Applying Data Mining Algorithms on Open Source Intelligence to Combat Cyber Crime

In this dissertation, we investigate the applications of data mining algorithms on online criminal information. Ever since the entry of the information era, the development of the world wide web makes the convenience of peoples\u27 lives to the next level. However, at the same time, the web is utilized by criminals for illegal activities like drug smuggling and online fraudulence. Cryptomarkets and instant message software are the most popular two online platforms for criminal activities. Here, we try to extract useful information from related open source intelligence in these two platforms with data mining algorithms. Cryptomarkets (or darknet markets) are commercial hidden-service websites that operate on The Onion Router (Tor) anonymity network, which have grown rapidly in recent years. In this dissertation, we discover interesting characteristics of Bitcoin transaction patterns in cryptomarkets. We present a method to identify vendors\u27 Bitcoin addresses by matching vendors\u27 feedback reviews with Bitcoin transactions in the public ledger. We further propose a cost-effective algorithm to accelerate both steps effectively. Comprehensive experimental results have demonstrated the effectiveness and efficiency of the proposed method. Instant message(IM) software is another base for these criminal activities. Users of IM applications can easily hide their identities while interacting with strangers online. In this dissertation, we propose an effective model to discover hidden networks of influence between members in a group chat. By transferring the whole chat history to sequential events, we can model message sequences to a multi-dimensional Hawkes process and learn the Granger Causality between different individuals. We learn the influence graph by applying an expectation–maximization(EM) algorithm on our text biased multi-dimensional Hawkes Process. Users in IM software normally maintain multiple accounts. We propose a model to cluster the accounts that belong to the same user

Realization and design of a pilot assist decision-making system based on speech recognition

A system based on speech recognition is proposed for pilot assist decision-making. It is based on a HIL aircraft simulation platform and uses the microcontroller SPCE061A as the central processor to achieve better reliability and higher cost-effect performance. Technologies of LPCC (linear predictive cepstral coding) and DTW (Dynamic Time Warping) are applied for isolated-word speech recognition to gain a smaller amount of calculation and a better real-time performance. Besides, we adopt the PWM (Pulse Width Modulation) regulation technology to effectively regulate each control surface by speech, and thus to assist the pilot to make decisions. By trial and error, it is proved that we have a satisfactory accuracy rate of speech recognition and control effect. More importantly, our paper provides a creative idea for intelligent human-computer interaction and applications of speech recognition in the field of aviation control. Our system is also very easy to be extended and applied.Comment: 10 pages, 8 figure

Characteristics of Bitcoin Transactions on Cryptomarkets

Cryptomarkets (or darknet markets) are commercial hidden-service websites that operate on The Onion Router (Tor) anonymity network. Cryptomarkets accept primarily bitcoin as payment since bitcoin is pseudonymous. Understanding bitcoin transaction patterns in cryptomarkets is important for analyzing vulnerabilities of privacy protection models in cryptocurrecies. It is also important for law enforcement to track illicit online crime activities in cryptomarkets. In this paper, we discover interesting characteristics of bitcoin transaction patterns in cryptomarkets. The results demonstrate that the privacy protection mechanism in cryptomarkets and bitcoin is vulnerable. Adversaries can easily gain valuable information for analyzing trading activities in cryptomarkets

iOS indoor positioning study on iPhone platform

This dissertation aims to assist readers have a better understanding of the steps and procedures of the indoor positioning system. Beacon is a cutting-edge technology device announced by Apple and it is often used as tags in this project for indoor localization. It can detect IOS devices within a specified range. Bluetooth low-energy devices can advertise iBeacon information. When IOS devices are close to the BLE devices, those IOS devices can detect the signals. This dissertation discussed two popular algorithms for the IOS platform. (i) trilateration method and (ii) fingerprint-based method. The challenge of the project was to improve the accuracy. A series of tests were conducted to compare the two algorithms on the iPhone on some simple paths. It was found the fingerprint method has better accuracy. Furthermore, we designed the web server and created the database to collect the reference points RSS value and connected the application to the web server. The localization function is realized by matching the reference RSS value with received RSS value. The application can detect the iBeacon signals and will match them with data in the cloud server. The map can show the position of the users.Master of Science (Communications Engineering

Numerical Simulation and Analysis of Fish-Like Robots Swarm

Artificial fish-like robot is an important branch of underwater robot research. At present, most of fish-like robot research focuses on single robot mechanism behavior, some research pays attention to the influence of the hydro-environment on robot crowds but does not reach a unified conclusion on the efficiency of fish-like robots swarm. In this work, the fish-like robots swarm is studied by numerical simulation. Four different formations, including the tandem, the phalanx, the diamond, and the rectangle are conducted by changing the spacing between fishes. The results show that at close spacing, the fish in the back can obtain a large wake from the front fish, but suffers large lateral power loss from the lateral fish. On the contrary, when the spacing is large, both the wake and pressure caused by the front and side fishes become small. In terms of the average swimming efficiency of fish swarms, we find that when the fish spacing is less than 1.25 L (L is the length of the fish body), the tandem swarm is the best choice. When the spacing is 1.25 L , the tandem, diamond and rectangle swarms have similar efficiency. When the spacing is larger than 1.25 L , the rectangle swarm is more efficient than other formations. The findings will provide significant guidance for the control of fish-like robots swarm

Receptivity to malaria in the China–Myanmar border in Yingjiang County, Yunnan Province, China

Abstract Background The re-establishment of malaria has become an important public health issue in and out of China, and receptivity to this disease is key to its re-emergence. Yingjiang is one of the few counties with locally acquired malaria cases in the China–Myanmar border in China. This study aimed to understand receptivity to malaria in Yingjiang County, China, from June to October 2016. Methods Light-traps were employed to capture the mosquitoes in 17 villages in eight towns which were categorized into four elevation levels: level 1, 0–599 m; level 2, 600–1199 m; level 3, 1200–1799 m; and level 4, > 1800 m. Species richness, diversity, dominance and evenness were used to picture the community structure. Similarity in species composition was compared between different elevation levels. Data of seasonal abundance of mosquitoes, human biting rate, density of light-trap-captured adult mosquitoes and larvae, parous rate, and height distribution (density) of Anopheles minimus and Anopheles sinensis were collected in two towns (Na Bang and Ping Yuan) each month from June to October, 2016. Results Over the study period, 10,053 Anopheles mosquitoes were collected from the eight towns, and 15 Anopheles species were identified, the most-common of which were An. sinensis (75.4%), Anopheles kunmingensis (15.6%), and An. minimus (3.5%). Anopheles minimus was the major malaria vector in low-elevation areas (< 600 m, i.e., Na Bang town), and An. sinensis in medium-elevation areas (600–1200 m, i.e., Ping Yuan town). In Na Bang, the peak human-biting rate of An. minimus at the inner and outer sites of the village occurred in June and August 2016, with 5/bait/night and 15/bait/night, respectively. In Ping Yuan, the peak human-biting rate of An. sinensis was in August, with 9/bait/night at the inner site and 21/bait/night at the outer site. The two towns exhibited seasonal abundance with high density of the two adult vectors: The peak density of An. minimus was in June and that of An. sinensis was in August. Meanwhile, the peak larval density of An. minimus was in July, but that of An. sinensis decreased during the investigation season; the slightly acidic water suited the growth of these vectors. The parous rates of An. sinensis and An. minimus were 90.46 and 93.33%, respectively. Conclusions The Anopheles community was spread across different elevation levels. Its structure was complex and stable during the entire epidemic season in low-elevation areas at the border. The high human-biting rates, adult and larval densities, and parous rates of the two Anopheles vectors reveal an exceedingly high receptivity to malaria in the China–Myanmar border in Yingjiang County

Python Scrapers for Scraping Cryptomarkets on Tor

Cryptomarkets are commercial websites on the web that operate via darknet, a portion of the Internet that limits the ability to trace users’ identity. Cryptomarkets have facilitated illicit product trading and transformed the methods used for illicit product transactions. The survellience and understanding of cryptomarkets is critical for law enforcement and public health. In this paper, we design and implement Python scrapers for scraping cryptomarkets. The design of the scraper system is described with details and the source code of the scrapers is shared with the public