Researchers and developers have widely used deep learning and computer vision in many applications, including bias and representation issues, with amazing and rapid progress. In order to identify and differentiate people based on gender and ethnicity, developers employ both color concentration and facial details. This paper utilizes a new convolutional neural network model to recognize facial race. We trained and tested the model on four races and genders (African, Asian, Indian, and Caucasian). the dataset collected from the datasets (pretty-face, SCUT-FBP5500_v2., called AFD-dataset, cnsifd_faces_bmp, Indian_actors_faces, img_align_celeba, CASIA-Face-Africa). The experiment results show that Res50 models have proven to have a better model accuracy rate in races. Race Gender Convolution Neural Network (RGCNN) and IncV3 both achieved second place, while VGG19 ranked last. Both the Res50 and IncV3 results by gender show a better accuracy rate. RGCNN is in third place, while VGG19 is the last one. The RGCNN model is a lightweight has a smaller total number of parameters. The VGG19 Model, on the other hand, comes in second place. The IncV3 model, on the other hand, comes in third place, and finally, the Res50 model is the last one to have a total number of parameters.

Conjugate gradient methods are widely recognized for their efficiency in solving large-scale optimization problems with minimal memory requirements. This paper introduces a new CG algorithm derived from a modified secant condition, utilizing a redefined difference gradient vector to improve numerical stability and optimization performance. By integrating this modified vector into the computation of the conjugacy parameter βk, we propose a new parmeter, β ABM k , which effectively resolves the issues of non-negativity and convergence that are encountered in existing methods. Numerical experiments confirm the superior performance of the proposed method on unconstrained optimization problems across various dimensions, demonstrating faster convergence rates, fewer iterations, and reduced computational times compared to traditional CG (HS, DL, and LS) methods. Additionally, the algorithm's practical effectiveness is validated in medical image restoration tasks, specifically in reducing salt-and-pepper noise in grayscale and color images. When evaluated against established methods like HS, FHTTCGM-N, and HZ, the proposed algorithm achieves notable improvements in metrics such as iteration count, computational time, mean squared error, peak signal-to-noise ratio, and structural similarity index. These results underscore the algorithms significant contributions to both the theoretical and practical advancements of CG methods in optimization and image restoration.

Human-computer interaction, emotion analysis, and more depend on computer vision's face expression recognition. Real-time facial expression identification enhances decision-making in human-computer interaction, healthcare, marketing, and other fields. This study introduces a fast and accurate real-time facial expression identification system utilizing Convolutional Neural Network (CNN) and Mobile Network Version 2 (MobileNetV2). The proposed methodology involves the design and implementation of a CNN architecture tailored for facial expression recognition. We strategically arrange layers, fine-tune parameters, and leverage transfer learning techniques, particularly focusing on the optimization of model depth and complexity. The model is trained and evaluated using benchmark datasets to ensure robust performance across various facial expressions. Data-driven infrastructure helps the system manage real-world changes. Our approach identifies facial emotions in real time from live footage. This paper explores facial expression recognition employing a simplified CNN architecture, contrasting with more complex pre-trained networks like MobileNetV2. Despite its simplicity, the proposed CNN yields result close to the performance of MobileNetV2. The study emphasizes the viability of less intricate CNNs for facial expression recognition, offering a balance between model simplicity and competitive accuracy. High accuracy and computational efficiency make the system suitable for real-time applications and resource-constrained systems The experimental tests extracted that a very acceptable accuracy achieved when using CNN model with the AffectNet dataset with a ratio of (97.2%).

Handwritten character recognition (HCR) involves identifying characters in images, documents, and various sources such as forms surveys, questionnaires, and signatures, and transforming them into a machine-readable format for subsequent processing. Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle. The use of convolutional neural network (CNN) in recent developments has notably advanced HCR, leveraging the ability to extract discriminative features from extensive sets of raw data. Because of the absence of pre-existing datasets in the Kurdish language, we created a Kurdish handwritten dataset called (KurdSet). The dataset consists of Kurdish characters, digits, texts, and symbols. The dataset consists of 1560 participants and contains 45,240 characters. In this study, we chose characters only from our dataset. We utilized a Kurdish dataset for handwritten character recognition. The study also utilizes various models, including InceptionV3, Xception, DenseNet121, and a custom CNN model. To show the performance of the KurdSet dataset, we compared it to Arabic handwritten character recognition dataset (AHCD). We applied the models to both datasets to show the performance of our dataset. Additionally, the performance of the models is evaluated using test accuracy, which measures the percentage of correctly classified characters in the evaluation phase. All models performed well in the training phase, DenseNet121 exhibited the highest accuracy among the models, achieving a high accuracy of 99.80% on the Kurdish dataset. And Xception model achieved 98.66% using the Arabic dataset.

Academics and researchers worldwide have paid close attention to biometric handwriting recognition using deep learning as much research has been proposed to enhance biometric recognition in the past and in recent years. Several solutions for character recognition systems in various languages, including Chinese, English, Japanese, Arabic, and Kurdish have been developed. Unfortunately, there has been minimal growth in the Assyrian language. There is still little research on Assyrian handwriting. In this paper, a new Assyrian language dataset was created as part of the procedure by distributing 500 forms consisting of 36 Assyrian characters to people between the ages of 13 and 60 of both genders. The preprocessing operation includes cleaning the noisy data and segmenting each image to 224x224 pixels. This effort resulted in the collection of 18,000 images of these characters to be trained 70% and tested 30% in four CNN models, VGG16, VGG19, MobileNet-V2, and ResNet-50, over 30 epochs to give an accuracy rate of 90.97%, 92.06%, 95.70%, and 94.97%., respectively.

Facial expression recognition is a crucial area of study in the field of computer vision. Research on nonverbal communication has shown that a significant amount of deliberate information is sent via facial expressions. Facial expression recognition is a crucial field in computer vision that deals with the significant impact of nonverbal communication. Expression recognition has lately been extensively used in the medical and advertising sectors. Difficulties in Facial Emotion Recognition. Facial emotion recognition is a technique that examines facial expressions in static photos and videos to uncover information about an individual's emotional state. The intricacy of facial expressions, the versatile use of the technology in any setting, and the incorporation of emerging technologies like artificial intelligence pose substantial privacy hazards. Facial expressions serve as non-verbal cues, offering indications of human emotions. Deciphering emotional expressions has been a focal point of study in psychology for many years. This study will examine several prior studies that have undertaken comprehensive facial analysis, including both total and partial face recognition, to identify expressions and emotions. The datasets and models used in previous studies, as well as the findings gained, show that employing the whole face yields more accuracy compared to using specific face-parts, which result in lower accuracy ratios. However, emotional identification often does not rely only on the whole face, since it is not always feasible to have the full face available. Contemporary research is now prioritising the identification of facial expressions based on certain facial features. Efficient deep learning algorithms, particularly the CNN algorithm, can do this task.

Cyberspace is extremely dynamic, with new attacks arising daily. Protecting cybersecurity controls is vital for network security. Deep Learning (DL) models find widespread use across various fields, with cybersecurity being one of the most crucial due to their rapid cyberattack detection capabilities on networks and hosts. The capabilities of DL in feature learning and analyzing extensive data volumes lead to the recognition of network traffic patterns. This study presents novel lightweight DL models, known as Cybernet models, for the detection and recognition of various cyber Distributed Denial of Service (DDoS) attacks. These models were constructed to have a reasonable number of learnable parameters, i.e., less than 225,000, hence the name “lightweight.” This not only helps reduce the number of computations required but also results in faster training and inference times. Additionally, these models were designed to extract features in parallel from 1D Convolutional Neural Networks (CNN) and Long ShortTerm Memory (LSTM), which makes them unique compared to earlier existing architectures and results in better performance measures. To validate their robustness and effectiveness, they were tested on the CIC-DDoS2019 dataset, which is an imbalanced and large dataset that contains different types of DDoS attacks. Experimental results revealed that both models yielded promising results, with 99.99% for the detection model and 99.76% for the recognition model in terms of accuracy, precision, recall, and F1 score. Furthermore, they outperformed the existing state-of-the-art models proposed for the same task. Thus, the proposed models can be used in cyber security research domains to successfully identify different types of attacks with a high detection and recognition rate.

Cyberspace is extremely dynamic, with new attacks arising daily. Protecting cybersecurity controls is vital for network security. Deep Learning (DL) models find widespread use across various fields, with cybersecurity being one of the most crucial due to their rapid cyberattack detection capabilities on networks and hosts. The capabilities of DL in feature learning and analyzing extensive data volumes lead to the recognition of network traffic patterns. This study presents novel lightweight DL models, known as Cybernet models, for the detection and recognition of various cyber Distributed Denial of Service (DDoS) attacks. These models were constructed to have a reasonable number of learnable parameters, i.e., less than 225,000, hence the name “lightweight.” This not only helps reduce the number of computations required but also results in faster training and inference times. Additionally, these models were designed to extract features in parallel from 1D Convolutional Neural Networks (CNN) and Long ShortTerm Memory (LSTM), which makes them unique compared to earlier existing architectures and results in better performance measures. To validate their robustness and effectiveness, they were tested on the CIC-DDoS2019 dataset, which is an imbalanced and large dataset that contains different types of DDoS attacks. Experimental results revealed that both models yielded promising results, with 99.99% for the detection model and 99.76% for the recognition model in terms of accuracy, precision, recall, and F1 score. Furthermore, they outperformed the existing state-of-the-art models proposed for the same task. Thus, the proposed models can be used in cyber security research domains to successfully identify different types of attacks with a high detection and recognition rate.

Nowadays, with the occurrence of new attacks and raised challenges have been facing the security of computer systems. Cyber security techniques have become essential for information technology services to detect and react against cyber-attacks. The strategy of cyber security enables visibility of various types of attacks and vulnerabilities throughout computer networks, whilst also provides detecting cyber-attacks and effective ways of identifying and preventing them. This study mainly focuses on the performance analysis and challenges faced by cyber security using the latest techniques. It also provides a review of the attack detection process including the robust effectiveness of intelligent techniques. Finally, summarize and discuss some methods to increase attack detection performance utilizing deep learning (DL) architectures.

Deep learning and deep neural networks, particularly Convolutional Neural Networks (CNNs), are rapidly growing areas of machine learning and are currently the primary tools used for image analysis and classification applications. Handwriting recognition involves using computer algorithms and software to interpret and recognize handwritten text and drawings and has various applications such as automated handwriting analysis, document digitization, and handwriting-based user interfaces. Many deep learning models have been applied in the field of handwriting recognition and various datasets have been used to evaluate new computer vision techniques. This article provides an overview of the current state-of-the-art approaches and contributions to handwriting recognition using different datasets. Furthermore, the paper explains the most commonly used algorithms for recognizing handwritten characters, words, and numbers. Compares them based on their accuracy. This study covered different aspects and methods of machine learning and Deep Learning (DL) for handwritten recognition that showed different achievements for each.

Health-related issues are a top priority for anyone in the world, and if there is any issue related to health, then solutions should be found as soon as possible. Medicine is one of the most important causes of recovery, so storing medicines of the highest possible quality is necessary. Storing medicines is very important for the hospital because they are used for hormones, viruses, and ointments, which are of great value to the hospital's sustainability and must always be preserved. This study designed and implemented a system to continuously control and monitor sensitive atmospheric information for drug storage, such as temperature, humidity, and light exposure. The system starts by reading the environment sensors around the drug store by the microcontroller. Then the microcontroller passes the data to the fuzzy logic. Mamdani-type fuzzy control is implemented to control and monitor devices that are used on fuzzy rules. The system has the ability to send an alarm when any of the temperature and humidity-related parameters are higher or lower than normal. The data collected using this system is sent via two methods: the first is the website platform, and the second is GSM notification. Drug storage is designed and implemented using a microcontroller and IoT sensors. During the system analysis phase, the SWOT method was utilized to obtain user perspectives and preferences, which were then incorporated into the system output specifications for the development phase. This approach helped to identify the necessary system requirements. During the implementation phase of the system and to measure the capabilities of the system, the technique of measuring the system usability scale was used, with the contribution of 22 users of the system, and the percentage recorded as general satisfaction with the system was 91%. The new system improves the overall quality of drug storage, reduces the risk of drug deterioration to patients' health, and reduces the incidence of adverse events associated with drug mismanagement.

The development of information systems motivated scientists to use their knowledge to improve new tactics that can provide competitive advantages in a highly complex environment, primarily in the management of agricultural products marketing systems. Farmers often blame marketing issues for their struggles. Poor prices, a lack of transportation, and substantial post-harvest losses are all issues that they can recognize, but they are typically ill-equipped to solve. Modern technology, particularly Internet and mobile connectivity, makes it easier for people to do their work across a wide range of activities,including economics, commerce, marketing, and agriculture. Agricultural Information Management Systems (AIMS) are becoming more popular as a useful sector as agriculture expands and becomes one of the world's most lucrative enterprises. In recent years, people have become increasingly dependent on this technology. Systems have evolved from passively absorbing information to actively incorporating users as an important part of the system. The goal of this research is to clarify and exhibit that different kind of systems employed by various agricultural-related authorities had a good probable influence on their operations by making duties simpler for their personnel. Specifically, the study focuses on displaying this information. In addition, the research analyzes the primary distinctions between these studies by contrasting many of them, with the goal of gaining an understanding of the foundational advantages that may be used by new researchers that are interested in developing new AIMS, And in order to fully comprehend websites, researchers are combining quantitative and qualitative approaches. Numerous analyses of agricultural product websites have been conducted, using a wide range of approaches. Study 26 investigated whether farmers' and retailers' use of web-based technology facilitated the display and sale of goods.

The development of information systems motivated scientists to use their knowledge to improve new tactics that can provide competitive advantages in a highly complex environment, primarily in the management of agricultural products marketing systems. Farmers often blame marketing issues for their struggles. Poor prices, a lack of transportation, and substantial post-harvest losses are all issues that they can recognize, but they are typically ill-equipped to solve. Modern technology, particularly Internet and mobile connectivity, makes it easier for people to do their work across a wide range of activities, including economics, commerce, marketing, and agriculture. Agricultural Information Management Systems (AIMS) are becoming more popular as a useful sector as agriculture expands and becomes one of the world's most lucrative enterprises. In recent years, people have become increasingly dependent on this technology. Systems have evolved from passively absorbing information to actively incorporating users as an important part of the system. The goal of this research to find out whether various agricultural implementations of various systems have a good effect on their operations by facilitating chores for farmers and dealers, and merchants easier. Study 26 investigated whether farmers' and retailers' use of web-based technology facilitated the display and sale of goods.

Deep learning and deep neural networks, particularly Convolutional Neural Networks (CNNs), are rapidly growing areas of machine learning and are currently the primary tools used for image analysis and classification applications. Handwriting recognition involves using computer algorithms and software to interpret and recognize handwritten text and drawings and has various applications such as automated handwriting analysis, document digitization, and handwriting-based user interfaces. Many deep learning models have been applied in the field of handwriting recognition and various datasets have been used to evaluate new computer vision techniques. This article provides an overview of the current state-of-the-art approaches and contributions to handwriting recognition using different datasets. Furthermore, the paper explains the most commonly used algorithms for recognizing handwritten characters, words, and numbers. Compares them based on their accuracy. This study covered different aspects and methods of machine learning and Deep Learning (DL) for handwritten recognition that showed different achievements for each.

The rapid development of internet of things (IoT) technology over the course of recent history has made it possible to connect a large number of smart things and sensors, as well as to establish an environment in which data can be seamlessly exchanged between them. This has led to an increase in the demand for data analysis and storage platforms such as cloud computing and fog computing. One of the application areas for the internet of things that has garnered a lot of interest from the business world, academic institutions, and the government is healthcare. The IoT and fuzzy logic are being used in the medical business to improve patient safety, the overall quality of care, and the overall efficiency of medical operations. The most important healthcare studies that are pertinent to pharmacies have been used as the basis for this research. The purpose of this research is to investigate recent advancements in medical modules, remotes, and detector patterns, as well as current innovations in IoT and fuzzy logic-based health care, and current policies from around the world, with the intention of determining how well they support the long-term growth of IoT and fuzzy logic in healthcare.

The concept of storing historical data for retrieving them when needed has been conceived, and the idea was primitive to build repositories for historical data to store these data, despite the use of a specific technique for recovering these data from various storage modes. The data warehouse is the most reliable and widely used technology for scheduling, forecasting, and managing corporations. Also, it is concerned with the data storage facility that extensive collection of data. Data warehouses are called ancient modern techniques; since the early days of relational databases, the critical component of decision support, increasing focus for the database industry. Many commercial products and services are now available, and almost all of the primary database management system vendors provide them. When opposed to conventional online transaction processing applications, decision support puts slightly different demands on database technology. This paper analyzes the performance of the data warehouse architectures by studying and comparing many research works in this field. The study involves extracting, transforming, and loading the data from different recourses and the important characteristic of the architecture types. Furthermore, the tools and application service techniques used to build data warehouse architecture.

In this study, the significance and necessities of surveillance systems have been investigated in several areas-both in the use of neural networks, street lighting systems, factories, and laboratories-for the monitoring systems, especially concerning the design of artificial intelligence programs. The importance of these initiatives and how they can affect any sector and industry reach an essential point from here. Here we reach an important point. An algorithm and an extraordinary approach have been used in every field to develop an intelligent programmer. Something has been mentioned here: the ability to access these intelligent programs in all areas of life. We concentrate on a variety of fields of use and design of monitoring systems in this review article.

Networks of computers are everywhere, and it implemented in all sectors, including an IT, industrial, managerial sector, and need to be accessed remotely. Remote administration offers access to a remote device. As more departments of Information Technology centralize and reorganize to keep costs down, many remote sites are left with no IT support on-site. Remote computer administration is increasingly common due to the major cost advantages; many activities can be completed, and not needs to be personally accessed by the administrator. In this paper, the remotecontrol administration system has designed and implemented based on client/server architecture. The main goal of the proposed system allows clients to request help from remote servers over the Network.

Facial expression recognition (FER) has achieved an extreme role in research area since the 1990s. This paper provides a comparison approach for FER based on three feature selection methods which are correlation, gain ration, and information gain for determining the most distinguished features of face images using multi-classification algorithms which are multilayer perceptron, Naïve Bayes, decision tree, and K-nearest neighbor (KNN). These classifiers are used for the mission of expression recognition and for comparing their proportional performance. The main aim of the provided approach is to determine the most effective classifier based on minimum acceptable number of features by analyzing and comparing their performance. The provided approach has been applied on CK+ dataset. The experimental results show that KNN is proven to be better classifier with 91% accuracy using only 30 features.tree, and K-nearest neighbor (KNN). These classifiers are used for the mission of expression recognition and for comparing their proportional performance. The main aim of the provided approach is to determine the most effective classifier based on minimum acceptable number of features by analyzing and comparing their performance. The provided approach has been applied on CK+ dataset. The experimental results show that KNN is proven to be better classifier with 91% accuracy using only 30 features.

Nowadays, with portable computation equipment becoming extra commonly depended besides incorporated through extra sensitive structures such as infrastructure besides armed or regulation implementation. However, is being clearer when considering that malware and other types with cyberattacks attack such portable structures. Malware has always been an issue regarding any technological advances in the world of software. Smartphones and other mobile devices are thus to be expected to face the same problems. In this paper, mobile malware detection techniques presented in details. Furthermore, we have stated the recent techniques of mobile malware detection. Adding to that, these closest works to the paper objective are compared with respect to the based-portal, depended-approach, and significant system’s description points.

One of the most important subject which many researchers depending on it by applying many algorithms and methods is Cloud Computing. Some of these methods were used to enhance performance, speed, and advantage of task level parallelism and some of these methods used to deal with big data and scheduling. Many others decrease the computation’s quantity in the process of implementation; specially decrease the space of the memory. Parallel data processing is one of the common applications of infrastructure, which is classified as a service in cloud computing. The purpose of this paper is to review parallel processing in cloud. However, the results and methods are inconsistent; therefore, the scheduling concepts give easy method to use the resources and process the data in parallel and decreasing the overall implementation time of processing algorithms. Overall, this review give us and open new doors for using the suitable technique in parallel data processing filed. As a result our work show according to many factors which strategies is better.

Facial Expression Recognition (FER) became an important and interest challenging area in the field of computer vision. Due to its major application possibilities, FER is an active topic of research papers since the 1990s, also for the same reason, FER has accomplished a high role in the image processing area. Typically, FER is processed in three main stages including face detection, feature extraction or selection, and classification. This study presents an overview of the current work done for the techniques of the FER field and reviews some of the published researches for the last five years till the date. This paper also gives an overview of the customized made FER algorithms and focused on the method of feature extraction of these FER techniques also the databases that are used. The feature extraction techniques played an important role in making these algorithms more efficient.

Facial Expression Recognition (FER) became an important and interest challenging area in the field of computer vision. Due to its major application possibilities, FER is an active topic of research papers since the 1990s, also for the same reason, FER has accomplished a high role in the image processing area. Typically, FER is processed in three main stages including face detection, feature extraction or selection, and classification. This study presents an overview of the current work done for the techniques of the FER field and reviews some of the published researches for the last five years till the date. This paper also gives an overview of the customized made FER algorithms and focused on the method of feature extraction of these FER techniques also the databases that are used. The feature extraction techniques played an important role in making these algorithms more efficient.

— Every era introduces different challenges to healthcare organizations, and the start of the twenty-first century has been no different. Today there is the unprecedented concentration on the quality of health services. A strong health-care system distributes quality services to all people, where and whenever they need it. Vaccination is one of the most important health-care services for the prevention and control of immuno-preventable diseases and, therefore it is essential to keep the vaccine card up-to-date and accessible to enable its benefits. This paper suggests a solution to remind the child’s parents to make an appointment for the next upcoming vaccination. The proposed system sends reminder short messages service (SMS) and an email message to remind the child's parents based on the vaccine schedule recommended in the Kurdistan region. The proposed system aimed at improving completion of the infant primary immunization by Automatic vaccine updates, reminders for free, and track vaccines for multiple babies from the health-care clinical center.

The use of the internet and communication technology noticeably contributes to development in all branches of science. The data that is transferred to communication channel are unsecured. Cryptography including security and integrity is a domain that provides policies for having privacy of the data. Protection of the data transferred through the internet is very important since such data when transferred through an unprotected channel may be attacked by a third party. RSA represents one of the public key cryptography methods that generate the modulus using two primes. These two primes represent the weakness of RSA benefited from and used by attackers. This paper proposes a new modified RSA method using selective multi keys encryption and decryption for the same modulus instead of using two keys, one for encryption and the other for decryption. This new method guarantees and increases the security of RSA.

The Information and Communication Technology (ICT) has witnessed great development in the recent years. Therefore, the design of Students and Alumni Web Portal (SAWP) involves the analysis of the internal and external environment of the three universities. For this آ آ purpose, SWOT technique has been used to detect the deep effect of environment factors on the strategic plan to discover the strengths, weaknesses, opportunities and threats facing the design of the proposed system. SAWP was designed using (MySQL, HTML, CSS, Java Script, jQuery, PHP, AJAX) techniques to provide robust portal system addressing two subsystems: student and alumni portal system. Testing of the SAWP was administered through two main stages: the first, to identify the student’ s views and their preferences. The second to measure the usability of the system through using System Usability Scale (SUS) method with subscription of 22 potential آ آ system users. The best results of SUS testing are: the rate of overall satisfaction was high nearly 80%. While the implementation outcomes found very compatibility and reasonable in wide extents between available data and system requirements.

Classification is one of the common tasks that are involved in data mining to build models for the prediction of future data. It performs its task by different classifier algorithms. This paper provides an approach based on information gain to determine the most distinguishing subset features of each attack class and combine multi classification algorithms which includes (Decision Tree J48, k nearest Neighbor and Naïve Bays). These classifiers are used for the task of detecting intrusions and comparing their relative performances. The goal of this work is to analyze the performance and accuracy of classification algorithms in order to identify the most efficient algorithm for each attack class, and then build accurate intrusion detection system. The proposed model has been applied on KDD Cup 99 data set using 60% of them for training and 40% for testing. These experimental results show that multiple classifiers work better than a single classifier. Also, multiple classifiers are more accurate and have abilities of distinguishing among the different attacks and normal connections effectively.

Classification is one of the common tasks that are involved in data mining to build models for the prediction of future data. It performs its task by different classifier algorithms. This paper provides an approach based on information gain to determine the most distinguishing subset features of each attack class and combine multi classification algorithms which includes (Decision Tree J48, k nearest Neighbor and Naïve Bays). These classifiers are used for the task of detecting intrusions and comparing their relative performances. The goal of this work is to analyze the performance and accuracy of classification algorithms in order to identify the most efficient algorithm for each attack class, and then build accurate intrusion detection system. The proposed model has been applied on KDD Cup 99 data set using 60% of them for training and 40% for testing. These experimental results show that multiple classifiers work better than a single classifier. Also, multiple classifiers are more accurate and have abilities of distinguishing among the different attacks and normal connections effectively.