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.

Over a decade, intelligent and persistent forms of cyber threats have been damaging to the organizations' cyber assets and missions. In this paper, we analyze current cyber kill chain models that explain the adversarial behavior to perform advanced persistent threat (APT) attacks, and propose a cyber kill chain model that can be used in view of cyber situation awareness. Based on the proposed cyber kill chain model, we propose a threat taxonomy that classifies attack tactics and techniques for each attack phase using CAPEC, ATT&CK that classify the attack tactics, techniques, and procedures (TTPs) proposed by MITRE. We also implement a cyber common operational picture (CyCOP) to recognize the situation of cyberspace. The threat situation can be represented on the CyCOP by applying cyber kill chain based threat taxonomy.

Zero dynamics attack is lethal to cyber-physical systems in the sense that it is stealthy and there is no way to detect it. Fortunately, if the given continuous-time physical system is of minimum phase, the effect of the attack is negligible even if it is not detected. However, the situation becomes unfavorable again if one uses digital control by sampling the sensor measurement and using the zero-order-hold for actuation because of the `sampling zeros.' When the continuous-time system has relative degree greater than two and the sampling period is small, the sampled-data system must have unstable zeros (even if the continuous-time system is of minimum phase), so that the cyber-physical system becomes vulnerable to `sampling zero dynamics attack.' In this paper, we begin with its demonstration by a few examples. Then, we present an idea to protect the system by allocating those discrete-time zeros into stable ones. This idea is realized by employing the so-called `generalized hold' which replaces the zero-order-hold.

Distributed Denial of Service (DDoS) attack has been bringing serious security concerns on banks, finance incorporation, public institutions, and data centers. Also, the emerging wave of Internet of Things (IoT) raises new concerns on the smart devices. Software Defined Networking (SDN) and Network Functions Virtualization (NFV) have provided a new paradigm for network security. In this paper, we propose a new method to efficiently prevent DDoS attacks, based on a SDN/NFV framework. To resolve the problem that normal packets are blocked due to the inspection on suspicious packets, we developed a threshold-based method that provides a client with an efficient, fast DDoS attack mitigation. In addition, we use open source code to develop the security functions in order to implement our solution for SDN-based network security functions. The source code is based on NETCONF protocol [1] and YANG Data Model [2].

Cyber-attacks have been evolved in a way to be more sophisticated by employing combinations of attack methodologies with greater impacts. For instance, Advanced Persistent Threats (APTs) employ a set of stealthy hacking processes running over a long period of time, making it much hard to detect. With this trend, the importance of big-data security analytics has taken greater attention since identifying such latest attacks requires large-scale data processing and analysis. In this paper, we present SEAS-MR (Security Event Aggregation System over MapReduce) that facilitates scalable security event aggregation for comprehensive situation analysis. The introduced system provides the following three core functions: (i) periodic aggregation, (ii) on-demand aggregation, and (iii) query support for effective analysis. We describe our design and implementation of the system over MapReduce and high-level query languages, and report our experimental results collected through extensive settings on a Hadoop cluster for performance evaluation and design impacts.