Genetic convolutional neural network for intrusion detection systems

Due to the continuous growth and advancement of technology, an enormous volume of text data is generated daily across various sources including social media platforms, websites, search engines, healthcare records, and news articles. Extracting meaningful patterns from text data, such as viewpoints, related theories, journal distribution, facts, and the development of online news text, is a challenging task due to the varying lengths of the texts. One issue arises from the length of the text data itself, and another challenge lies in extracting valuable features, especially in news articles. In the deep learning models, the convolutional neural networks (CNNs) are capable of capturing local features in text data, but unable to capture the structural information or semantic relationships between words. Consequently, a sole CNN network often yields poor performance in text classification tasks, whereas the Gated Recurrent Unit (GRU) is adept at effectively extracting semantic information and understanding the global structural relationships present in textual data. This paper presents a solution to the problem by introducing a new text classification that integrates the strengths of CNN and GRU. The proposed hybrid models incorporate word vectorization and word dispersion in parallel. Initially, the model trains word vectors using the Word2vec model and then leverages the GRU model to capture semantic information from text sentences. Subsequently, the CNN method is employed to capture crucial semantic features, leading to classification using the SoftMax layer. Experimental findings demonstrated that the proposed hybrid GRU_CNN model outperformed and achieved accuracy 97.73% as compared to individual CNN, LSTM, and GRU models in terms of classification effectiveness and accuracy.

Attacks on networks are currently the most pressing issue confronting modern society. Network risks affect all networks, from small to large. An intrusion detection system must be present for detecting and mitigating hostile attacks inside networks. Machine Learning and Deep Learning are currently used in several sectors, particularly the security of information, to design efficient intrusion detection systems. These systems can quickly and accurately identify threats. However, because malicious threats emerge and evolve regularly, networks need an advanced security solution. Hence, building an intrusion detection system that is both effective and intelligent is one of the most cognizant research issues. There are several public datasets available for research on intrusion detection. Because of the complexity of attacks and the continually evolving detection of an attack method, publicly available intrusion databases must be updated frequently. A convolutional recurrent neural network is employed in this study to construct a deep-learning-based hybrid intrusion detection system that detects attacks over a network. To boost the efficiency of the intrusion detection system and predictability, the convolutional neural network performs the convolution to collect local features, while a deep-layered recurrent neural network extracts the features in the proposed Hybrid Deep-Learning-Based Network Intrusion Detection System (HDLNIDS). Experiments are conducted using publicly accessible benchmark CICIDS-2018 data, to determine the effectiveness of the proposed system. The findings of the research demonstrate that the proposed HDLNIDS outperforms current intrusion detection approaches with an average accuracy of 98.90% in detecting malicious attacks.

Enhancing CNN-based network intrusion detection through hyperparameter optimization

Cloud Computing (CC) is the preference of all information technology (IT) organizations as it offers pay-per-use based and flexible services to its users. But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders. This research article proposes a novel approach for enhancing the security of cloud storage systems by developing an Intrusion Detection System (IDS) that utilizes feature extraction, pre-processing, and advanced analysis tools. Specifically, the proposed IDS leverage a Convolutional Neural Network (CNN) model and a grey wolf optimization algorithm to detect potential cyber-attacks on cloud storage. The research work focuses on extracting relevant features from the data and performing preprocessing to remove any irrelevant or noisy data points. The CNN model is then trained using the pre-processed data, while the grey wolf optimization algorithm is engaged to optimize the runtime performance of the IDS. The outcome of the proposed approach demonstrates its ability to detect various types of cyber-attacks with high accuracy and efficiency, which can help to improve the security of cloud storage systems. By measuring the effectiveness and efficiency of the algorithm, it can be determined whether GWO is a suitable optimization algorithm for intrusion detection in cloud security.

Destructive cyber-attacks and cybercriminals are increasing with the increase in IoT (Internet of Things) devices globally. This ha/s led to the need for increase in security in IoT systems. Innovative and novel techniques are used by the intruders to accomplish malicious goals effectively through cyber-attacks. An Intrusion Detection System (IDS) is used for classification of attacks in IoT networks based on anomaly detection and machine learning techniques. Inefficiency is observed in the conventional machine learning models and intrusion detection techniques as the network technologies are unpredictable. Accurate identification of various anomalies is possible with deep learning models in several research segments. The input data along with its prominent characteristics may be categorized automatically for classification and anomaly detection using convolutional neural networks (CNN). Faster computations are enabled due to the performance efficiency of CNN. For IoT networks, an intrusion detection model based on anomaly detection is designed and developed in this paper. A multiclass classification framework is created initially using a CNN model. Further, 3D CNN is used for implementation of the proposed model. Various intrusion detection datasets from IoT networks are used for validation of the proposed CNN model. Pre-trained multiclass CNN model is used for implementation of multiclass and binary classification based on transfer learning. When compared to the conventional deep learning models, the proposed multiclass and binary classification framework has attained improved F1 score, recall, precision and accuracy.

This study aims to evaluate the effectiveness of the Convolutional Neural Network ( CNN ) method combined with Support Vector Machine ( SVM ) in detecting URLs.Phishing.Phishing is one of the significant cyber threats, where attackers try to trick users into providing sensitive information through fake websites.With the increasing number of phishing attacks , there is a need for effective methods to detect and prevent this threat.In this study, a dataset containing URLs phishing and non-phishing data were used to train the CNN -SVM model .The training process involved feature extraction from URLs using CNN , which is capable of capturing complex patterns in the data, followed by classification using SVM , which is known for its ability to handle high-dimensional data.Testing was conducted across nine different scenarios to evaluate the performance of the model under various conditions.The test results showed that the hybrid CNN -SVM model achieved a precision of 95%, a recall of 92%, and an F1-Score of 93%, with an overall accuracy of 94%.These results indicate that the model is not only effective in detecting URLs phishing , but also has a good balance between precision and recall.This study indicates that the combination of CNN and SVM can be an effective solution for detecting URLs phishing , making a significant contribution to the development of better cyber security systems.

This research aims to implement deep learning techniques to determine fact and fake news in Indonesian language. The methods used are Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The stages of the research consisted of collecting data, labeling data, preprocessing data, word embedding, splitting data, forming CNN and LSTM models, evaluating, testing new input data and comparing evaluations of the established CNN and LSTM models. The Data are collected from a fact and fake news provider site that is valid, namely TurnbackHoax.id. There are 1786 news used in this study, with 802 fact and 984 fake news. The results indicate that the CNN and LSTM methods were successfully applied to determine fact and fake news in Indonesian language properly. CNN has an accuracy test, precision and recall value of 0.88, while the LSTM model has an accuracy test and precision value of 0.84 and a recall of 0.83. In testing the new data input, all of the predictions obtained by CNN are correct, while the prediction results obtained by LSTM have 1 wrong prediction. Based on the evaluation results and the results of testing the new data input, the model produced by the CNN method is better than the model produced by the LSTM method.

This study compares the 5-day tropical cyclone (TC) track forecasts from the Global Ensemble Forecast System (GEFS) with a machine learning approach applied to this ensemble. A convolutional neural network (CNN) model was developed using northern Atlantic TC training data from 2008 to 2022, while an independent set of storms was used to evaluate the CNN model. The CNN for tropical cyclone track forecasting was trained using fivefold cross validation on a shuffled dataset split into 90% training/validation and 10% evaluation. PyTorch was used to develop a CNN model with a custom loss function, and performance was assessed using the haversine function and error metrics, comparing the CNN’s TC track forecasts to GEFS mean track forecasts. The CNN has a 58%–86% better track prediction against GEFS mean for forecast hours 0–96, which decreases to 35%–53% for hours 108–120. Track differences between those cases that the CNN improved the forecast versus did not improve are also explored, which shows that most of the CNN improvement is from a decrease in the along-track error (ATE). This is consistent with past studies of this ensemble, which showed that the largest bias exists in the along-track direction. Significance Statement This research focuses on enhancing the accuracy of tropical cyclone track forecasts by leveraging a convolutional neural network (CNN). Traditional forecasting models have relatively large errors in predicting the track of tropical cyclones, especially at forecast lead times of 3–5 days. These errors can result in inadequate warnings and misallocation of resources during storm preparations, potentially endangering lives and property. Our CNN model addresses these issues by correcting systematic biases, resulting in more accurate forecasts.

Empirical-statistical Downscaling (SD) techniques are valuable tools able to generate high-resolution climate information needed to carry out impact studies. In this regard, Convolutional Neural Networks (CNNs) are promising SD techniques capable of handling large amounts of data and extracting relevant predictor information for each particular site. These characteristics of the CNN represent a major advantage over traditional SD methods, which typically rely on human-guided predictor selection. Notwithstanding, an adequate tuning of the CNN is key for optimising their potential.In southern South America (SSA), CNNs has proven to be skilful in representing daily extreme temperatures and extrapolating into future scenarios. Although the selection of the activation function introduces a source of uncertainty in the future projections. In this context, this study aims to explore the use of CNNs as a statistical downscaling tool to simulate the wet bulb temperature (Tw) over SSA, a multivariate heat-stress index estimated from temperature and humidity. Tw has been widely used as a heat-stress proxy in different parts of the world, however, its characterisation and modelling in SSA remain as a pending task. To this end, four different CNN architectures regarding the activation function (ReLU or linear), domain size and configuration of the CNN layers were tested. All CNN models were trained during summer days using a cross-validation (CV) scheme in the period 1991-2020 and then evaluated in four unseen summers between 2021 and 2024. For comparison purposes, CNN models were similarly trained and validated to simulate maximum temperature (Tx). Overall, CNN models well represented all the features evaluated, including the heat-waves that took place in the summers evaluated independently. In particular, CNN models presents a better performance in simulating Tw than Tx with smaller errors in terms of mean and extremes aspects. Regarding the domain size, for both temperatures, the configuration with the smaller domain yields the best results. Also in this latter case, the reduction of the number of filter size in the last layer slightly improves the representation of Tx. When considering the large domain, the differences between the CNNs based on different activation functions increase, and CNN models with linear configuration outperform the ones with ReLu. The findings of this work reinforces the potential of CNNs for climate downscaling in SSA, especially for its use to simulate multivariate impact indices.

The growing development of IoT (Internet of Things) devices creates a large attack surface for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the security industry has seen an exponential increase in cyber-attacks. Many of these attacks have effectively accomplished their malicious goals because intruders conduct cyber-attacks using novel and innovative techniques. An anomaly-based IDS (Intrusion Detection System) uses machine learning techniques to detect and classify attacks in IoT networks. In the presence of unpredictable network technologies and various intrusion methods, traditional machine learning techniques appear inefficient. In many research areas, deep learning methods have shown their ability to identify anomalies accurately. Convolutional neural networks are an excellent alternative for anomaly detection and classification due to their ability to automatically categorize main characteristics in input data and their effectiveness in performing faster computations. In this paper, we design and develop a novel anomaly-based intrusion detection model for IoT networks. First, a convolutional neural network model is used to create a multiclass classification model. The proposed model is then implemented using convolutional neural networks in 1D, 2D, and 3D. The proposed convolutional neural network model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Transfer learning is used to implement binary and multiclass classification using a convolutional neural network multiclass pre-trained model. Our proposed binary and multiclass classification models have achieved high accuracy, precision, recall, and F1 score compared to existing deep learning implementations.

Brain tumors are formed due to the abnormal growth of the cells that get multiplied and become in uncontrollable perspective. Tumors can damage brain cells by pressing down the skull, which consociate begins in, which negatively affects human health. In advanced stages, a brain tumor is a more dangerous infection that cannot be relieved. Brain tumor detection and early prevention are necessary in today's world. An extreme learning machine (ELM) is a widely adopted algorithm in machine learning. It is proposed to use classification models in brain tumor imaging. This classification is based on the techniques implemented: Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN). CNN efficiently solves the convex optimization problem and is faster, requiring less human effort. The algorithmic architecture of GAN uses two neural networks, pocking one against the other. These networks are implemented in various fields to classify brain Tumor images. The present study mainly aims to introduce a new proposed classification system of preschool children brain imaging with Hybrid Convolutional Neural Networks and with the techniques of GAN. The proposed technique is compared with the existing hybrid-CNN and hybrid-GAN techniques. The outcomes are encouraging because the loss is deduced, and the accuracy facet increases. The proposed system achieved a training accuracy of 97.8% and a validation accuracy of 89%. The outcomes of the studies show that ELM in the platform of GAN for preschool children brain imaging classification has achieved higher predictive performance than the traditional classification mechanisms in increasingly complex scenarios. Time elapsed for training brain images samples finds inference value for training samples and time elapsed value increased by 28.9855%. Approximation ratio for cost based on probability, finding Approximation ratio for low probability range is increased by 88.1%. The combination of CNN, GAN and hybrid-CNN, hybrid-GAN, and hybrid CNN + GAN, compared with the proposed hybrid system, increased Detection latency for low range learning rate by 3.31%.

Hybrid convolution neural network model for a quicker detection of infested maize plants with fall armyworms using UAV-based images

A chest x-ray is considered to be a readily available and a reliable and fast method for the diagnosis of the pneumonia. In literature, different object detection algorithm are used to the x-ray images. Since, pneumonia can be highly life threatening, the decisions made by the object detection model can influence the lives of thousands of people. It is imperative that we precisely know the performance and the accuracy of the model so that the stakeholders have a wider view of how trustworthy the results are. There is a need to understand the performance and accuracy of the underlying deep learning model. The architecture of the model such as number of neural layers or the computation cost poses a great impact on the accuracy and efficiency. In this paper, our contribution is twofold. Firstly, we have proposed a new deep learning (DL) strategy for the automated identification of pneumonia employing a convolutional neural network (CNN) with improved accuracy. We have introduced a hybrid model in which we have used a pre-trained CNN model, a vision transformer (ViT) and applied N times and N number of CNN layers to improve the accuracy and computational performance. We have identified a point where the accuracy of the model is highest and computational cost is acceptable. Secondly, we have compared our proposed model with different other classifiers such as You Only Look Once (YOLO) v4, and CNN and its multiple layers. Using a publicly accessible dataset, this study attempted to pre-process the input chest X-ray images in order to detect the presence of pneumonia. The experimental results have revealed that the accuracy level of the proposed hybrid CNN model reached a level of 97.6% on datasets of varying sizes.

With the fast development of 5G networks, the need to ensure that these networks are secured against cyber threats, which are advanced and constantly evolving, is of immense importance. In this paper, an intrusion detection system based on deep learning using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture is introduced to meet the security requirements of 5G wireless networks. The model also showed high accuracy, precision, and recall when training and testing on various publicly available datasets (CICIDS 2017 and NSL-KDD) by identifying different types of attacks, namely Distributed Denial of Service (DDoS), SQL Injection, and Advanced Persistent Threats (APT). Findings reveal that the CNN-LSTM model performs better compared to standard machine learning models such as Support Vector Machines (SVM) and Decision Tree, scoring high with regard to detection abilities and optimizing computing requirements. The model is an effective intrusion detection method in real-time despite training time and latency issues, it is a very useful solution to the fast-developing intrusion detection scenario involving the dynamic 5G network. The study offers an insight into the possibility of deep learning methods in boosting cybersecurity in 5G networks and lays the foundation for newer advances in network security systems. Keywords: 5G Networks, Intrusion Detection System (Ids), Deep Learning, Cnn, Lstm, Hybrid Model, Cybersecurity, Real-Time Detection, Network Security, Machine Learning, Ddos, Apt, Attack Detection.

The demand for safe internet access will increase in the future, as a variety of malicious attacks are targeting fragile devices in the network. For this reason, a network intrusion detection system (NIDS) is utilized to detect and notify attacks. Various algorithms employing pattern analysis techniques have been successfully implemented in NIDS, such as shallow machine learning and deep learning models. However, the problem of an imbalanced dataset is often negligible in intrusion detection. Often, the results achieved by machine learning tend to majority classes and ignore minority classes. Therefore, we propose the focal loss network intrusion detection system (FL-NIDS), an improved classification model, to address the issue of an imbalanced dataset. This paper focuses on finding the most appropriate hyperparameter values for focal loss in intrusion detection systems. To show the effectiveness of the proposed FL-NIDS, we utilize the UNSW-NB15 dataset, which suffers the imbalance classes. Our experiment shows that the FL-NIDS using the best combination parameters of focal loss obtain higher detection accuracy in the majority classes as well as in the minority classes, compared to the general model such as Deep Neural Network (DNN) and Convolutional Neural Network (CNN).