Deokjai Choi

Application of Data Mining to Network Intrusion Detection: Classifier Selection Model

As network attacks have increased in number and severity over the past few years, intrusion detection system (IDS) is increasingly becoming a critical component to secure the network. Due to large volumes of security audit data as well as complex and dynamic properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem that is receiving more and more attention from the research community. The uncertainty to explore if certain algorithms perform better for certain attack classes constitutes the motivation for the reported herein. In this paper, we evaluate performance of a comprehensive set of classifier algorithms using KDD99 dataset. Based on evaluation results, best algorithms for each attack category is chosen and two classifier algorithm selection models are proposed. The simulation result comparison indicates that noticeable performance improvement and real-time intrusion detection can be achieved as we apply the proposed models to detect different kinds of network attacks.

Gait identification using accelerometer on mobile phone

In this paper, we present two approaches for identification based on biometric gait using acceleration sensor - called accelerometer. In contrast to preceding works, acceleration data are acquired from built-in sensor in mobile phone placed at the trouser pocket position. Data are then analyzed in both time domain and frequency domain. In time domain, gait templates are extracted and Dynamic Time Warping (DTW) is used to evaluate the similarity score. On the other hand, extracted features in frequency domain are classified using Support Vector Machine (SVM). With the participation of total 11 volunteers over 24 years old in our experiment, we achieved the accuracy of both methods respectively 79.1% and 92.7%.

An efficient recovery mechanism for MPLS-based protection LSP

Network reliability and survivability have been very important issues to provide for application services that may require a real-time service or high priority QoS (quality of service) in the Internet. IETF has proposed largely two recovery mechanisms for MPLS-based protection label switching path (LSP), which are protection switching and rerouting models. However, from the viewpoint of TE (traffic engineering), IETFs recovery mechanisms have not considered the optimal backup path for the recovery of an LSP in the occurrence of a network failure. This paper suggests an efficient pre-qualified recovery mechanism, which optimizes the network performance by considering link usage. Since an existing recovery mechanism, pre-qualified rerouting, selects a backup path only once at the LSP setup time, it may not reflect the exact status of network resources at the time of a fault. In contrast, our approach exchanges network status information among LSRs so that the backup path selection engine can use up-to-date information and decide an optimal backup path for a possible failure. The performance of the proposed recovery method has been demonstrated by simulation using MNS (MPLS network simulator). The new proposed recovery mechanism can always maintain an optimized network state regardless of the fault occurrence.

Fall Detection Based on Movement and Smart Phone Technology

Nowadays, recognizing human activities is an important subject; it is exploited widely and applied to many fields in real-life, especially health care or context aware application. Research achievements are mainly focused on activities of daily living which are useful for suggesting advises to health care applications. Falling event is one of the biggest risks to the health and well being of the elderly especially in independent living because falling accidents may be caused from heart attack. Recognizing this activity still remains in difficult research area. Many systems which equip wearable sensors have been proposed but they are not useful if users forget to wear the clothes or lack ability to adapt themselves to mobile systems without specific wearable sensors. In this paper, we develop novel method based on analyzing the change of acceleration, orientation when the fall occurs. In this study, we recruit five volunteers in our experiment including various fall categories. The results are effective for recognizing fall activity. Our system is implemented on Google Android smart phone which already plugged accelerometer and orientation sensors. The popular phone is used to get data from accelerometer and results show the feasibility of our method and contribute significantly to fall detection in Health care.

Independent and Personal SMS Spam Filtering

The amount of Short Message Service (SMS) spam is increasing. SMS spam should be put into the spam folder, not the inbox. Some solutions have been proposed that for the most part use Text Classification techniques. However, they need another computer system to create the filtering system using a large amount of SMS data in advance and install the filtering system into the mobile phone to filter incoming SMS. This kind of solution reduces independence because the user has to store received SMS into computer to train or update the filtering system or data and user can get the filtering system. Storing SMS, which may consist of private data especially SMS ham, leads to privacy problem. Obviously, it also increases hardware cost and increases communication cost between mobile phone and computer system. Thus, we propose an independent filtering system that does not need computer system support. The training, filtering and updating processes were done on mobile phone. Our proposed approach filters SMS spam on an independent mobile phone while obtaining reasonable accuracy, minimum storage consumption and acceptable processing time.

Adaptive Cross-Device Gait Recognition Using a Mobile Accelerometer

Mobile authentication/identification has grown into a priority issue nowadays because of its existing outdated mechanisms, such as PINs or passwords. In this paper, we introduce gait recognition by using a mobile accelerometer as not only effective but also as an implicit identification model. Unlike previous works, the gait recognition only performs well with a particular mobile specification (e.g., a fixed sampling rate). Our work focuses on constructing a unique adaptive mechanism that could be independently deployed with the specification of mobile devices. To do this, the impact of the sampling rate on the preprocessing steps, such as noise elimination, data segmentation, and feature extraction, is examined in depth. Moreover, the degrees of agreement between the gait features that were extracted from two different mobiles, including both the Average Error Rate (AER) and Intra-class Correlation Coefficients (ICC), are assessed to evaluate the possibility of constructing a device-independent mechanism. We achieved the classification accuracy approximately for both devices, which showed that it is feasible and reliable to construct adaptive cross-device gait recognition on a mobile phone.

Network traffic anomalies detection and identification with flow monitoring

Network management and security is currently one of the most vibrant research areas, among which, research on detecting and identifying anomalies has attracted a lot of interest. Researchers are still struggling to find an effective and lightweight method for anomaly detection purpose. In this paper, we propose a simple, robust method that detects network anomalous traffic data based on flow monitoring. Our method works based on monitoring the four predefined metrics that capture the flow statistics of the network. In order to prove the power of the new method, we did build an application that detects network anomalies using our method. And the result of the experiments proves that by using the four simple metrics from the flow data, we do not only effectively detect but can also identify the network traffic anomalies.

Implicit authentication based on ear shape biometrics using smartphone camera during a call

From secret knowledge like password up to physical traits as biometrics, current smartphone authentication systems are deemed inconvenience and difficult for users. Burdens on remembering password as well as privacy issues on stolen or forged biometrics have raised a futuristic idea of authentication systems. New system is hoped being transparent and with very minimum user involvement denoted as implicit authentication system. One of the ways to implicitly authenticate users is by authenticating them via image or video captured using smartphone camera during a call. During call interaction, we implicitly take ear image using front smartphone camera to recognize and authenticate users without them realizing. In this paper, we present a novel approach to ear recognition which considers both shape and texture information to represent ear image. Firstly, all Local Binary Pattern (LBP) are combined after extracted and concatenated into a single histogram. Second, in order to get geometric features, we use the idea of ear location center that is easily adjusted by smartphone user. Then, we combine previous steps to represent ear image as a descriptor. The recognition is performed using a nearest neighbor classifier computed feature space with Euclidean distance as a similarity/dissimilarity measure. Our proposed approach is very easy and simple thereby its simplicity allows very fast feature extraction. We foresee that this experiment is applicable directly on smartphone.

Personalization in Mobile Activity Recognition System Using K-Medoids Clustering Algorithm

Nowadays mobile activity recognition (AR) has been creating great potentials in many applications including mobile healthcare and context-aware systems. Human activities could be detected based on sensory data that are available on today’s smart phone. In this study, we consider mobile phones as an independent device since sending the data to central server can generate privacy issues. Furthermore, applying AR on mobile phone does not only require an effective accuracy rate but also the lowest power consumption. Normally, an AR model learnt from acceleration data of a specific person is distributed to other people to recognize the same activities instead of generating different models individually. This work often cannot create accurate results on the prediction in broad range of participants. Moreover, such AR model also has to allow each user to update his new activities independently. Therefore, we propose an algorithm that integrates Support Vector Machine classifier and -medoids clustering method to resolve completely the demand.

A Novel Reference Node Selection Algorithm Based on Trilateration for Indoor Sensor Networks

The key problem of location service in indoor sensor networks is to quickly and precisely acquire the position information of mobile nodes. Traditional positioning algorithms, such as two-phase positioning (TPP) algorithm, are too complicated to be used in a mobile node and they cannot satisfy the time constraints. We analyze the localization error and draw the conclusion that the localization error is the least when three reference nodes form an equilateral triangle. We also proof three basic positioning theorem of reference node. Therefore, we propose a novel reference node selection algorithm based on trilateration (RNST), which can effective meet realtime localization requirement of the mobile nodes in indoor environment, and guarantee less localization error.