Biblio

While machine learning technologies have been remarkably advanced over the past several years, one of the fundamental requirements for the success of learning-based approaches would be the availability of high-quality data that thoroughly represent individual classes in a problem space. Unfortunately, it is not uncommon to observe a significant degree of class imbalance with only a few instances for minority classes in many datasets, including network traffic traces highly skewed toward a large number of normal connections while very small in quantity for attack instances. A well-known approach to addressing the class imbalance problem is data augmentation that generates synthetic instances belonging to minority classes. However, traditional statistical techniques may be limited since the extended data through statistical sampling should have the same density as original data instances with a minor degree of variation. This paper takes a learning-based approach to data augmentation to enable effective network anomaly detection. One of the critical challenges for the learning-based approach is the mode collapse problem resulting in a limited diversity of samples, which was also observed from our preliminary experimental result. To this end, we present a novel "Divide-Augment-Combine" (DAC) strategy, which groups the instances based on their characteristics and augments data on a group basis to represent a subset independently using a generative adversarial model. Our experimental results conducted with two recently collected public network datasets (UNSW-NB15 and IDS-2017) show that the proposed technique enhances performances up to 21.5% for identifying network anomalies.

Due to the proliferation of Internet-of-Things (IoT) environments, humans working with heterogeneous, smart objects in public IoT environments become more popular than ever before. This situation often requires to establish trust relationships between a user and a smart object for their secure interactions, but without the presence of prior interactions. In this work, we are interested in how a smart object can grant an access right to a human user in the absence of any prior knowledge in which some users may be malicious aiming to breach security goals of the IoT system. To solve this problem, we propose a trust-aware, role-based access control system, namely TARAS, which provides adaptive authorization to users based on dynamic trust estimation. In TARAS, for the initial trust establishment, we take a multidisciplinary approach by adopting the concept of I-sharing from psychology. The I-sharing follows the rationale that people with similar roles and traits are more likely to respond in a similar way. This theory provides a powerful tool to quickly establish trust between a smart object and a new user with no prior interactions. In addition, TARAS can adaptively filter malicious users out by revoking their access rights based on adaptive, dynamic trust estimation. Our experimental results show that the proposed TARAS mechanism can maximize system integrity in terms of correctly detecting malicious or benign users while maximizing service availability to users particularly when the system is fine-tuned based on the identified optimal setting in terms of an optimal trust threshold.