method for the exchange of large low level data as an object by creating a proxy agent known as a Delivery Agent, which temporarily imitates the Receiver Agent. The results showed that the proposed method is able to send large-sized data. The experiments were conducted using 16 datasets ranging from 100,000 to 7 million instances. However, for the proposed method, the RAM and the CPU machine had to be slightly increased for the Receiver Agent, but the latency time was...
The growth of network attacks has lengthened the intrusion detection system’s (IDS) processing ti... more The growth of network attacks has lengthened the intrusion detection system’s (IDS) processing time to detect these attacks. The demand for reducing the processing time has increased when dealing with real time IDS. Several methods were proposed, such as improving the algorithm, or improving the IDS’s architectural design; which includes distributed and parallel. However, this paper sought to present a Multi-agent System solution (MAS-IDS) to enhance the performance of IDS in order to reduce the analysis of the network’s traffic data processing time when detecting attacks. Numerous works of MAS improved the accuracy of IDS, however, only a few had focused on enhancing the processing time of IDS. The number of analysis agents that can be created in a system depends upon the size of traffic data and the availability of logical processors (cores) in the system, without affecting the performance of the hosts with less targeted time. The conducted experiments employed the dataset KDDCUP&...
2018 IEEE Middle East and North Africa Communications Conference (MENACOMM)
One of the big data provider in the future of the Internet of Things (IoT) is the Periodic Wirele... more One of the big data provider in the future of the Internet of Things (IoT) is the Periodic Wireless Sensor Networks (PWSNs) because of the widespread use of this type of networks in various real life applications. The amount of data clearly grows at an unexpected rate. The high-density deployment of the sensor nodes will lead to high data redundancy in the collected readings of the sensor nodes. An energy-saving data aggregation may be an essential way to remove the data redundancy. In this article, we propose a Distributed Data Aggregation based Modified K-means (DiDAMoK) Technique for enhancement the lifetime of the PWSNs. DiDAMoK is distributed inside each sensor node. It works into periods. Each period is composed of three stages. First, the sensor readings are collected and saved in the sensor node. Second, the modified K-means is employed on these readings to convert them into clusters of readings. The number of clusters is dynamic and depends on the nature of collected readings. In the third stage, One representative reading of each cluster will be transmitted to the sink. The performance of the DiDAMoK technique is evaluated using OMNeT++ network simulator and based on real sensed data of a WSN. Simulation results explain that our DiDAMoK technique can efficiently decrease the consumed energy of the whole PWSN due to reducing the sensed readings number transmitted to the sink node while keeping a suitable data accuracy at the sink.
International Journal of Advanced Intelligence Paradigms
International Journal of Intelligent Engineering and Systems
Indonesian Journal of Electrical Engineering and Computer Science
In general, multidimensional data (mobile application for example) contain a large number of unne... more In general, multidimensional data (mobile application for example) contain a large number of unnecessary information. Web app users find it difficult to get the information needed quickly and effectively due to the sheer volume of data (big data produced per second). In this paper, we tend to study the data mining in web personalization using blended deep learning model. So, one of the effective solutions to this problem is web personalization. As well as, explore how this model helps to analyze and estimate the huge amounts of operations. Providing personalized recommendations to improve reliability depends on the web application using useful information in the web application. The results of this research are important for the training and testing of large data sets for a map of deep mixed learning based on the model of back-spread neural network. The HADOOP framework was used to perform a number of experiments in a different environment with a learning rate between -1 and +1. Als...
Procedia Computer Science
International Journal of Advanced Intelligence Paradigms
Expert Systems with Applications, 2017
Communications in Computer and Information Science, 2015
The Scientific World Journal, 2015
Presently, the processing time and performance of intrusion detection systems are of great import... more Presently, the processing time and performance of intrusion detection systems are of great importance due to the increased speed of traffic data networks and a growing number of attacks on networks and computers. Several approaches have been proposed to address this issue, including hybridizing with several algorithms. However, this paper aims at proposing a hybrid of modifiedK-means with C4.5 intrusion detection system in a multiagent system (MAS-IDS). The MAS-IDS consists of three agents, namely, coordinator, analysis, and communication agent. The basic concept underpinning the utilized MAS is dividing the large captured network dataset into a number of subsets and distributing these to a number of agents depending on the data network size and core CPU availability. KDD Cup 1999 dataset is used for evaluation. The proposed hybrid modifiedK-means with C4.5 classification in MAS is developed in JADE platform. The results show that compared to the current methods, the MAS-IDS reduces...
Pattern Recognition Letters, 2017