An Assessment on Credit Card Fraud Detection: Survey

Credit card fraud is a costly problem for many financial institutions, costing businesses billions of dollars a year. Many adversaries still escape fraud detection systems because these systems often do not include information about the adversary's knowledge of the fraud detection mechanism. This thesis aims to include information on the motivations of "crooks" and the knowledge base in an adaptive fraud detection system. In this thesis, we use a theoretical adversarial learning approach to classification to model the best fraudster strategy. We proactively adapt the fraud detection system to classify these future fraudulent transactions better. Therefore, this document aims to provide an over-supervised bird's-eye approach with a suitable feature extraction technique that improves fraud detection rather than mistakenly classifying an actual transaction as fraud

Quantifying Bat Detection Survey Methods and Activity Patterns

Bats have an astonishing diversity and provide vital ecosystem services in an array of different niches. In North America, most species of bats are insectivores and tend to be frequently overlooked for their important ecosystem role providing insect control. As bat populations have declined in recent years, farmers, land managers, conservationists, and bat enthusiasts have wondered what we can do to protect our local bat populations. As a first step, we need to develop methods that more effectively survey for rare species of bats. By performing inefficient surveys, we are doing a disservice to our funding agencies providing misinformation that ultimately puts populations at risk. Our results reveal the low detection probability associated with mist netting of relatively common bats, the big brown (Eptesicus fuscus) and little brown bat (Myotis lucifugus), compared to the detection probability using full spectrum recorders. These results suggest that acoustic recorders may provide the most robust information and that mist netting alone for presence-absence of species may require additional nights of sampling for accurate results. We can also manage for bat populations through a better understanding of how they select habitat. In this study we used full spectrum acoustic detectors to sample major land cover types and analyze bat activity patterns at local and landscape scales. Our results indicate that bats in McHenry County most likely use a hierarchical approach to habitat selection and prefer forested riparian areas with large trees that also have numerous small patches of agriculture within a 1 km radius. This information can help us better manage forests for Midwestern bat populations as they hopefully recover from recent population declines

Quantifying Bat Detection Survey Methods and Activity Patterns

Bats have an astonishing diversity and provide vital ecosystem services in an array of different niches. In North America, most species of bats are insectivores and tend to be frequently overlooked for their important ecosystem role providing insect control. As bat populations have declined in recent years, farmers, land managers, conservationists, and bat enthusiasts have wondered what we can do to protect our local bat populations. As a first step, we need to develop methods that more effectively survey for rare species of bats. By performing inefficient surveys, we are doing a disservice to our funding agencies providing misinformation that ultimately puts populations at risk. Our results reveal the low detection probability associated with mist netting of relatively common bats, the big brown (Eptesicus fuscus) and little brown bat (Myotis lucifugus), compared to the detection probability using full spectrum recorders. These results suggest that acoustic recorders may provide the most robust information and that mist netting alone for presence-absence of species may require additional nights of sampling for accurate results. We can also manage for bat populations through a better understanding of how they select habitat. In this study we used full spectrum acoustic detectors to sample major land cover types and analyze bat activity patterns at local and landscape scales. Our results indicate that bats in McHenry County most likely use a hierarchical approach to habitat selection and prefer forested riparian areas with large trees that also have numerous small patches of agriculture within a 1 km radius. This information can help us better manage forests for Midwestern bat populations as they hopefully recover from recent population declines

Sampling strategies for forest aerial detection survey in Colorado

2015 Summer.Aerial detection survey (ADS) has been commonly employed in forest surveys in the United States for detecting forest damage and monitoring forest health. In Colorado, ADS by USDA Forest Service has conducted annual 100% census of government forested land for more than 20 years with the goal of achieving information about forest damage due to different causal agents and disorders. Sketchmapping has been commonly employed in ADS with the goal of detecting and documenting on maps mortality, defoliation and other visible forest change from aircraft. At medium and large scale, sketchmapping is a suitable technique for forest monitoring that provides valuable information in forest health. This dissertation deals with data of forest area damaged by five causal agents mountain pine beetle, spruce beetle, western spruce budworm, pin engraver, and Douglas fir beetle and two disorders subalpine fir mortality and sudden aspen decline. The combined areas damaged by all causes were also considered. Data were downloaded from ADS in Colorado from 1994 to 2013 as polygon shapefiles with associated information such as causal agents or disorders, area damaged, and type of forest. The goal of my dissertation was to identify an appropriate sampling strategies to archive good estimates of total area damaged, to decrease survey cost, and to increase safety by reducing the amount of flights. To approach this goal, four sample designs for estimating total area damaged caused by various causal agent were evaluated: simple random sampling, stratified random sampling, probability proportional to size, and non-alignment systematic sampling. A GIS layer of 150 transects covering Colorado’s forestlands was developed and represented the sample unit for my study. Each transect was 3.2 km wide and 625 km long and was numbered from 1 to 150 from south to north. Each sample design was evaluated using eight sample sizes (10, 15, 20, 25,30, 35, 50, and 70) and applied to the seven damages and the combined damaged area. The statistical properties were evaluated to determine the optimal sample design for estimating area damaged caused by different causal agents. The spatio-temporal characteristics of area damaged that influence precision and accuracy of estimate were considered. Most of the damaged forest areas by single causal agents and disorders showed aggregated spatial patterns; whereas the combined damaged areas were uniformly distributed across the landscape. A loss plus cost function was employed to determine the optimal sample size for each sample design and analyzed for the cost advantage of alternative sample designs. We found that stratified random sampling was the most optimal sample design by producing the highest percentage of unbiased estimates of total area damaged and the smallest variances. The next best sampling designs were simple random sampling and probability proportional to size. The non-alignment systematic sampling was the worst for estimating total area damaged both for individual causal agents and disorders and all causal agents combined. The optimal sample size varied by sample design and causal agents and disorders as well as the level of confidence. Optimal sample size increased with increasing variability in the population and as the desired level of confidence increased. Larger samples were required to simultaneously provide estimates for multiple causal agents and disorder with reasonable levels of precision when compared to a single causal agent. Stratified random sampling was the most cost effective when compared with other sample designs. For example, the cost advantage of stratified sampling over random sampling for estimating the damage from subalpine-fir mortality was $85,000 per year. In contrast, PPS sampling had a cost disadvantage of -$13,000 per year when compared with simple random sampling and -$95,000 per year when compared with stratified sampling for estimating the total damage from all causal agents combined at the 0.95 level of confidence

Towards Large-Scale Small Object Detection: Survey and Benchmarks

With the rise of deep convolutional neural networks, object detection has achieved prominent advances in past years. However, such prosperity could not camouflage the unsatisfactory situation of Small Object Detection (SOD), one of the notoriously challenging tasks in computer vision, owing to the poor visual appearance and noisy representation caused by the intrinsic structure of small targets. In addition, large-scale dataset for benchmarking small object detection methods remains a bottleneck. In this paper, we first conduct a thorough review of small object detection. Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively. SODA-D includes 24828 high-quality traffic images and 278433 instances of nine categories. For SODA-A, we harvest 2513 high resolution aerial images and annotate 872069 instances over nine classes. The proposed datasets, as we know, are the first-ever attempt to large-scale benchmarks with a vast collection of exhaustively annotated instances tailored for multi-category SOD. Finally, we evaluate the performance of mainstream methods on SODA. We expect the released benchmarks could facilitate the development of SOD and spawn more breakthroughs in this field. Datasets and codes are available at: \url{https://shaunyuan22.github.io/SODA}

BOTNET DETECTION SURVEY

Di antara berbagai bentuk malware, Botnet merupakan salah satu ancaman yang paling serius terhadap cyber-crime saat ini. Hal ini disebabkan karena Botnet mampu menyediakan platform yang dapat didistribusikan pada kegiatan ilegal seperti serangan-serangan di internet, termasuk spam, phishing, clickfraud, pencurian password dan Distributed Denial of service(DDoS) attack.Akhir-akhir ini, deteksi Botnet telah menarik perhatian para peneliti untuk dijadikan topik penelitian dalam usaha pencegahan terhadap cyber-crime. Dalam paper ini, penulis melakukan studi literature untuk mengkaji beberapa penelitian sebelumnya yang membahas tentang teknik-teknik yang digunakan untuk mendeteksi keberadaan Botnet didalam suatu sistem. Beberapa teknik yang dibahas dalam paper ini yaitu signature-based, anomaly-based DNS-based, dan mining-base. Kajian komprehensif ini diharapkan dapat memberikan gambaran yang lebih jelas tentang teknik-teknik mendeteksi Botnet dengan memaparkan kelebihan dan kekurangan dari masing-masing metode tersebut yang selanjutnya dapat digunakan sebagai langkah awal dalam usaha prefentif terhadap serangan Botnet.Kata kunci : Botnet, deteksi Botnet, cyber-crim

The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future

The Arecibo Dual-Beam Survey: Arecibo and VLA Observations

The Arecibo Dual-Beam Survey is a "blind" 21 cm search for galaxies covering \~430 deg^2 of sky. We present the data from the detection survey as well as from the follow-up observations to confirm detections and improve positions and flux measurements. We find 265 galaxies, many of which are extremely low surface brightness. Some of these previously uncataloged galaxies lie within the zone of avoidance where they are obscured by the gas and dust in our Galaxy. 81 of these sources are not previously cataloged optically and there are 11 galaxies that have no associated optical counterpart or are only tentatively associated with faint wisps of nebulosity on the Digitized Sky Survey images. We discuss the properties of the survey and in particular we make direct determinations of the completeness and reliability of the sample. The behavior of the completeness and its dependencies is essential for determining the HI mass function. We leave the discussion of the mass function for a later paper, but do note that we find many low surface brightness galaxies and 7 sources with M_HI < 10^8 Msolar.Comment: 23 pages, 20 figures, accepted ApJS. For tables 2 and 3 only the first page has been included. ASCII tables are provided separatel