Research on Application of Convolutional Neural Network in Intrusion Detection

At present, the electric power information system has been more widely used. The power sector in the realization of management information at the same time, its security problems are also increasingly severe. It can be said that the security problem of the electric power information has become a hidden danger to the safe and stable operation of the power system. There is a corresponding intrusion problem in the power information network, which affects the stability and security of the power system to a great extent. Aiming at the problem of a long time and low accuracy of network intrusion detection in the power monitoring systems, this paper proposes a method of network intrusion detection in power monitoring systems based on data mining technology. The method can ensure the safe and stable operation of the power monitoring system. This method needs to extract the data characteristics of network intrusion as the initial data set of intrusion detection method, and then apply the clustering partition method to preprocess the data to provide accurate data basis for intrusion detection method, and finally, based on data mining technology, complete the intrusion detection of power monitoring system network. A simulation experiment is designed to test the feasibility and accuracy of the method, and the experimental results show that, compared with the traditional intrusion detection method, the proposed method is faster by about 1.8s, the intrusion detection accuracy is higher by 15.6% for 500 groups of data, and the false alarm rate is lower by 12.3%. It can be proved that the design method in this paper has high efficiency and accuracy, and can provide technical support for the safe operation of the power system.

Intrusion detection of network traffic is essential in the field of network security. The types of network traffic are diverse. Their attributes are highly correlated at the same time and are continuous in time. However, existing deep learning methods do not use them together for classification. The traditional intrusion detection methods that rely on manual extraction features have problems such as high false positive rate and low recognition performance. This paper proposes a deep learning method DeepIDN that uses the spatial and temporal features of data attributes to perform classification tasks and applies it to intrusion detection. DeepIDN uses a two-dimensional convolution operation to construct a convolutional neural network (CNN) layer to extract the association between data attributes and establish a spatial feature model. Then, by constructing a long short-term memory (LSTM) layer to extract time-related correlation between features, a relational model on time series is established. Finally, using the support vector machine as a classifier, the intrusion detection of network traffic is realized, and the prediction performance is greatly improved. Compared to traditional methods, DeepIDN does not require a lot of data preprocessing workload. The experimental results show that compared with the similar intrusion detection methods, the accuracy of DeepIDN for malicious network traffic judgment is significantly improved, reaching 98.66% training accuracy and 97.15% testing accuracy and higher robustness during the training and testing.

With the popularization and development of network knowledge, network intruders are increasing, and the attack mode has been updated. Intrusion detection technology is a kind of active defense technology, which can extract the key information from the network system, and quickly judge and protect the internal or external network intrusion. Intrusion detection is a kind of active security technology, which provides real-time protection for internal attacks, external attacks and misuse, and it plays an important role in ensuring network security. However, with the diversification of intrusion technology, the traditional intrusion detection system cannot meet the requirements of the current network security. Therefore, the implementation of intrusion detection needs diversifying. In this context, we apply neural network technology to the network intrusion detection system to solve the problem. In this paper, on the basis of intrusion detection method, we analyze the development history and the present situation of intrusion detection technology, and summarize the intrusion detection system overview and architecture. The neural network intrusion detection is divided into data acquisition, data analysis, pretreatment, intrusion behavior detection and testing.

Probabilistic Neural Network(PNN) based on Alphastable distribution is proposed to improve former algorithm's poor fitting ability caused by independent identically distributed Gaussian assumption in intrusion detection's non-Gaussian data. Fast Fourier Transform Numerical Computation is used in the algorithm to establish its Alphastable distribution probability density function(PDF), and then the Samples Characteristic Function Algorithm is adopted to estimate the parameters, and finally Alpha-stable distribution PDF instead of Gaussian function is used as the activation function of PNN. In the simulation, intrusion detection dataset, KDD CUP 99, is used to validate the algorithm, which shows that compared with previous PNN and BPNN, the new algorithm has a stronger ability of fitting the non-Gaussian data and has improved the detection rate and false negative rate in intrusion detection greatly while the false alarm rate is stable.

In order to overcome the separately selection advantages of traditional feature and RBF neural network parameter, increase accuracy rate of network’s intrusion detection, there came up with a research on neural network intrusion detection of improved particle swarm optimization. According to optimize the feature selection of network and RBF neural network parameter, established a neural network intrusion detection model of IPSO-RBF, made convergence and disturbance variation analysis on improving optimized particle swarm optimization, finally made a experimental simulation to this detection model, the result showed: compared with traditional models, the neural network intrusion detection of improved particle swarm optimization was faster and has a higher accuracy rate as well as efficiency.

Convolutional neural networks (CNN) applications range from recognition and reasoning (such as handwriting recognition, facial expression recognition and video surveillance) to intelligent text applications such as semantic text analysis and natural language processing applications. Two key observations drive the design of a new architecture for CNN. First, CNN workloads exhibit a widely varying mix of three types of parallelism: parallelism within a convolution operation, intra-output parallelism where multiple input sources (features) are combined to create a single output, and inter-output parallelism where multiple, independent outputs (features) are computed simultaneously. Workloads differ significantly across different CNN applications, and across different layers of a CNN. Second, the number of processing elements in an architecture continues to scale (as per Moore's law) much faster than off-chip memory bandwidth (or pin-count) of chips. Based on these two observations, we show that for a given number of processing elements and off-chip memory bandwidth, a new CNN hardware architecture that dynamically configures the hardware on-the-fly to match the specific mix of parallelism in a given workload gives the best throughput performance. Our CNN compiler automatically translates high abstraction network specification into a parallel microprogram (a sequence of low-level VLIW instructions) that is mapped, scheduled and executed by the coprocessor. Compared to a 2.3 GHz quad-core, dual socket Intel Xeon, 1.35 GHz C870 GPU, and a 200 MHz FPGA implementation, our 120 MHz dynamically configurable architecture is 4x to 8x faster. This is the first CNN architecture to achieve real-time video stream processing (25 to 30 frames per second) on a wide range of object detection and recognition tasks.

Aiming at the problem of low detection accuracy of network traffic data types by traditional intrusion detection methods, we propose an improved Harris Hawk hybrid intrusion detection method to enhance the detection capability. The improved Harris Hawk optimization algorithm is used as a feature selection scheme to reduce the impact of redundant and noisy features on the performance of the classification model. The algorithm introduces the singer map to initialise the population, uses multi-information fusion to obtain the best prey position, and applies the sine function-based escape energy to execute a prey search strategy to obtain the optimal subset of features. In addition, the original data is preprocessed by the k-nearest neighbour and deep denoising autoencoder (KNN-DDAE) to relieve the imbalance problem of the network traffic data. Finally, a deep neural network (DNN) is used to complete the classification. Simulation experiments are conducted on the dataset NSL-KDD, KDD CUP99, and UNSW-NB15. The results show that our feature selection and data balancing scheme greatly improves the detection accuracy. In addition, the detection performance of this method is better than the current popular intrusion detection schemes.

Network intrusion detection is of great significance for network security in Local Area Network (LAN). Traditional methods such as firewalls do not completely protect against attacks on the LAN due to lack of continuous learning. Recently, the ability of convolutional neural networks (CNN) to extract features in the field of computer vision has received extensive attention. CNN can automatically extract effective complex features to adapt to constantly changing environments, which is especially important in network intrusion detection. In this paper, we focus on network security in the LAN. We propose an approach based on CNN to implement intrusion detection in LAN. This approach can effectively identify network attacks and has an accuracy of 98.34% on the KDD99 dataset. The experimental results show that the proposed approach based on the CNN has high accuracy in intrusion detection.

Early detection and treatment are crucial for recovery from cancer. Due to rapid advancement in medical imaging techniques, today doctors can scan various body parts from different perspectives to diagnose cancer or see the progress during a course of treatment. Such techniques are less painful and require less preparation and recovery. The recent evolution of AI, particularly deep learning algorithms have shown potential in computer-aided diagnosis of cancer. These include analysis of cancer images for detection of tumor region, prognosis, skin tissue inspection, etc. Convolutional Neural Network (CNN) based image processing techniques are showing promising results in the diagnosis of breast cancer, skin cancer, prostate cancer, lung cancer, etc. Literature shows many other CNN-based models such as feed-forward neural network, Deep CNN, Multi-Crop CNN, fully convolutional networks (FCNs), Deep Fully Convolutional Network (DFCNet), Recurrent neural networks, etc. The CNN focus solely on the image-specific features and hence require a lesser number of input parameters. A CNN model reduces the size of the input image vector without losing the features critical for making an accurate prediction. This makes it easier to process and reduces computational complexity. CNNs are also not sensitive to the object positioning in an image since the approximate position of image motifs relative to others is known. CNN deals with the variations in images through augmentation and other pre-processing techniques. This makes them robust to various straining conditions in images. This chapter presents an overview of a generic CNN architecture followed by a discussion on major advances in CNN models and their applications for cancer image analysis.

An anomaly-dependent network intrusion detection method is proposed by presenting a hybrid of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) approaches where it detects various network attacks by training a CNN on the NSL-KDD dataset. The proposed approach is able to automatically discovers significant traits without human intervention and achieves higher accuracy in detecting attacks which can improve the performance of intrusion detection systems. The CNN-LSTM approach is applied to detect attacks such as DoS, Probe, U2R, and R2L, and identify normal traffic at the same time. The experimental result reached a low false-positive rate, a high accuracy rate, and a low time average. The results show that the proposed approach outperformed the current state-of-the-art by 4.69% in terms of accuracy and conventional CNN achieved a greater accuracy of roughly 3% compared to the current approach, which amounted to 98.95%, 97.81%, and 94.26% respectively. The consistency of the simulated attacks is unequal which leads to issues in the prediction, therefore, additional efforts to collect more consistent traffic on unusual attacks are required. The proposed approach is targeted to be applied as an effective tool for solving complex classification problems such as NIDS.

Objectives: The paper presents application of convolution neural network and artificial neural network for image classification problem for clothing dataset along with their performance comparison against different optimizers. The major objective of this paper is to perform image classification on fashion-mnist clothing dataset images. Methods: The methods used here are, the traditional ANN and CNN. Here image classification is performed on Fashion-mnist, clothing dataset using CNN and ANN with different optimizers. The performance of the working of ANN and CNN in classifying images from fashion-mnist dataset is compared against different optimizers namely stochastic gradient Descent, Adagrad, RMS prop and Adam optimizer. Findings: The study found that CNN worked better than ANN yielding training accuracy of 95%, 93% and testing accuracy of 91%, 89% when used with Adam and RmsProp respectively. Novelty: The novelty of this work is to present a comparative study of image classification using CNN, ANN using different optimizers, since not many studies or research articles showed the performance comparison of traditional and convolution neural networks in image classification along with different optimizers. Since the real-world scenarios of today require enormous data to be processed, CNN can fit well to diversify applications since they highly reduce the number of parameters to be trained that speeds up the training process. Moreover, to be specific on image classification problems they require the best and most prominent features to be detected and uncovered; this can be achieved using CNN since it has the concept of convolution using filters at its Core. Hence, CNN is highly recommended for such image classification applications than the traditional artificial-neural-networks because of the aforementioned reasons. Keywords: ANN (Artificial NeuralNetworks); CNN (ConvolutionNeural Networks); Optimization; SGD (Stochastic Gradient Descent); RmsProp (Root mean Square propagation); Adam (Adaptive moment estimation); AdaGrad (Adaptive Gradient)

Network Security Intrusion Detection Methods Combining Optimization Algorithms and Neural Networks

Abstract. Deep learning models have demonstrated remarkable success in fields such as language processing and computer vision, routinely employed for tasks like language translation, image classification, and anomaly detection. Recent advancements in ocean sciences, particularly in data assimilation (DA), suggest that machine learning can emulate dynamical models, replace traditional DA steps to expedite processes, or serve as hybrid surrogate models to enhance forecasts. However, these studies often rely on ocean models of intermediate complexity, which involve significant simplifications that present challenges when transitioning to full-scale operational ocean models. This work explores the application of convolutional neural networks (CNNs) in data assimilation within the context of the HYbrid Coordinate Ocean Model (HYCOM) in the Gulf of Mexico. The CNNs are trained to correct model errors from a 2-year, high-resolution (1/25°) HYCOM dataset, assimilated using the Tendral Statistical Interpolation System (T-SIS). The CNNs are trained to replicate the increments generated by the T-SIS data assimilation package, aiming to correct model forecasts of sea surface temperature (SST) and sea surface height (SSH). The inputs to the CNNs include real satellite observations of SST from the Group for High Resolution Sea Surface Temperature (GHRSST), along-track altimeter SSH observations (ADT), the model background state (previous forecast), and the innovations (differences between observations and background). We assess the performance of the CNNs across five controlled experiments, designed to provide insights into their application in environments governed by full primitive equations, real observations, and complex topographies. The experiments focus on evaluating (1) the architecture and complexity of the CNNs, (2) the type and quantity of observations, (3) the type and number of assimilated fields, (4) the impact of training window size, and (5) the influence of coastal boundaries. Our findings reveal significant correlations between the chosen training window size – a factor not commonly examined – and the CNNs' ability to assimilate observations effectively. We also establish a clear link between the CNNs' architecture and complexity and their overall performance. This research uses artificial intelligence to enhance ocean forecasting in the Gulf of Mexico. By using convolutional neural networks, the study improves predictions of sea temperatures and heights by integrating real satellite data with existing models. Through five comprehensive experiments, the team found that the amount of training data and the design of the neural networks significantly affect accuracy. These insights pave the way for faster, more reliable ocean models, benefiting environmental monitoring and maritime operations.

Against the backdrop of accelerated digital transformation and evolving cyberattacks, traditional intrusion detection methods face severe challenges due to their limited feature extraction capabilities, fragmented spatiotemporal feature processing, and insufficient model adaptability. These challenges include a high proportion of encrypted traffic and the emergence of novel attacks. To enhance detection accuracy and adaptability, this paper proposes a Multi-Scale Convolutional Neural Network-Bidirectional Long Short-Term Memory Network (MSCNN-BiLSTM) model optimized by genetic algorithms. This approach utilizes the multi-branch structure of MSCNN (with 1x1, 3x3, and 5x5 convolutional kernels) coupled with a spatial attention mechanism to strengthen spatial feature extraction. It combines BiLSTM and a self-attention mechanism to capture temporal dependencies, and employs genetic algorithms to automatically optimize hyperparameters, achieving efficient synchronous learning of spatiotemporal features in network traffic. Experimental results on the UNSW-NB15 dataset demonstrate that the model achieves an accuracy of 96.8%, a recall rate of 96.4%, a precision rate of 97.2%, and an F1-score of 96.7%. In robustness tests, the performance loss under noise and adversarial attacks is controllable (average <5%). The average performance loss in cross-scenario transfer experiments is only 4.2%, significantly outperforming comparative models. The model proposed in this paper effectively addresses the limitations of traditional methods in feature extraction and adaptability. Its main contribution lies in the innovative integration of a deep spatiotemporal feature learning framework, providing key technical support for building a highly accurate and strongly adaptive network intrusion detection system. This has significant theoretical value and broad application prospects.

Intrusion detection algorithms based on deep learning are currently a hot topic in the field of intrusion detection research, but most of the research focuses on how to improve the algorithms to improve the accuracy of intrusion detection, while ignoring the problem that the limited labeled data generated by a single organization is not enough to train a deep model with high accuracy in practice. The paper proposes a federal learning and convolutional neural network-based intrusion detection method that can expand the data volume by jointly training the model with data sets from multiple participants. The method uses a federation learning framework to design a deep learning-based intrusion detection model. The data dimensionality is first reconstructed by data padding to form two-dimensional data, then the DCNN network is used for feature extraction and learning under the mechanism of federation learning, and finally the model is trained with a softmax classifier for detection. In addition, aiming at the over-fitting problem caused by the compression and over-parameterization of deep neural networks, this paper proposes a model pruning method based on sparse convolutional neural network according to the weight parameters of the convolutional layer and the Batch Normalization layer (BN). This section will introduce the implementation details of this method. The experimental results show that this method largely reduces the training time and maintains a high detection rate. In addition, the model ensures data security and privacy compared to typical intrusion detection models.