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Early and effective network intrusion detection is deemed to be a critical basis for cybersecurity domain. In the past decade, although a significant amount of work has focused on network intrusion detection, it is still a challenge to establish an intrusion detection system with a high detection rate and a relatively low false alarm rate. In this paper, we have performed a comprehensive empirical study on network intrusion detection as a multiclass classification task, not just to detect a suspicious connection but also to assign the correct type as well. To surpass the previous studies, we have utilized four deep learning models, namely, deep neural networks, long short‐term memory recurrent neural networks, gated recurrent unit recurrent neural networks, and deep belief networks. Our approach relies on the pretraining of the models by exploiting a particle swarm optimization–based algorithm for their hyperparameters selection. In order to investigate the performance differences, we also included two well‐known shallow learning methods, namely, decision forest and decision jungle. Furthermore, we used in our experiments four datasets, which are dedicated to intrusion detection systems to explore various environments. These datasets are KDD CUP 99, NSL‐KDD, CIDDS, and CICIDS2017. Moreover, 22 evaluation metrics are used to assess the model's performance in each of the datasets. Finally, intensive quantitative, Friedman test, and ranking methods analyses of our results are provided at the end of this paper. The results show a significant improvement in the detection of network attacks with our recommended approach.

Steganography is the technique for hiding information within a carrier file so that it is imperceptible for unauthorized parties. In this study, it is intended to combine many techniques to gather a new method for colour image steganography to obtain enhanced efficiency, attain increased payload capacity, posses integrity check and security with cryptography at the same time. Proposed work supports many different formats as payload. In the proposed method, the codeword is firstly formed with secret data and its CRC-32 checksum, then the codeword is compressed by Gzip just before encrypting it by AES, and it is finally added to encrypted header information for further process and then embedded into the cover image. Embedding the encrypted data and header information process utilizes Fisher-Yates Shuffle algorithm for selecting next pixel location. To hide one byte, different LSB (least significant bits) of all colour channels of the selected pixel is exploited. In order to evaluate the proposed method, comparative performance tests are carried out against different spatial image steganographic techniques using some of the well-known image quality metrics. For security analysis, histogram, enhanced LSB and Chi-square analyses are carried out. The results indicate that with the proposed method has an improved payload capacity, security and integrity check for common problems of simple LSB method. Moreover, it has been shown that the proposed method increases the visual quality of the stego image when compared to other studied methods, and makes the secret message difficult to be discovered.

In computer security, masquerade detection is a special type of intrusion detection problem. Effective and early intrusion detection is a crucial factor for computer security. Although considerable work has been focused on masquerade detection for more than a decade, achieving a high level of accuracy and a comparatively low false alarm rate is still a big challenge. In this paper, we present a comprehensive empirical study in the area of anomaly-based masquerade detection using three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Convolutional Neural Networks (CNN). In order to surpass previous studies on this subject, we used three UNIX command line-based datasets, with six variant data configurations implemented from them. Furthermore, static and dynamic masquerade detection approaches were utilized in this study. In a static approach, DNN and LSTM-RNN models are used along with a Particle Swarm Optimization-based algorithm for their hyperparameters selection. On the other hand, a CNN model is employed in a dynamic approach. Moreover, twelve well-known evaluation metrics are used to assess model performance in each of the data configurations. Finally, intensive quantitative and ROC curves analyses of results are provided at the end of this paper. The results not only show that deep learning models outperform all traditional machine learning methods in the literature but also prove their ability to enhance masquerade detection on the used datasets significantly.

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