In recent years, attacks on network environments continue to rapidly advance and are increasingly intelligent. Accordingly, it is evident that there are limitations in existing signature-based intrusion detection systems. In particular, for novel attacks such as Advanced Persistent Threat (APT), signature patterns have problems with poor generalization performance. Furthermore, in a network environment, attack samples are rarely collected compared to normal samples, creating the problem of imbalanced data. Anomaly detection using an autoencoder has been widely studied in this environment, and learning is through semi-supervised learning methods to overcome these problems. This approach is based on the assumption that reconstruction errors for samples that are not used for training will be large, but an autoencoder is often over-generalized and this assumption is often broken. In this paper, we propose a network intrusion detection method using a memory-augmented deep auto-encoder (MemAE) that can solve the over-generalization problem of autoencoders. The MemAE model is trained to reconstruct the input of an abnormal sample that is close to a normal sample, which solves the generalization problem for such abnormal samples. Experiments were conducted on the NSL-KDD, UNSW-NB15, and CICIDS 2017 datasets, and it was confirmed that the proposed method is better than other one-class models.

Recently, several plant pathogens have become more active due to temperature increases arising from climate change, which has caused damage to various crops. If climate change continues, it will likely be very difficult to maintain current crop production, and the problem of a shortage of expert manpower is also deepening. Fortunately, research on various early diagnosis systems based on deep learning is actively underway to solve these problems, but the problem of lack of diversity in some hard-to-collect disease samples remains. This imbalanced data increases the bias of machine learning models, causing overfitting problems. In this paper, we propose a data augmentation method based on an image-to-image translation model to solve the bias problem by supplementing these insufficient diseased leaf images. The proposed augmentation method performs translation between healthy and diseased leaf images and utilizes attention mechanisms to create images that reflect more evident disease textures. Through these improvements, we generated a more plausible diseased leaf image compared to existing methods and conducted an experiment to verify whether this data augmentation method could further improve the performance of a classification model for early diagnosis of plants. In the experiment, the PlantVillage dataset was used, and the extended dataset was built using the generated images and original images, and the performance of the classification models was evaluated through the test set.

As cyberattacks become more intelligent, the difficulty increases for traditional intrusion detection systems to detect advanced attacks that deviate from previously stored patterns. To solve this problem, a deep learning-based intrusion detection system model has emerged that analyzes intelligent attack patterns through data learning. However, deep learning models have the disadvantage of having to re-learn each time a new cyberattack method emerges. The time required to learn a large amount of data is not efficient. In this paper, an experiment was conducted using the Leipzig Intrusion Detection Data Set (LID-DS), which is a host-based intrusion detection data set released in 2018. In addition, in order to evaluate and improve the performance of the system, a host-based intrusion detection model consisting of pre-processing, vector-to-image processing, training and testing steps is proposed. In the training and testing steps, a Siamese Convolutional Neural Network (Siamese-CNN) is constructed using the few-shot learning method, which shows excellent performance by learning a small amount of data. Siamese-CNN determines whether the attack type is the same based on the similarity score of each cyberattack sample converted to an image. The accuracy was calculated using the few-shot learning technique. The performance of the Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN are compared to confirm the performance of Siamese-CNN. As a result of measuring the accuracy, precision, recall, and F1-score indicators, it was confirmed that the recall of the Siamese-CNN model proposed in this study increased by about 6% compared to the Vanilla-CNN model.

The network intrusion detection system is a core technology of network security that detects packets for malicious activities occurring in the network and is an essential element for stable services in extended network environments such as big data and IoT. These network intrusion detection systems have been studied together with machine learning and deep learning, but performance is not guaranteed in the actual environment or the class balance problem has not been solved. Therefore, in this study, we investigate the performance of a discretization preprocessing method with a CNN-based classifier on the class imbalance problem of network traffic data. The preprocessing method adds a discretization algorithm for continuous variables in the commonly used conventional preprocessing method and converts 1D network packet vectors into 2D image vectors to improve relational analysis and generalization performance. Since the convolution neural network has immutability to the input data, it improves statistical efficiency in learning network packets converted into images. To evaluate the proposed model, we compared the computational complexity and generalization performance using NSL-KDD and CSE-CIC-IDS 2018, which is a representative network packet data. As a result of the experiment, it was confirmed that in the case of computational complexity, training time and parameters were reduced compared to the model designed similarly to the proposed model, and accuracy and F1 score improved in generalization performance.

Wireless Sensor Network (WSN) technology, for services that are difficult to access or which need to be continuously monitored regardless of location, needs further research and development due to an expansion of fields where it can be applied and due to increases in efficiency. In particular, in the field of defense, research on the latest IT technologies including sensor networks is being actively conducted as an alternative to the risky use of personnel in areas such as surveillance and surveillance reconnaissance. This paper experimented with analyzing the conditions necessary for increasing the energy efficiency of the nodes constituting a sensor network using a clustering routing technique and a location-based routing technique. The derived factors include a method for selecting a cluster head (CH), a method for establishing a path from each channel to a base station (BS), and a method for transmitting collected data. We experimented with the derived factors and proposed a WSN configuration method that increases the energy efficiency of each node by applying optimal results and methods that were verified experimentally.