A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This g... more A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This growth is considered deadly since it may cause death. The brain controls numerous functions, such as memory, vision, and emotions. Due to the location, size, and shape of these tumors, their detection is a challenging and complex task. Several efforts have been conducted toward improved detection and yielded promising results and outcomes. However, the accuracy should be higher than what has been reached. This paper presents a method to detect brain tumors with high accuracy. The method works using an image segmentation technique and a classifier in MATLAB. The utilized classifier is a Support Vector Machine (SVM). Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) are also involved. A dataset from the Kaggle website is used to test the developed approach. The obtained results reached nearly 99.2% of accuracy. The paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the literature. This evaluation shows that the presented system outperforms other approaches regarding the accuracy, precision, and recall. This research discovered that the developed method is extremely useful in detecting brain tumors, given the high accuracy, precision, and recall results. The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
IEEE Transactions on Dependable and Secure Computing, 2019
With the growing amount of cyber threats, the need for development of high-assurance cyber system... more With the growing amount of cyber threats, the need for development of high-assurance cyber systems is becoming increasingly important. The objective of this paper is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved attacks. We present the interleaving concept and investigate how interleaving multiple attacks can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop two architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a database of HMM templates of known attacks and exhibit varying performance and complexity. For performance evaluation, in the presence of multiple multi-stage attack scenarios, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are used to demonstrate the efficacy of the proposed architectures.
The growing reliance on Cyber technology for Smart Grid and other Cyber-Physical Systems (CPS) ap... more The growing reliance on Cyber technology for Smart Grid and other Cyber-Physical Systems (CPS) applications increases high assurance challenges. Advanced Metering Infrastructure (AMI) is a critical part of smart grid and failure to address security risks in it might disrupt utilities in their mission to deliver power reliably. The objective of this paper is to find mitigation actions for attacks that might be launched against AMI. The paper introduces a tool called SecAMI that calculates the relationship between the speed at which an attack spreads, the speed at which it can be detected, and the consequences on the availability of the AMI. The results from SecAMI can be used as a performance metric to significantly improve the development and the deployment of Intrusion Detection Systems (IDSs) in an AMI environment. Experimental results with an IDS called Amilyzer show that centralized IDS might not work efficiently in scalable systems comparing with distributed IDS in term of detection time, especially with a Distributed Denial of Service (DDoS) attack or remote disconnects on the AMI.
Health providers use the ECG machine to get information about the heart. This information plays a... more Health providers use the ECG machine to get information about the heart. This information plays a significant role since it tells them about the status of the heart. The ECG machine presents this information in a waveform. During the Covid-19 pandemic, all governments have placed numerous rules and policies to protect people from the virus and from spreading it. One of the rules and policies is to prevent touching surfaces in public places. However, in health care centers, touching surfaces can't be avoided completely since there is a need to touch them or place some wires on the human body such as placing wires to use the ECG machine. In Saudi Arabia, the government has placed a policy in all its buildings, public places, and the private sector to measure the temperature at the entrance. Due to this situation, the idea has come into mind to have a touchless method to measure the heartbeat rate. In this paper, proposing a feasible and reliable method to estimate a continuous heartbeat rate is presented. It uses a face recognition approach to predict the heart pulse continuously in real-time according to colors intensity measurement. Using a segmentation algorithm is involved since the approach takes its input from a video or an image. Several experiments have been conducted on volunteers to verify the obtained results and measure their relative errors. Consequently, the errors are less than 7% which is quite acceptable. At the end of this article, a comparative assessment is performed between the presented approach and some works from literature. This assessment is conducted based on the methodologies being utilized and applied and Mean Absolute Error (MAE). Furthermore, it shows whether those methods require physical contact or not. The obtained results indicate that the implemented system herein outperforms other state-of-the-art methods.
Saudi Arabia has started building smart cities and communities as part of the Saudi 2030 vision, ... more Saudi Arabia has started building smart cities and communities as part of the Saudi 2030 vision, which aims to digitalize all services. Smart cities use different types of technologies and data to improve the quality of life for citizens, manage resources, and make operations more efficient. In big cities such as Riyadh and Jeddah, the number of vehicles on the road has dramatically increased. Hence, parking has become a problem since there are limited spaces available. In this article, a novel, intelligent, and automated method for vehicle parking and management is proposed. This approach employs a convolutional neural network (CNN) tool to train the algorithm deeply. Image segmentation and preprocessing techniques are employed as well. All operations are automated and cost-effective since the proposed smart parking management system utilizes only a single camera to provide real-time views of the status of a parking lot. Furthermore, there is no need for human interference, and it is easy to maintain. Several simulation scenarios were conducted on MATLAB to validate this approach and prove its efficiency. A comparative evaluation between the proposed system and some works of literature is provided, and it indicates that the developed system outperforms the works from the preexisting literature.
Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover... more Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information. Precision agriculture uses data mining to advance agricultural developments. Many farmers aren't getting the most out of their land because they don't use precision agriculture. They harvest crops without a well-planned recommendation system. Future crop production is calculated by combining environmental conditions and management behavior, yielding numerical and categorical data. Most existing research still needs to address data preprocessing and crop categorization/classification. Furthermore, statistical analysis receives less attention, despite producing more accurate and valid results. The study was conducted on a dataset about Karnataka state, India, with crops of eight parameters taken into account, namely the minimum amount of fertilizers required, such as nitrogen, phosphorus, potassium, and pH values. The research considers rainfall, season, soil type, and temperature parameters to provide precise cultivation recommendations for high productivity. The presented algorithm converts discrete numerals to factors first, then reduces levels. Second, the algorithm generates six datasets, two from Case-1 (dataset with many numeric variables), two from Case-2 (dataset with many categorical variables), and one from Case-3 (dataset with reduced factor variables). Finally, the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda estimation. The presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions, soil nutrients, and geo-location. Finally, the prepared dataset solves the classification problem, leading to a model evaluation that selects the best dataset for precise crop prediction.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
With the growing amount of cyber threats, the need for development of high-assurance cyber system... more With the growing amount of cyber threats, the need for development of high-assurance cyber systems is becoming increasingly important. The objective of this paper is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved attacks. We present the interleaving concept and investigate how interleaving multiple attacks can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop two architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a database of HMM templates of known attacks and exhibit varying performance and complexity. For performance evaluation, in the presence of multiple multi-stage attack scenarios, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are used to demonstrate the efficacy of the proposed architectures.
Lung cancer is the leading cause of cancer-related death around the globe. The treatment and surv... more Lung cancer is the leading cause of cancer-related death around the globe. The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis. Most diagnostic techniques can identify and classify only one type of lung cancer. It is crucial to close this gap with a system that detects all lung cancer types. This paper proposes an intelligent decision support system for this purpose. This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives. Its algorithm uses a Convolutional Neural Network (CNN) tool to perform deep learning and a Random Forest Algorithm (RFA) to help classify the type of cancer present using several extracted features, including histograms and energy. Numerous simulation experiments were conducted on MATLAB, evidencing that this system achieves 98.7% accuracy and over 98% precision and recall. A comparative assessment assessing accuracy, recall, precision, specificity, and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered metrics. This study found that using CNNs and RFAs is highly effective in detecting lung cancer, given the high accuracy, precision, and recall results. These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Nowadays, the pace of coordinated cyber security crimes has become drastically more rapid, and ne... more Nowadays, the pace of coordinated cyber security crimes has become drastically more rapid, and network attacks have become more advanced and diversified. The explosive growth of network security threats poses serious challenges for building secure Cyber-based Systems (CBS). Existing studies have addressed a breadth of challenges related to detecting network attacks. However, there is still a lack of studies on the detection of sophisticated Multi-stage Attacks (MSAs). The objective of this dissertation is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved MSAs. We present the interleaving concept and investigate how interleaving multiple MSAs can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop three architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a set of HMM templates of known attacks and exhibit varying performance and complexity. For performance evaluation, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are conducted to demonstrate the efficacy of the proposed architecture in the presence of multiple multi-stage attack scenarios, and in the presence of false alerts with various rates.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Computational Intelligence and Neuroscience, Aug 29, 2022
Leukemia cancer is the most common type of cancer that occurs in childhood. e most common types a... more Leukemia cancer is the most common type of cancer that occurs in childhood. e most common types are acute lymphocytic leukemia (ALL) and acute myelogenous leukemia (AML) which a ect children and adults, respectively. Several health issues occur due to these cancers. Leukemia a ects the bone marrow or the lymph nodes. Leukemia produces abnormal white blood cells via the bone marrow system. e a ected white blood cells are unable to perform their tasks properly. Detecting leukemia usually requires taking a blood smear from a patient and working with expert hematologists who analyze the smear with a microscope. In this paper, a method to detect ALL and AML using a deep learner classi er is developed and proposed. e method detects both types, determines their severity, and creates a message that recommends next steps to patients. is approach works based on image segmentation and a convolutional neural network (CNN) tool called AlexNet. e obtained results from the proposed approach and using MATLAB reached more than 98% accuracy. e margin exists because several operations are needed to fully detect the blood cancer. A dataset of leukemia from the Kaggle site is used to test the developed method and illustrate its e ectiveness. is dataset is C-NMC_Leukemia, and it consists of nearly 10 GB worth of 15,000 images. A confusion matrix of testing images is provided to prove the correctness of the presented approach. Furthermore, a comparative analysis between the proposed algorithm and some works from the literature is presented. is analysis compares the method used to extract features, the classi er that is utilized, the accuracy, the precision, and the recall. e obtained results indicate that the proposed method outperforms other works and produces better results.
A Taguchi particle swarm optimization (TPSO) with a three-layer feedforward artificial neural net... more A Taguchi particle swarm optimization (TPSO) with a three-layer feedforward artificial neural network (ANN) is used to model and optimize the chemical composition of a steel bar. The novel contribution of a TPSO is the use of a Taguchi method mechanism to exploit better solutions in the search space through iterations, the use of the conventional non-linear PSO to increase convergence speed, and the use of random movement for particle diversity. The exploration and exploitation capability of the TPSO were confirmed by performance comparisons with other PSO-based algorithms in solving high-dimensional global numerical optimization problems. Experiments in this paper showed that the TPSO provides higher computational efficiency and higher robustness when solving problems involving seven non-linear benchmark functions, including three unimodal functions, one multimodal functions, two rotated functions, and one shifted functions. The results for the computational experiments show that the TPSO outperforms other PSO-based algorithms reported in the literature. Finally, the results obtained by a TPSO-based ANN model of the chemical composition of the steel bar were consistent with the actual data. That is, the proposed TPSO with three-layer feedforward ANN can be used in practical applications. INDEX TERMS Taguchi method, particle swarm optimization, feedforward artificial neural network, chemical composition of steel bar, yield point, tensile strength.
Emerging cyber-physical systems incorporate systems of systems that have functional interdependen... more Emerging cyber-physical systems incorporate systems of systems that have functional interdependencies. With the increase in complexity of the cyberphysical systems, the attack surface also expands, making cyber-physical systems more vulnerable to cyber-attacks. The functional interdependencies exacerbate the security risk as a cyber-attack that compromises one constituent system of a cyber-physical system can disseminate to others. This can result in a cascade effect that can impair the operability of the whole cyber-physical system. In this article, we present a novel security architecture that localizes the cyber-attack in a timely manner, and simultaneously recovers the affected cyber-physical system functionality. We have evaluated the performance of the architecture for advanced metering infrastructure-based pricing cyber-attacks scenario. The simulation results exhibit the effectiveness of the proposed architecture in containing the attack in terms of system availability and its impact on the electric load distribution in the power grid. The increase in popularity of automated systems has made cyber-physical systems (CPSs) find their application in many diverse domains, including electrical power grid, automotive systems, smarthome systems and healthcare systems. Emerging CPSs integrate heterogeneous systems that have functional interdependencies. These integrated heterogeneous systems effectuate discrete functions and collaborate in an intricate fashion to perform the overall functionality of the CPSs. 1 Modern CPSs are connected to the internet and employ cloud-based infrastructure for their computational requirements. Moreover, the systems of a CPS systematically communicate with each other and the field devices of a CPS are commonly provided with one or more interfaces for maintenance purposes. 2 Nowadays, CPSs are capable of performing multiple diverse functions as compared to the legacy systems. However, such capability can result in an increased system complexity. From the systems engineering perspective, the increase in system complexity makes it more challenging to provide service guarantees. Similarly, from the security perspective, added complexity widens the attack surface. 3 The functional interdependencies among the systems of CPS further exacerbates the security risk. An attack that is initiated at one subsystem of a CPS can disseminate to others. This can result in a cascade effect of compromising one system after other and eventually impairing the functionality of whole CPS in a short amount of time. Some CPS security frameworks mitigate the security risk by promptly patching the system components for newly discovered vulnerabilities, in addition to implementation of strict
Transactions on Emerging Telecommunications Technologies
Opportunistic networks (OppNets) have attracted widespread attention as wireless technologies hav... more Opportunistic networks (OppNets) have attracted widespread attention as wireless technologies have advanced. OppNets are widely used in delay‐tolerant applications because they route messages using a store‐carry‐forward mechanism. Recently, socially aware routing has been increasingly modeled for message dissemination in OppNets, where the message is routed selectively through cooperative nodes based on user interests; however, routing becomes extremely difficult as node density and data size increase. However, the current method fails to reduce data redundancy, message overhead, delay, and improve performance efficiency. To address the issues, this article proposes social context‐aware microscopic routing (SCAMR) for OppNets. SCAMR uses cluster‐based communication, novel social‐context association mapping, and an improved lost packet retrieval mechanism with minimal messaging overhead. In this work, the experiment was performed by considering three scenarios: varying node size, var...
A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This g... more A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This growth is considered deadly since it may cause death. The brain controls numerous functions, such as memory, vision, and emotions. Due to the location, size, and shape of these tumors, their detection is a challenging and complex task. Several efforts have been conducted toward improved detection and yielded promising results and outcomes. However, the accuracy should be higher than what has been reached. This paper presents a method to detect brain tumors with high accuracy. The method works using an image segmentation technique and a classifier in MATLAB. The utilized classifier is a Support Vector Machine (SVM). Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) are also involved. A dataset from the Kaggle website is used to test the developed approach. The obtained results reached nearly 99.2% of accuracy. The paper provides a confusion matrix of applying the proposed approach to testing images and a comparative evaluation between the developed method and some works in the literature. This evaluation shows that the presented system outperforms other approaches regarding the accuracy, precision, and recall. This research discovered that the developed method is extremely useful in detecting brain tumors, given the high accuracy, precision, and recall results. The proposed system directs us to believe that bringing this kind of technology to physicians diagnosing brain tumors is crucial.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
IEEE Transactions on Dependable and Secure Computing, 2019
With the growing amount of cyber threats, the need for development of high-assurance cyber system... more With the growing amount of cyber threats, the need for development of high-assurance cyber systems is becoming increasingly important. The objective of this paper is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved attacks. We present the interleaving concept and investigate how interleaving multiple attacks can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop two architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a database of HMM templates of known attacks and exhibit varying performance and complexity. For performance evaluation, in the presence of multiple multi-stage attack scenarios, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are used to demonstrate the efficacy of the proposed architectures.
The growing reliance on Cyber technology for Smart Grid and other Cyber-Physical Systems (CPS) ap... more The growing reliance on Cyber technology for Smart Grid and other Cyber-Physical Systems (CPS) applications increases high assurance challenges. Advanced Metering Infrastructure (AMI) is a critical part of smart grid and failure to address security risks in it might disrupt utilities in their mission to deliver power reliably. The objective of this paper is to find mitigation actions for attacks that might be launched against AMI. The paper introduces a tool called SecAMI that calculates the relationship between the speed at which an attack spreads, the speed at which it can be detected, and the consequences on the availability of the AMI. The results from SecAMI can be used as a performance metric to significantly improve the development and the deployment of Intrusion Detection Systems (IDSs) in an AMI environment. Experimental results with an IDS called Amilyzer show that centralized IDS might not work efficiently in scalable systems comparing with distributed IDS in term of detection time, especially with a Distributed Denial of Service (DDoS) attack or remote disconnects on the AMI.
Health providers use the ECG machine to get information about the heart. This information plays a... more Health providers use the ECG machine to get information about the heart. This information plays a significant role since it tells them about the status of the heart. The ECG machine presents this information in a waveform. During the Covid-19 pandemic, all governments have placed numerous rules and policies to protect people from the virus and from spreading it. One of the rules and policies is to prevent touching surfaces in public places. However, in health care centers, touching surfaces can't be avoided completely since there is a need to touch them or place some wires on the human body such as placing wires to use the ECG machine. In Saudi Arabia, the government has placed a policy in all its buildings, public places, and the private sector to measure the temperature at the entrance. Due to this situation, the idea has come into mind to have a touchless method to measure the heartbeat rate. In this paper, proposing a feasible and reliable method to estimate a continuous heartbeat rate is presented. It uses a face recognition approach to predict the heart pulse continuously in real-time according to colors intensity measurement. Using a segmentation algorithm is involved since the approach takes its input from a video or an image. Several experiments have been conducted on volunteers to verify the obtained results and measure their relative errors. Consequently, the errors are less than 7% which is quite acceptable. At the end of this article, a comparative assessment is performed between the presented approach and some works from literature. This assessment is conducted based on the methodologies being utilized and applied and Mean Absolute Error (MAE). Furthermore, it shows whether those methods require physical contact or not. The obtained results indicate that the implemented system herein outperforms other state-of-the-art methods.
Saudi Arabia has started building smart cities and communities as part of the Saudi 2030 vision, ... more Saudi Arabia has started building smart cities and communities as part of the Saudi 2030 vision, which aims to digitalize all services. Smart cities use different types of technologies and data to improve the quality of life for citizens, manage resources, and make operations more efficient. In big cities such as Riyadh and Jeddah, the number of vehicles on the road has dramatically increased. Hence, parking has become a problem since there are limited spaces available. In this article, a novel, intelligent, and automated method for vehicle parking and management is proposed. This approach employs a convolutional neural network (CNN) tool to train the algorithm deeply. Image segmentation and preprocessing techniques are employed as well. All operations are automated and cost-effective since the proposed smart parking management system utilizes only a single camera to provide real-time views of the status of a parking lot. Furthermore, there is no need for human interference, and it is easy to maintain. Several simulation scenarios were conducted on MATLAB to validate this approach and prove its efficiency. A comparative evaluation between the proposed system and some works of literature is provided, and it indicates that the developed system outperforms the works from the preexisting literature.
Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover... more Data mining and analytics involve inspecting and modeling large pre-existing datasets to discover decision-making information. Precision agriculture uses data mining to advance agricultural developments. Many farmers aren't getting the most out of their land because they don't use precision agriculture. They harvest crops without a well-planned recommendation system. Future crop production is calculated by combining environmental conditions and management behavior, yielding numerical and categorical data. Most existing research still needs to address data preprocessing and crop categorization/classification. Furthermore, statistical analysis receives less attention, despite producing more accurate and valid results. The study was conducted on a dataset about Karnataka state, India, with crops of eight parameters taken into account, namely the minimum amount of fertilizers required, such as nitrogen, phosphorus, potassium, and pH values. The research considers rainfall, season, soil type, and temperature parameters to provide precise cultivation recommendations for high productivity. The presented algorithm converts discrete numerals to factors first, then reduces levels. Second, the algorithm generates six datasets, two from Case-1 (dataset with many numeric variables), two from Case-2 (dataset with many categorical variables), and one from Case-3 (dataset with reduced factor variables). Finally, the algorithm outputs a class membership allocation based on an extended version of the K-means partitioning method with lambda estimation. The presented work produces mixed-type datasets with precisely categorized crops by organizing data based on environmental conditions, soil nutrients, and geo-location. Finally, the prepared dataset solves the classification problem, leading to a model evaluation that selects the best dataset for precise crop prediction.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
With the growing amount of cyber threats, the need for development of high-assurance cyber system... more With the growing amount of cyber threats, the need for development of high-assurance cyber systems is becoming increasingly important. The objective of this paper is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved attacks. We present the interleaving concept and investigate how interleaving multiple attacks can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop two architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a database of HMM templates of known attacks and exhibit varying performance and complexity. For performance evaluation, in the presence of multiple multi-stage attack scenarios, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are used to demonstrate the efficacy of the proposed architectures.
Lung cancer is the leading cause of cancer-related death around the globe. The treatment and surv... more Lung cancer is the leading cause of cancer-related death around the globe. The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis. Most diagnostic techniques can identify and classify only one type of lung cancer. It is crucial to close this gap with a system that detects all lung cancer types. This paper proposes an intelligent decision support system for this purpose. This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives. Its algorithm uses a Convolutional Neural Network (CNN) tool to perform deep learning and a Random Forest Algorithm (RFA) to help classify the type of cancer present using several extracted features, including histograms and energy. Numerous simulation experiments were conducted on MATLAB, evidencing that this system achieves 98.7% accuracy and over 98% precision and recall. A comparative assessment assessing accuracy, recall, precision, specificity, and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered metrics. This study found that using CNNs and RFAs is highly effective in detecting lung cancer, given the high accuracy, precision, and recall results. These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Nowadays, the pace of coordinated cyber security crimes has become drastically more rapid, and ne... more Nowadays, the pace of coordinated cyber security crimes has become drastically more rapid, and network attacks have become more advanced and diversified. The explosive growth of network security threats poses serious challenges for building secure Cyber-based Systems (CBS). Existing studies have addressed a breadth of challenges related to detecting network attacks. However, there is still a lack of studies on the detection of sophisticated Multi-stage Attacks (MSAs). The objective of this dissertation is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved MSAs. We present the interleaving concept and investigate how interleaving multiple MSAs can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop three architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a set of HMM templates of known attacks and exhibit varying performance and complexity. For performance evaluation, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are conducted to demonstrate the efficacy of the proposed architecture in the presence of multiple multi-stage attack scenarios, and in the presence of false alerts with various rates.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Computational Intelligence and Neuroscience, Aug 29, 2022
Leukemia cancer is the most common type of cancer that occurs in childhood. e most common types a... more Leukemia cancer is the most common type of cancer that occurs in childhood. e most common types are acute lymphocytic leukemia (ALL) and acute myelogenous leukemia (AML) which a ect children and adults, respectively. Several health issues occur due to these cancers. Leukemia a ects the bone marrow or the lymph nodes. Leukemia produces abnormal white blood cells via the bone marrow system. e a ected white blood cells are unable to perform their tasks properly. Detecting leukemia usually requires taking a blood smear from a patient and working with expert hematologists who analyze the smear with a microscope. In this paper, a method to detect ALL and AML using a deep learner classi er is developed and proposed. e method detects both types, determines their severity, and creates a message that recommends next steps to patients. is approach works based on image segmentation and a convolutional neural network (CNN) tool called AlexNet. e obtained results from the proposed approach and using MATLAB reached more than 98% accuracy. e margin exists because several operations are needed to fully detect the blood cancer. A dataset of leukemia from the Kaggle site is used to test the developed method and illustrate its e ectiveness. is dataset is C-NMC_Leukemia, and it consists of nearly 10 GB worth of 15,000 images. A confusion matrix of testing images is provided to prove the correctness of the presented approach. Furthermore, a comparative analysis between the proposed algorithm and some works from the literature is presented. is analysis compares the method used to extract features, the classi er that is utilized, the accuracy, the precision, and the recall. e obtained results indicate that the proposed method outperforms other works and produces better results.
A Taguchi particle swarm optimization (TPSO) with a three-layer feedforward artificial neural net... more A Taguchi particle swarm optimization (TPSO) with a three-layer feedforward artificial neural network (ANN) is used to model and optimize the chemical composition of a steel bar. The novel contribution of a TPSO is the use of a Taguchi method mechanism to exploit better solutions in the search space through iterations, the use of the conventional non-linear PSO to increase convergence speed, and the use of random movement for particle diversity. The exploration and exploitation capability of the TPSO were confirmed by performance comparisons with other PSO-based algorithms in solving high-dimensional global numerical optimization problems. Experiments in this paper showed that the TPSO provides higher computational efficiency and higher robustness when solving problems involving seven non-linear benchmark functions, including three unimodal functions, one multimodal functions, two rotated functions, and one shifted functions. The results for the computational experiments show that the TPSO outperforms other PSO-based algorithms reported in the literature. Finally, the results obtained by a TPSO-based ANN model of the chemical composition of the steel bar were consistent with the actual data. That is, the proposed TPSO with three-layer feedforward ANN can be used in practical applications. INDEX TERMS Taguchi method, particle swarm optimization, feedforward artificial neural network, chemical composition of steel bar, yield point, tensile strength.
Emerging cyber-physical systems incorporate systems of systems that have functional interdependen... more Emerging cyber-physical systems incorporate systems of systems that have functional interdependencies. With the increase in complexity of the cyberphysical systems, the attack surface also expands, making cyber-physical systems more vulnerable to cyber-attacks. The functional interdependencies exacerbate the security risk as a cyber-attack that compromises one constituent system of a cyber-physical system can disseminate to others. This can result in a cascade effect that can impair the operability of the whole cyber-physical system. In this article, we present a novel security architecture that localizes the cyber-attack in a timely manner, and simultaneously recovers the affected cyber-physical system functionality. We have evaluated the performance of the architecture for advanced metering infrastructure-based pricing cyber-attacks scenario. The simulation results exhibit the effectiveness of the proposed architecture in containing the attack in terms of system availability and its impact on the electric load distribution in the power grid. The increase in popularity of automated systems has made cyber-physical systems (CPSs) find their application in many diverse domains, including electrical power grid, automotive systems, smarthome systems and healthcare systems. Emerging CPSs integrate heterogeneous systems that have functional interdependencies. These integrated heterogeneous systems effectuate discrete functions and collaborate in an intricate fashion to perform the overall functionality of the CPSs. 1 Modern CPSs are connected to the internet and employ cloud-based infrastructure for their computational requirements. Moreover, the systems of a CPS systematically communicate with each other and the field devices of a CPS are commonly provided with one or more interfaces for maintenance purposes. 2 Nowadays, CPSs are capable of performing multiple diverse functions as compared to the legacy systems. However, such capability can result in an increased system complexity. From the systems engineering perspective, the increase in system complexity makes it more challenging to provide service guarantees. Similarly, from the security perspective, added complexity widens the attack surface. 3 The functional interdependencies among the systems of CPS further exacerbates the security risk. An attack that is initiated at one subsystem of a CPS can disseminate to others. This can result in a cascade effect of compromising one system after other and eventually impairing the functionality of whole CPS in a short amount of time. Some CPS security frameworks mitigate the security risk by promptly patching the system components for newly discovered vulnerabilities, in addition to implementation of strict
Transactions on Emerging Telecommunications Technologies
Opportunistic networks (OppNets) have attracted widespread attention as wireless technologies hav... more Opportunistic networks (OppNets) have attracted widespread attention as wireless technologies have advanced. OppNets are widely used in delay‐tolerant applications because they route messages using a store‐carry‐forward mechanism. Recently, socially aware routing has been increasingly modeled for message dissemination in OppNets, where the message is routed selectively through cooperative nodes based on user interests; however, routing becomes extremely difficult as node density and data size increase. However, the current method fails to reduce data redundancy, message overhead, delay, and improve performance efficiency. To address the issues, this article proposes social context‐aware microscopic routing (SCAMR) for OppNets. SCAMR uses cluster‐based communication, novel social‐context association mapping, and an improved lost packet retrieval mechanism with minimal messaging overhead. In this work, the experiment was performed by considering three scenarios: varying node size, var...
With the growing Cyber threats, the need to develop high assurance Cyber systems is becoming incr... more With the growing Cyber threats, the need to develop high assurance Cyber systems is becoming increasingly important. The objective of this paper is to address the challenges of modeling and detecting sophisticated and diversified network attacks. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop two architectures that can detect and track in real-time the progress of these organized attacks. These architectures are based on developing a database of HMM templates and exhibit varying performance and complexity. For performance evaluation, in the presence of multiple multi-stage attack scenarios, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are used based on the DARPA2000 dataset to demonstrate the efficacy of the proposed architectures.
Uploads
Papers by Tawfeeq Shawly