Dan Chia-Tien Lo

Automated Detection and Analysis for Android Ransomware

Along with the rapid growth of new science and technology, the functions of smartphones become more and more powerful. Nevertheless, everything has two aspects. Smartphones bring so much convenience for people and also bring the security risks at the same time. Malicious application has become a big threat to the mobile security. Thus, an efficiency security analysis and detection method is important and necessary. Due to attacking of malicious application, user could not use smartphone normally and personal information could be stolen. What is worse, attacking proliferation will impact the healthy growth of the mobile Internet industry. To limit the growing speed of malicious application, the first thing we need to know what malicious application is and how to deal with. Detecting and analyzing their behaviors helps us deeply understand the attacking principle such that we can take effective countermeasures against malicious application. This article describes the basic Android component and manifest, the reason that Android is prevalent and why attacking came in. This paper analyzed and penetrated malicious ransom ware which threats mobile security now with our developed automated analysis approach for such mobile malware detection.

Integrating Internet of Things (IoT) into STEM undergraduate education: Case study of a modern technology infused courseware for embedded system course

Internet of Things (IoT) is rapidly emerging as the next generation of communication infrastructure, where myriad of multi-scale sensors and devices are seamlessly blended for ubiquitous computing and communication. The rapid growth of IoT applications has increased the demand for experienced professionals in the area. Since few, if any, dedicated IoT courses are currently offered, most Science, Technology, Engineering, and Mathematics (STEM) students will have limited or no exposure to IoT development until after graduation and entrance into the workforce. Moreover, there is a little room for adding additional courses into existing STEM curriculum. Therefore, we propose to transform STEM core courses by integrating IoT-based learning framework into their corresponding lab projects. The design challenges of the new learning framework is summarized in the paper. Subsequently, we propose the effective learning approaches to address those challenges. Moreover, in this paper, we present a case study by incorporating IoT-based learning framework into a Software Engineering (SWE) embedded system analysis & design course. Specifically, we introduce a lab development kit composed of Raspberry Pi/Arduino boards and a set of sensors with Zigbee supporting to provide wireless communication in the class lab section. We adopt module design method to design the course labware. Well-developed modules are presented and one sample module is illustrated in the paper. The labware is evaluated through survey questions. The majority of the students provided positive feedback and enjoyed the IoT-based lab development kit.

A Low Cost, Portable Platform for Information Assurance and Security Education

This paper presents a new inexpensive portable learning platform for information assurance and security (IAS) education. Unique features such as affordable settings, portability, and isolated network are discussed. This project is developed based on open sourced or free software to minimize the implementation cost. A dedicated repository that hosts our findings and deliverables is freely available online. This portable learning platform makes it possible to offer hands-on lab intensive curriculum online and to enhance research productivity on IAS.

A Neural-Network Based DDoS Detection System Using Hadoop and HBase

This paper presents a detection system for the Distributed Denial of Service (DDoS) attack based on neural network, which is implemented in the Apache Hadoop cluster and the HBase system. While there are already many approaches for the DDoS detection, there are two main challenges: the learning capability of a DDoS detection system and the ability to process a huge unstructured dataset. The main contribution of this paper is to develop a DDoS detection system with learning capability to adapt to new types of DDoS attacks and ability to store and analyze a huge unstructured dataset collected from network logs. Particularly, a neural network architecture is designed for the DDoS detection system, and a list of training samples is developed to train the neural network. This approach is validated with a series of generated datasets of different scenarios. It was shown that the system with the well-trained neural network is able to detect DDoS attacks efficiently and successfully.

Internet of Things-Based Temperature Tracking System

The work described in this paper consists of a temperature tracking system that follows a Client-Server architecture. A Raspberry Pi, a System-on-a-Chip (SoC) device, is responsible for sensing the temperature and streaming it to a server, the readings then are displayed in a mobile android application. For this system, a python application was developed to sense and stream the temperature, a servlet was developed to read and store the temperature in a SQLite database, and a mobile Android application was developed to read and display the temperature readings from the server. The initial versions of the project used the SoC device as a server (storing temperature readings into a local SQLite database), and both the SoC device and the mobile device needed to be connected in a local area network. However, the project was further developed to separate the server responsibilities from the SoC device. The system now supports user authentication, and both devices are connected through the Internet. This implementation allows the temperature readings to be viewed and displayed anytime from anywhere in the world since the database is hosted on a server which can be accessed over the internet. Also, this solution allows multiple SoC devices to stream temperatures to the server, to different mobile clients using the same database. The Android client application was also implemented to graphically show the temperature readings recorded by Raspberry Pi using Restful architecture. Moreover, an alert message notification was implemented in Android application so that a user is notified whenever the temperature reading reaches the preset threshold. On the other hand, the smart chair system has brilliant commercial prospects, which can be helpful to build health care products with the help of wearable sensors, intelligent refrigerator/oven temperature tracking system and etc.

Work in progress: Enhance CS/CE student learning in computer architecture and organization through a remote instrument control lab with mixed reality

The paper presents our preliminary work on developing a remote hardware development lab using mixed reality technology (mRLab). The mRLab allows a group of students to remotely and simultaneously work on a VHDL project with an FPGA development board. All lab objects are created in 3D and an interactive layer on the 3D objects is added to host learning information along with caveats to assist learning.

Static Mining and Dynamic Taint for Mobile Security Threats Analysis

In the last decade, the computing landscape has been rapidly shifting to mobile platform. More and more individuals and businesses are using smartphones and tablet pcs as their main general purpose computing devices. As such, mobile platform becomes prominent targets of cybercriminals. Its imperative to develop techniques that can detect mobile malware and protect the mobile devices from cyber-attacks. In this paper, we propose a hybrid approach combing a static mining algorithm and a dynamic taint analysis for effective mobile malware analysis. A preliminary prototype was built and tested on real-world mobile apps.

Spam filtering using Association Rules and Naïve Bayes Classifier

E-mail service is one of the most popular Internet communication services. Thousands of companies, organizations and individuals use e-mail every day and get benefit from it. However, an amount of spam emails always hang around us and bring down our productivity. We urgently need a spam filtering to clean up our network environment. A spam filtering using Association Rule and Naïve Bayes Classifier is recommended here. Instead of focusing on increasing spam precision rate, we try to preserve all non-spam emails as the first priority. In the real world applications and services, thats what we should do. In this paper, we also provide the comparison between using both Association Rule and Naïve Bayes Classifier algorithms and just using Naïve Bayes Classifier.

Learning Hands-on Information Assurance and Security on Mobile Devices

The popularity and the large market share of mobile devices such as smart phones and tablets have had a significant impact on our daily lives. The full-fledged computing platforms render a ubiquitous means to deliver information and knowledge. This project aims to utilize such platforms to enhance computer science education, especially in curricula such as programming, mobile application development and information assurance and security. Meanwhile, there is an urgent need to produce more qualified computer professionals in mobile application development and information assurance and security to meet the workforce shortage. In this paper, we describe the design and implementation of our hands-on pedagogical model for learning information assurance and security based on mobile devices. Not only is this model feasible, but also it is affordable. The lab ware consists of hands-on real-world relevant self-contained learning modules, and is designed to be ready for wide scale implementation.