Arnold P. Boedihardjo

Exploiting efficient data mining techniques to enhance intrusion detection systems

Security is becoming a critical part of organizational information systems. Intrusion detection system (IDS) is an important detection that is used as a countermeasure to preserve data integrity and system availability from attacks. Data mining is being used to clean, classify, and examine large amount of network data to correlate common infringement for intrusion detection. The main reason for using data mining techniques for intrusion detection systems is due to the enormous volume of existing and newly appearing network data that require processing. The amount of data accumulated each day by a network is huge. Several data mining techniques such as clustering, classification, and association rules are proving to be useful for gathering different knowledge for intrusion detection. This paper presents the idea of applying data mining techniques to intrusion detection systems to maximize the effectiveness in identifying attacks, thereby helping the users to construct more secure information systems.

GrammarViz 2.0: a tool for grammar-based pattern discovery in time series

The problem of frequent and anomalous patterns discovery in time series has received a lot of attention in the past decade. Addressing the common limitation of existing techniques, which require a pattern length to be known in advance, we recently proposed grammar-based algorithms for efficient discovery of variable length frequent and rare patterns. In this paper we present GrammarViz 2.0, an interactive tool that, based on our previous work, implements algorithms for grammar-driven mining and visualization of variable length time series patterns1.

AITVS: Advanced Interactive Traffic Visualization System

Transportation and the highway network form the backbone of the total public infrastructure system. As such, planning and monitoring for an effective transportation system is crucial in the building and maintenance of a region’s economy and safety. However, demand for road travel continues to expand as population increases (particularly in the metropolitan areas) while new constructions have not kept pace. According to the Federal Highway Administration, it is forecasted that the volume of freight movement alone is to nearly double by 2020 [1]. Congestion and looming gridlock crises seriously threaten any region’s mobility, safety and economic vitality. A crucial component in addressing these concerns is the development of specific technologies to monitor, model, and optimize traffic flow.

Model-driven matching and segmentation of trajectories

A fundamental problem in analyzing trajectory data is to identify common patterns between pairs or among groups of trajectories. In this paper, we consider the problem of matching similar portions between a pair of trajectories, each observed as a sequence of points sampled from it. We present new measures of trajectory similarity --- both local and global --- between a pair of trajectories to distinguish between similar and dissimilar portions. We then use this model to perform segmentation of a set of trajectories into fragments, contiguous portions of trajectories shared by many of them. Our model for similarity is robust under noise and sampling rate variations. The model also yields a score which can be used to rank multiple pairs of trajectories according to similarity, e.g. in clustering applications. We present quadratic time algorithms to compute the similarity between trajectory pairs under our measures together with algorithms to identify fragments in a large set of trajectories efficiently using the similarity model. Finally, we present an extensive experimental study evaluating the effectiveness of our approach on real datasets, comparing it with earlier approaches. Our experiments show that our model for similarity is highly accurate in distinguishing similar and dissimilar portions as compared to earlier methods even with sparse sampling. Further, our segmentation algorithm is able to identify a small set of fragments capturing the common parts of trajectories in the dataset.

An integrated framework for spatio-temporal-textual search and mining

This paper presents an integrated framework for Spatio-Temporal-Textual (STT) information retrieval and knowledge discovery system. The proposed ensemble framework contains an efficient STT search engine with multiple indexing, ranking and scoring schemes, an effective STT pattern miner with Spatio-Temporal (ST) analytics, and novel STT topic modeling. Specifically, we design an effective prediction prototype with a third-order linear regression model, and present an innovative STT topic modeling relevance ranker to score documents based on inherent STT features under topical space. We demonstrate the framework with a crime dataset from the Washington, DC area from 2006 to 2010 and a global terrorism dataset from 2004 to 2010.

On Locally Linear Classification by Pairwise Coupling

Locally linear classification by pairwise coupling addresses a nonlinear classification problem by three basic phases: decompose the classes of complex concepts into linearly separable subclasses, learn a linear classifier for each pair, and combine pairwise classifiers into a single classifier. A number of methods have been proposed in this framework. However, these methods have two major deficiencies: 1) lack of systematic evaluation of this framework; 2) naive application of clustering algorithms to generate subclasses. This paper proves the equivalence between three popular combination schemas under general settings, defines several global criterion functions for measuring the goodness of subclasses, and presents a supervised greedy clustering algorithm to optimize the proposed criterion functions. Extensive experiments were conducted to validate the effectiveness of the proposed techniques.

TerraNNI: natural neighbor interpolation on a 3D grid using a GPU

With modern focus on LiDAR technology the amount of topographic data, in the form of massive point clouds, has increased dramatically. Furthermore, due to the popularity of LiDAR, repeated surveys of the same areas are becoming more common. This trend will only increase as topographic changes prompt surveys over already scanned terrain, in which case we obtain large spatio-temporal data sets. In dynamic terrains, such as coastal regions, such spatio-temporal data can offer interesting insight into how the terrain changes over time. An initial step in the analysis of such data is to create a digital elevation model representing the terrain over time. In the case of spatio-temporal data sets those models often represent elevation on a 3D volumetric grid. This involves interpolating the elevation of LiDAR points on these grid points. In this paper we show how to efficiently perform natural neighbor interpolation over a 3D volumetric grid. Using a graphics processing unit (GPU), we describe different algorithms to attain speed and GPU-memory trade-offs. Our algorithm extends to higher dimensions. Our experimental results demonstrate that the algorithm is efficient and scalable. Categories and Subject.

Motif discovery in spatial trajectories using grammar inference

Spatial trajectory analysis is crucial to uncovering insights into the motives and nature of human behavior. In this work, we study the problem of discovering motifs in trajectories based on symbolically transformed representations and context free grammars. We propose a fast and robust grammar induction algorithm called mSEQUITUR to infer a grammar rule set from a trajectory for motif generation. Second, we designed the Symbolic Trajectory Analysis and VIsualization System (STAVIS), the first of its kind trajectory analytical system that applies grammar inference to derive trajectory signatures and enable mining tasks on the signatures. Third, an empirical evaluation is performed to demonstrate the efficiency and effectiveness of mSEQUITUR for generating trajectory signatures and discovering motifs.

HOMES: highway operation monitoring and evaluation system

This work proposes high-performance critical visualization techniques for exploring real-time and historical traffic loop-detector data. Until recently, it has been difficult to discover trends, identify patterns, or locate abnormalities within the massive collection of traffic data. Many of the current visualization techniques do not scale to large data sets and are not practical for interactive visualization. We have developed an effective visualization system, Highway Operation Monitoring and Evaluation System (HOMES), for observing the summarization of spatiotemporal patterns and trends in traffic data. HOMES is designed for browsing the spatial-temporal dimension hierarchy via integrated roll-up and drill-down operations. The identified traffic patterns and rules can assist decision-making for transportation managers, establish traffic models for researchers and planners, and allow travelers to select commuting routes.

Forecasting location-based events with spatio-temporal storytelling

Storytelling, the act of connecting entities through relationships, provides an intuitive platform for exploratory analysis. This paper combines storytelling and Spatio-logical Inference (SLI) to generate rules of interaction among entities and measure how well they forecast a real-world event. The proposed algorithm first takes as input the probability of prior occurrences of events along with their spatial distances. It calculates their soft truths, i.e., the belief they have indeed been observed with certainty. Subsequently, the algorithm applies a relaxed form of logical conjunction and disjunction to compute a distance to satisfaction for each rule. The rules of lowest distances represent the best forecasts. Extensive experiments with social unrest in Afghanistan show that storytelling and SLI can outperform common probabilistic approaches by as much as 30% in terms of precision and 13% in terms of recall.