Existing generative adversarial networks (GANs), primarily used for creating fake image samples from natural images, demand a strong dependence (i.e., the training strategy of the generators and the discriminators require to be in sync) for the generators to produce as realistic fake samples that can “fool” the discriminators. We argue that this strong dependency required for GAN training on images does not necessarily work for GAN models for network intrusion detection tasks. This is because the network intrusion inputs have a simpler feature structure such as relatively low-dimension, discrete feature values, and smaller input size compared to the existing GAN-based anomaly detection tasks proposed on images. To address this issue, we propose a new Bidirectional GAN (Bi-GAN) model that is better equipped for network intrusion detection with reduced overheads involved in excessive training. In our proposed method, the training iteration of the generator (and accordingly the encoder) is increased separate from the training of the discriminator until it satisfies the condition associated with the cross-entropy loss. Our empirical results show that this proposed training strategy greatly improves the performance of both the generator and the discriminator even in the presence of imbalanced classes. In addition, our model offers a new construct of a one-class classifier using the trained encoder–discriminator. The one-class classifier detects anomalous network traffic based on binary classification results instead of calculating expensive and complex anomaly scores (or thresholds). Our experimental result illustrates that our proposed method is highly effective to be used in network intrusion detection tasks and outperforms other similar generative methods on two datasets: NSL-KDD and CIC-DDoS2019 datasets.

In recent years, the internet of things has been widely utilized in various fields, such as in smart factories or connected cars. As its domain of application has expanded, it has begun to be employed using multi-server architectures for a more efficient use of resources. However, because users wishing to receive IoT(Internet of Things) services connect to multi-servers over wireless networks, this can expose systems to various attacks and result in serious security risks. To protect systems (and users) from potential security vulnerabilities, a secure authentication technology is necessary. In this paper, we propose a smart card-based authentication protocol, which performs the authentication for each entity by allowing users to go through the authentication process using a smart card transmitted from an authentication server, and to login to a server connected to the IoT. Furthermore, the security of our proposed authentication protocol is verified by simulating a formal verification scenario using AVISPA(Automated Validation of Internet Security Protocols and Applications), a security protocol-verification tool.

The majority of recent cyber incidents have been caused by malware. According to a report by Symantec, an average of one million malicious codes is found daily. Automated static and dynamic analysis technologies are generally applied to cope with this, but most of the new malicious codes are the mutants of existing malware. In this paper, we present technology that automatically detects the n-gram and clustering coefficient-based malware mutants and that automatically groups the different types of malware. We verified our system by applying more than 2600 malicious codes. Our proposed technology does more than just respond to malware as it can also provide the ground for the effective analysis of new malware, the trend analysis of a malware group, the automatic identification of specific malware, and the analysis of the estimated trend of an attacker.

Performance Improvement of a Movie Recommendation System based on Personal Propensity and Secure Collaborative Filtering Collaborative Filtering;Movie Recommendation System;Personal Propensity;Security;Push Stack; There are many recommendation systems available to provide users with personalized services. Among them, the most frequently used in electronic commerce is 'collaborative filtering', which is a technique that provides a process of filtering customer information for the preparation of profiles and making recommendations of products that are expected to be preferred by other users, based on such information profiles. Collaborative filtering systems, however, have in their nature both technical issues such as sparsity, scalability, and transparency, as well as security issues in the collection of the information that becomes the basis for preparation of the profiles. In this paper, we suggest a movie recommendation system, based on the selection of optimal personal propensity variables and the utilization of a secure collaborating filtering system, in order to provide a solution to such sparsity and scalability issues. At the same time, we adopt 'push attack' principles to deal with the security vulnerability of collaborative filtering systems. Furthermore, we assess the system's applicability by using the open database MovieLens, and present a personal propensity framework for improvement in the performance of recommender systems. We successfully come up with a movie recommendation system through the selection of optimal personalization factors and the embodiment of a safe collaborative filtering system.