Applying convolutional neural network for network intrusion detection

Ravi, Vinayakumar; Soman, K. P.; Poornachandran, Prabaharan

doi:10.1109/icacci.2017.8126009

Cited by 424 publications

(176 citation statements)

References 12 publications

Supporting

Mentioning

175

Contrasting

Order By: Relevance

“…Use of 1D-CNN as compared to other ML methods is becoming popular because of its good feature extraction ability. Vinayakumar et al used 1D-CNN in combination with RNN, LSTM and gated recurrent units for intrusion detection in network traffic[231]. They evaluated performance of the proposed models on KDDCup 99 dataset consisting of network traffic of TCP/IP packets and 49 showed that CNN significantly surpasses the performance of classical ML models.…”

mentioning

confidence: 99%

A survey of the recent architectures of deep convolutional neural networks

et al. 2020

View full text Add to dashboard Cite

Deep Convolutional Neural Networks (CNNs) are a special type of Neural Networks, which have shown exemplary performance on several competitions related to Computer Vision and Image Processing. Interesting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, Speech Recognition, etc. The powerful learning ability of deep CNN is largely due to the use of multiple feature extraction stages that can automatically learn representations from the data. Availability of a large amount of data and improvements in the hardware technology have accelerated the research in CNNs, and recently very interesting deep CNN architectures have been reported. In fact, several interesting ideas to bring advancements in CNNs have been explored such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the major improvement in representational capacity of the deep CNN is achieved through architectural innovations. Especially, the idea of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing has gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges and applications of CNN are also provided. CNNs are the best among learning algorithms in understanding images content, and have shown exemplary results in segmentation, classification, detection, and retrieval related tasks [8], [9]. The success of CNNs has captured attention beyond academia. In industry, companies such as Google, Microsoft, AT&T, NEC, and Facebook have developed active research groups for exploring new architectures of CNN [10]. At present, most of the frontrunners of image processing and computer vision competitions are employing deep CNN based models.The attractive feature of CNN is its ability to exploit spatial or time correlation of the data. The topology of CNN is divided into multiple learning stages composed of a combination of the convolutional layers, non-linear processing units, and subsampling layers [11]. CNNs are feedforward multilayered hierarchical networks that are similar to fully connected neural network where each layer, using a bank of convolutional kernels, performs multiple transformations [12]. Convolution operation extracts useful features from locally correlated data points. Output of the convolutional kernels is assigned to non-linear processing unit (activation function), which not only helps in learning abstractions but also emb...

show abstract

mentioning

confidence: 99%

A survey of the recent architectures of deep convolutional neural networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…This algorithm was earlier explored for sense disambiguation of a native language (Tamil), having rich feature representation presented in his work by Anand Kumar et al (2014a), and is also implemented in his work (2014b). A simple one dimension convolution neural networks model is also illustrated upon, based on the works by Vinayakumar et al (2017). The CNN model is fixed on an empirical method where the representation is convoluted with twenty filters, of size three, on a batch size of sixty-four, with activation ReLU over a wayward ten epochs, which are flattened and reduced to thirty-two and later to one at the final layer for evaluation.…”

Section: Methodsologymentioning

confidence: 99%

AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.

Vinayan

M²

2018

Proceedings of the 12th International Workshop on Semantic Evaluation

View full text Add to dashboard Cite

The "Capturing Discriminative Attributes" sharedtask is the tenth task, conjoint with SemEval2018. The task is to predict if a word can capture distinguishing attributes of one word from another. We use GloVe word embedding, pre-trained on openly sourced corpus for this task. A base representation is initially established over varied dimensions. These representations are evaluated based on validation scores over two models, first on an SVM based classifier and second on a one dimension CNN model. The scores are used to further develop the representation with vector combinations, by considering various distance measures. These measures correspond to offset vectors which are concatenated as features, mainly to improve upon the F1score, with the best accuracy. The features are then further tuned on the validation scores, to achieve highest F1score. Our evaluation narrowed down to two representations, classified on CNN models, having a total dimension length of 1204 & 1203 for the final submissions. Of the two, the latter feature representation delivered our best F1score of 0.658024 (as per result 1 .)

show abstract

“…CNN-based anomaly detection methods have been mainly applied to intrusion detection [60,61] by preprocessing data samples with float and integer attributes into an image form convenient for CNN processing. In a more recent study, Kwon et al [62] assess several CNN architectures for anomaly detection using different network traffic datasets by comparing their performance to other techniques including Variational Autoencoders (VAE), Fully Connected Networks (FCN) [84] and LSTM.…”

Section: Recent Advances In Convolutional Neural Networkmentioning

confidence: 99%

Recent Advances in Anomaly Detection Methods Applied to Aviation

2019

View full text Add to dashboard Cite

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

show abstract

Applying convolutional neural network for network intrusion detection

Cited by 424 publications

References 12 publications

A survey of the recent architectures of deep convolutional neural networks

A survey of the recent architectures of deep convolutional neural networks

AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.

Recent Advances in Anomaly Detection Methods Applied to Aviation

Contact Info

Product

Resources

About