RAIDER: Reinforcement-aided Spear Phishing Detector

Spear Phishing is a harmful cyber-attack facing business and individuals worldwide. Considerable research has been conducted recently into the use of Machine Learning (ML) techniques to detect spear-phishing emails. ML-based solutions may suffer from zero-day attacks; unseen attacks unaccounted for in the training data. As new attacks emerge, classifiers trained on older data are unable to detect these new varieties of attacks resulting in increasingly inaccurate predictions. Spear Phishing detection also faces scalability challenges due to the growth of the required features which is proportional to the number of the senders within a receiver mailbox. This differs from traditional phishing attacks which typically perform only a binary classification between phishing and benign emails. Therefore, we devise a possible solution to these problems, named RAIDER: Reinforcement AIded Spear Phishing DEtectoR. A reinforcement-learning based feature evaluation system that can automatically find the optimum features for detecting different types of attacks. By leveraging a reward and penalty system, RAIDER allows for autonomous features selection. RAIDER also keeps the number of features to a minimum by selecting only the significant features to represent phishing emails and detect spear-phishing attacks. After extensive evaluation of RAIDER over 11,000 emails and across 3 attack scenarios, our results suggest that using reinforcement learning to automatically identify the significant features could reduce the dimensions of the required features by 55% in comparison to existing ML-based systems. It also improves the accuracy of detecting spoofing attacks by 4% from 90% to 94%. In addition, RAIDER demonstrates reasonable detection accuracy even against a sophisticated attack named Known Sender in which spear-phishing emails greatly resemble those of the impersonated sender.Comment: 16 page

Pokemon GO in Melbourne CBD: A case study of the cyber-physical symbiotic social networks

[EN] The recent popular game, Pokemon GO, created two symbiotic social networks by location-based mobile augmented reality (LMAR) technique. One is in the physical world among players, and another one is in the cyber world among players' avatars. To date, there is no study that has explored the formation of each social network and their symbiosis. In this paper, we carried out a data-driven research on the Pokemon GO game to solve this problem. We accordingly organised the collection of two real datasets. For the first dataset, we designed a questionnaire to collect players' individual behaviours in Pokemon GO, and used maps of Melbourne (Australia) to track and record their usual playing areas. Based on the data that we collected, we modelled the formation of the symbiotic social networks in both physical world (i.e. for players) and cyber world (i.e. for avatars) as well as interactions between players and Pokemon GO elements (i.e. 'bridges' of the two worlds). By investigating the mechanism of network formation, we revealed the relatively weak correlation between the formation processes of the two networks. We further incorporated the real-world pedestrian dataset collected by sensors across Melbourne CBD into the study of their symbiosis. Based on the second dataset, we examined the changes of people's social behaviours in terms of most visited places. The results suggested that the existence of the cyber social network has reciprocally changed the structure of the symbiotic physical social network. (C) 2017 Elsevier B.V. All rights reserved.This research is partially supported by the Australian Research Council projects DP150103732, DP140103649, and LP140100816. The authors extend their appreciation to the International Scientific Partnership Program (ISPP) at King Saud University, Riyadh, Saudi Arabia for funding this work through the project No. ISPP#0069.Wang, D.; Wu, T.; Wen, S.; Liu, D.; Xiang, Y.; Zhou, W.; Hassan Mohamed, H.... (2018). Pokemon GO in Melbourne CBD: A case study of the cyber-physical symbiotic social networks. Journal of Computational Science. 26:456-467. https://doi.org/10.1016/j.jocs.2017.06.009S4564672

Genetic diversity in Spartina patens in remnant patches in the New Jersey Meadowlands

Habitat fragmentation is a factor that influences virtually all plant and animal communities. For plants, it typically reduces the size, and increases spatial isolation of populations and causes a decrease in genetic variation. Spartina patens, a clonal and salt-tolerant grass, is commonly used in local high marsh ecosystem restoration. When an ecosystem is restored, as is the case for many urban salt marshes, the genetic profiles of plants propagules employed are often ignored. This investigation was conducted not only to identify the genetic profiles of S. patens in the Hackensack Meadowlands New Jersey for restoration, but also to understand the influence of patch size in distribution of genetic diversity of populations. To address these questions, ISSR (inter-simple sequence repeats) analysis was utilized to establish molecular genotype signatures for clones of S. patens that have been used for restoration. Approximately 83% of polymorphic bands were obtained using fourteen primer combinations. The number of polymorphic bands among the six populations/ patches ranged from 15 to 52. Shannon’s index also indicated that larger patches had higher genetic variation than small patches had. Analysis of the resulting patterns suggests that the Hawk Property Large, River Bend Small, and Fish Creek Large populations are genetically closely related. The River Bend Large population is related to Fish Small, and Hawk Property Small. This indicates that geographical distance is not related to genetic variation among populations. Based on the analysis of molecular variance (AMOVA), 58% of the genetic variation accounts for the differences among populations. It indicates that the divergence among populations is higher than the variation within population. In order to enhance the conservation of habitat of S. patens, the samples should be collected all over the mash. In this way, it may ensure the survival of the species when they were transplanted to a new habitat due to more genetic variation.M.S.Includes bibliographical referencesIncludes vitaby TingMin W

Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid

Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly.Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists.Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer’s primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM