Computing and Information Systems - Research Publications
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ItemNo Preview AvailableA Group Formation Game for Local Anomaly Detection(IEEE, 2023-01-01)
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ItemNo Preview AvailableOnline Trajectory Anomaly Detection Based on Intention Orientation(IEEE, 2023-01-01)
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ItemNo Preview AvailableRobust Wireless Network Anomaly Detection with Collaborative Adversarial Autoencoders(Institute of Electrical and Electronics Engineers, 2023)Anomaly detection is often deployed in centralised systems, for which critical failure points exist. However, the rising availability of low-cost, wireless-connected devices introduces opportunities for new anomaly detection techniques that leverage more robust topologies. In this paper, we propose a novel collaborative training scheme for anomaly detection models that involves sharing machine learning models amongst devices for incremental training. Using the Adversarial Autoencoder architecture, pseudo-rehearsal, and gossip-based communication, our framework provides all participating devices with a structured representation of other devices' data, so that training can continue even in the event of a device failure, with a 43 % smaller performance degradation than state of the art alternatives. Under both optimal conditions and those with device failure, our model consistently exhibits better anomaly detection performance.
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ItemImproving the quality of explanations with local embedding perturbations(ACM, 2019-07-25)Classifier explanations have been identified as a crucial component of knowledge discovery. Local explanations evaluate the behavior of a classifier in the vicinity of a given instance. A key step in this approach is to generate synthetic neighbors of the given instance. This neighbor generation process is challenging and it has considerable impact on the quality of explanations. To assess quality of generated neighborhoods, we propose a local intrinsic dimensionality (LID) based locality constraint. Based on this, we then propose a new neighborhood generation method. Our method first fits a local embedding/subspace around a given instance using the LID of the test instance as the target dimensionality, then generates neighbors in the local embedding and projects them back to the original space. Experimental results show that our method generates more realistic neighborhoods and consequently better explanations. It can be used in combination with existing local explanation algorithms.
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ItemNo Preview AvailableIt's PageRank All The Way Down: Simplifying Deep Graph Networks(Society for Industrial and Applied Mathematics, 2023-01-01)First developed to rank website relevance, PageRank has become ubiquitous in many areas of graph machine learning including deep learning. We demonstrate that a number of recently published deep graph neural networks are qualitatively equivalent to shallow networks utilizing Personalized PageRank (PPR), and that their performance improvements over existing PPR implementations can be fully explained by hyperparameter choices. We also show that PPR with these hyperparameters outperform more recently published sophisticated variations of PPR-based graph neural networks, and present efficient implementations that reduce training times and memory requirements while improving scalability.
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ItemNo Preview AvailableWireless Network Simulation to Create Machine Learning Benchmark Data(IEEE, 2022-01-01)
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ItemEmbracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data(AAAI Press, 2021)With the rapid evolution of social media, fake news has become a significant social problem, which cannot be addressed in a timely manner using manual investigation. This has motivated numerous studies on automating fake news detection. Most studies explore supervised training models with different modalities (e.g., text, images, and propagation networks) of news records to identify fake news. However, the performance of such techniques generally drops if news records are coming from different domains (e.g., politics, entertainment), especially for domains that are unseen or rarely-seen during training. As motivation, we empirically show that news records from different domains have significantly different word usage and propagation patterns. Furthermore, due to the sheer volume of unlabelled news records, it is challenging to select news records for manual labelling so that the domain-coverage of the labelled dataset is maximized. Hence, this work: (1) proposes a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains; and (2) introduces an unsupervised technique to select a set of unlabelled informative news records for manual labelling, which can be ultimately used to train a fake news detection model that performs well for many domains while minimizing the labelling cost. Our experiments show that the integration of the proposed fake news model and the selective annotation approach achieves state-of-the-art performance for cross-domain news datasets, while yielding notable improvements for rarely-appearing domains in news datasets.
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ItemSummarizing Significant Changes in Network Traffic Using Contrast Pattern Mining(Association for Computing Machinery, 2017)Extracting knowledge from the massive volumes of network traffic is an important challenge in network and security management. In particular, network managers require concise reports about significant changes in their network traffic. While most existing techniques focus on summarizing a single traffic dataset, the problem of finding significant differences between multiple datasets is an open challenge. In this paper, we focus on finding important differences between network traffic datasets, and preparing a summarized and interpretable report for security managers. We propose the use of contrast pattern mining, which finds patterns whose support differs significantly from one dataset to another. We show that contrast patterns are highly effective at extracting meaningful changes in traffic data. We also propose several evaluation metrics that reflect the interpretability of patterns for security managers. Our experimental results show that with the proposed unsupervised approach, the vast majority of extracted patterns are pure, i.e., most changes are either attack traffic or normal traffic, but not a mixture of both.
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ItemMining Rare Recurring Events in Network Traffic using Second Order Contrast Patterns(IEEE, 2021)Data mining techniques such as contrast pattern mining provide a promising approach to detecting and characterizing changes in network traffic. However, a major challenge for network managers is how to prioritize their analysis of these changes, without being overwhelmed by uninformative patterns. In particular, some changes in traffic occur on a regular basis, such as system backups, and it is important to filter out these rare recurring events, so that network managers can focus on new events. In this paper we address the problem of identifying rare recurring events in network traffic, and we propose a novel solution to detecting new events based on the approach of mining second order contrast patterns. Based on an empirical evaluation using a variety of real traffic sources, we show that our method can achieve high accuracy and F1-Score in detecting new events. Our work demonstrates the importance of higher order contrast pattern mining in practice, and provides an effective method for finding such higher order patterns in large datasets.