Blockchain leveraged task migration in body area sensor networks
- Authors: Uddin, Ashraf , Stranieri, Andrew , Gondal, Iqbal , Balasubramanian, Venki
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 25th Asia-Pacific Conference on Communications, APCC 2019 p. 177-184
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- Description: Blockchain technologies emerging for healthcare support secure health data sharing with greater interoperability among different heterogeneous systems. However, the collection and storage of data generated from Body Area Sensor Net-works(BASN) for migration to high processing power computing services requires an efficient BASN architecture. We present a decentralized BASN architecture that involves devices at three levels; 1) Body Area Sensor Network-medical sensors typically on or in patient's body transmitting data to a Smartphone, 2) Fog/Edge, and 3) Cloud. We propose that a Patient Agent(PA) replicated on the Smartphone, Fog and Cloud servers processes medical data and execute a task offloading algorithm by leveraging a Blockchain. Performance analysis is conducted to demonstrate the feasibility of the proposed Blockchain leveraged, distributed Patient Agent controlled BASN. © 2019 IEEE.
- Description: E1
Can a robot hear the shape and dimensions of a room?
- Authors: Nguyen, Linh , Miro, Jaime Valls , Qiu, Xiaojun
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Macau, China; 03-08 November 2019 p. 5346-5351
- Full Text: false
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- Description: Knowing the geometry of a space is desirable for many applications, e.g. sound source localization, sound field reproduction or auralization. In circumstances where only acoustic signals can be obtained, estimating the geometry of a room is a challenging proposition. Existing methods have been proposed to reconstruct a room from the room impulse responses (RIRs). However, the sound source and microphones must be deployed in a feasible region of the room for it to work, which is impractical when the room is unknown. This work propose to employ a robot equipped with a sound source and four acoustic sensors, to follow a proposed path planning strategy to moves around the room to collect first image sources for room geometry estimation. The strategy can effectively drives the robot from a random initial location through the room so that the room geometry is guaranteed to be revealed. Effectiveness of the proposed approach is extensively validated in a synthetic environment, where the results obtained are highly promising.
Categorical features transformation with compact one-hot encoder for fraud detection in distributed environment
- Authors: Ul Haq, Ikram , Gondal, Iqbal , Vamplew, Peter , Brown, Simon
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 16th Australasian Conference on Data Mining, AusDM 2018; Bathurst, NSW; 28 November 2018 through 30 November 2018 Vol. 996, p. 69-80
- Full Text: false
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- Description: Fraud detection for online banking is an important research area, but one of the challenges is the heterogeneous nature of transactions data i.e. a combination of numeric as well as mixed attributes. Usually, numeric format data gives better performance for classification, regression and clustering algorithms. However, many machine learning problems have categorical, or nominal features, rather than numeric features only. In addition, some machine learning platforms such as Apache Spark accept numeric data only. One-hot Encoding (OHE) is a widely used approach for transforming categorical features to numerical features in traditional data mining tasks. The one-hot approach has some challenges as well: the sparseness of the transformed data and that the distinct values of an attribute are not always known in advance. Other than the model accuracy, compactness of machine learning models is equally important due to growing memory and storage needs. This paper presents an innovative technique to transform categorical features to numeric features by compacting sparse data even if all the distinct values are not known. The transformed data can be used for the development of fraud detection systems. The accuracy of the results has been validated on synthetic and real bank fraud data and a publicly available anomaly detection (KDD-99) dataset on a multi-node data cluster. © Springer Nature Singapore Pte Ltd. 2019.
Comparative study on object tracking algorithms for mobile robot navigation in GPS-denied environment
- Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 19-26
- Full Text: false
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- Description: This paper presents a comparative study conducted on the performance of the commonly used object tracking and location prediction algorithms for mobile robot navigation in a dynamically cluttered and GPS-denied mining environment. The study was done to test the different algorithms for the same set criteria (such as accuracy and computational time) under the same conditions.The identified commonly used algorithms for object tracking and location prediction of moving objects used in this investigation are Kalman filter (KF), extended Kalman filter (EKF) and particle filter (PF). The study results of those algorithms are analyzed and discussed in this paper. A trade-off was apparent. However, in overall performance KF has shown its competitiveness.The result from the study has found that the KF based algorithm provides better performance in terms of accuracy in tracking dynamic objects under commonly used benchmarks. This finding can be used in development of an efficient robot navigation algorithm.
- Description: Proceedings of the IEEE International Conference on Industrial Technology
Comparing Pixel N-grams and bag of visual word features for the classification of diabetic retinopathy
- Authors: Kulkarni, Pradnya , Stranieri, Andrew , Jelinek, Herbert
- Date: 2019
- Type: Text , Conference proceedings
- Relation: ACSW 2019: Australasian Computer Science Week 2019;Sydney NSW Australia; January 29 - 31, 2019; published in Proceedings of the Australasian Computer Science Week Multiconference p. 1-7
- Full Text: false
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- Description: The extraction of Bag of Visual Words (BoVW) features from retinal images for automated classification has been shown to be effective but computationally expensive. Histogram and co-variance matrix features do not generally result in models that have the same predictive accuracy as BoVW and are still computationally expensive. The discovery of features that result in accurate image classification on computationally constrained devices such as smartphones would enable new and promising applications for image classification. For example, smartphone retinal cameras can conceivably make diabetic retinopathy widely available and potentially reduce undiagnosed retinopathy if it could be achieved with computationally simple classification algorithms. A novel image feature extraction technique inspired by N-grams in text mining, called 'Pixel N-grams' is described that can serve this purpose. Results on mammogram and texture classification have shown high accuracy despite the reduced computational complexity. However retinal scan classification results using Pixel N-grams lag behind BoVW approaches. An explanation for the relative poor performance of Pixel N-grams with diabetic retinopathy that draws on concepts associated with the No Free Lunch theorem are presented.
Cybersecurity indexes for eHealth
- Authors: Burke, Wendy , Oseni, Taiwo , Jolfaei, Alireza , Gondal, Iqbal
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 Australasian Computer Science Week Multiconference, ACSW 2019; Sydney, Australia; 29th-31st January 2019 p. 1-8
- Full Text: false
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- Description: This study aimed to explore the cybersecurity landscape to identify cybersecurity indexes that may be relevant to the health industry. While the healthcare sector poses security concerns regarding patients' records, cybersecurity in the healthcare sector has not been given much consideration. Cybersecurity indexes are a survey that measures security preparedness and capabilities of a country or organisation. An index is made up of a series of questions, often broken into categories. These categories target areas such as law, technical responses, organisational threats, capacity building and social context. Some indexes provide ranking capabilities against other countries, while others directly evaluate what it means to be cyber-ready. In this paper, cybersecurity indexes were reviewed regarding the level of assessment (country level/organisation level), and their consideration of the wider community, the health sector, and their appearance in academic literature. Results from this study found that there was no consistency between the indexes investigated, with each index having a diverse number of categories and indicators. Some indexes resulted in a score; others did not rank their results in league tables. Evidence to calculate the level of adherence was often obtained from secondary sources, with four of the country indexes using both primary and secondary sources. Eight (out of fourteen) indexes measured wider community indicators and only one index specifically measured eHealth services. Findings from the initial systematic review suggest that hardly any peer-reviewed journal articles exist on the topic of cybersecurity indexes. The paper concludes that most of the indexes studied are broad and do not consider the eHealth sector specifically. Each index relies on a different process to gauge cybersecurity, with little to no academic rigour. It is expected that this research will contribute to the current (limited) literature addressing cybersecurity indexes.
- Description: ACM International Conference Proceeding Series
Designing security intelligent agent for petrol theft prevention
- Authors: Bakkar, Mahmoud , Alazab, Ammar
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 Cybersecurity and Cyberforensics Conference CCC 2019; Melbourne, VIC, Australia; 8-9 May 2019 p. 123-128
- Full Text: false
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- Description: Automotive industry has increased exponentially in recent years, and the number of car drivers has increased in the street as well, that lead to the increasing demand for using fuel stations. The increasing demand causes an increase of theft cases in the fuel stations particularly customers filling their cars and not paying for it. Although, there are several anti-petrol theft initiatives which include the use of Closed-Circuit Television Cameras (CCTV) to recognize vehicle number plates or people's faces. However, the record shows that existing methods for detecting petrol theft are less ineffective and time-consuming as it has been delayed in detecting the offenders and it is not a good measure to deter offenders as it is weak to be precise on evidence/mapping features. In this paper, Media Access Control (MAC) address detection of mobile devices used for preventing the petrol theft. Mac addresses are extracted from the customer mobile devices to develop a framework that can prevent and detect petrol theft. Also, car plate number is captured as well to develop this framework.
Detection and compensation of covert service-degrading intrusions in cyber physical systems through intelligent adaptive control
- Authors: Farivar, Faezeh , Haghighi, Mohammad , Barchinezhad, Soheila , Jolfaei, Alireza
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 1143-1148
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- Description: Cyber-Physical Systems (CPS) are playing important roles in the critical infrastructure now. A prominent family of CPSs are networked control systems in which the control and feedback signals are carried over computer networks like the Internet. Communication over insecure networks make system vulnerable to cyber attacks. In this article, we design an intrusion detection and compensation framework based on system/plant identification to fight covert attacks. We collect error statistics of the output estimation during the learning phase of system operation and after that, monitor the system behavior to see if it significantly deviates from the expected outputs. A compensating controller is further designed to intervene and replace the classic controller once the attack is detected. The proposed model is tested on a DC motor as the plant and is put against a deception signal amplification attack over the forward link. Simulation results show that the detection algorithm well detects the intrusion and the compensator is also successful in alleviating the attack effects.
Development of a risk-based maintenance (RBM) strategy for sewerage pumping station network
- Authors: Masud, M. , Chattopadhyay, Gopi , Gunawan, Indra
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2019 p. 455-458
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- Description: Industries have been facing ever-increasing challenges to do more with less under ongoing budget constraints. They are pushing the boundary by challenging the OEM recommended maintenance intervals and relaxing or tightening based on where it is needed. This is also evident in water sector where industries are trying to do targeted maintenance based on balancing costs, performances and risks. The unexpected failures, the down time associated with such failures, the environmental overflows and, the increasing maintenance costs are major challenges all wastewater reticulation and distribution networks. Industries have been working hard to increase the availability of equipment and reduce the life-cycle cost without compromising safety and environmental targets. Risk-based maintenance (RBM) strategy is useful for allocation of maintenance resources where first allocation occurs to the highest risk item and progressively allocated till it reached budget limits. This paper is based on findings from a study covering 186 sewerage pumping stations of Townsville Water in North of Queensland in Australia. This study covered identifying the critical subsystems and mitigating the risks of failure of those subsystems. Implementation of risk based maintenance strategy was useful in further enhancing reliability and reduction of maintenance costs. © 2019 IEEE.
- Description: E1
Diabetic retinopathy detection : methods and challenges
- Authors: Patil, Shivani , Kulkarni, Pradnya
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2nd IEEE Pune Section International Conference, PuneCon 2019
- Full Text: false
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- Description: Diabetic retinopathy (DR) mostly affects the people as a result of diabetes and it leads to blindness. Diabetic retinopathy (DR) affects the eyes as a result of increased blood glucose levels. Among people in the 70-year-old age group, 50% of deaths are associated with diabetes. Detection of diabetes at a early stage and taking a proper treatment can reduce vision loss among the patients. Once DR symptoms are recognized, the severity of the disease must be assessed in order to prescribe the correct medication. There are five stages of severity of diabetic retinopathy, Mild Non Proliferative Diabetic Retinopathy (NPDR),Moderate NPDR, Severe NPDR, Proliferative Diabetic Retinopathy(PDR) and No DR(Diabetic Retinopathy).This paper summarizes DR detection methods and problems. © 2019 IEEE.
- Description: E1
Differentially private streaming to untrusted edge servers in intelligent transportation system
- Authors: Ezabadi, Soheila , Jolfaei, Alireza , Kulik, Lars , Ramamohanarao, Kotagiri
- Date: 2019
- Type: Text , Conference paper , Conference proceedings
- Relation: 2019 18th Ieee International Conference on Trust, Security and Privacy in Computing and Communications/13th Ieee International Conference on Big Data Science and Engineering; Rotorua, New Zealand; 5th- 8th August 2019 p. 781-786
- Full Text: false
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- Description: This paper considers the privacy issues in the intelligent transportation system, in which the data is largely communicated based upon vehicle-to-infrastructure and vehicle-to-vehicle protocols. The sensory data communicated by the vehicles contain sensitive information, such as location and speed, which could violate the driver's privacy if they are leaked with no perturbation. Recent studies suggested mechanisms for randomizing the stream of vehicular data to ensure individuals' privacy. Although the past works on differential privacy provide a strong privacy guarantee, they are limited to applications where communication parties are trusted and/or data is limited to a few types. In this paper, we address this gap by proposing a differentially private mechanism that adds noise in the user side rather than the server. Also, our mechanism is able to perturb various types of data as pointed out by the dedicated short-range communication protocols in the automotive industry. The proposed mechanism is data adaptive and scales the noise with respect to the data type and distribution. Our extensive experiments show the accuracy of our mechanism compared to the recent approaches.
Discovering regularities from traditional chinese medicine prescriptions via bipartite embedding model
- Authors: Ruan, Chunyang , Ma, Jiangang , Wang, Ye , Zhang, Yanchun , Yang, Yun
- Date: 2019
- Type: Text , Conference proceedings
- Relation: International Joint Conferences on Artificial Intelligence (IJCAI-49); Macao, China; 10th-16th August 2019; published in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) p. 3346-3352
- Full Text: false
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- Description: Regularities analysis for prescriptions is a significant task for traditional Chinese medicine (TCM), both in inheritance of clinical experience and in improvement of clinical quality. Recently, many methods have been proposed for regularities discovery, but this task is challenging due to the quantity, sparsity and free-style of prescriptions. In this paper, we address the specific problem of regularities discovery and propose a graph embedding based framework for regularities discovery for massive prescriptions. We model this task as a relation prediction in which the correlation of two herbs or of herb and symptom are incorporated to characterize the different relationships. Specifically, we first establish a heterogeneous network with herbs and symptoms as its nodes. We develop a bipartite embedding model termed HS2Vec to detect regularities, which explores multiple relations of herbherb, and herb-symptom based on the heterogeneous network. Experiments on four real-world datasets demonstrate that the proposed framework is very effective for regularities discovery.
Disease gene prediction based on heterogeneous probabilistic hypergraph ranking
- Authors: Ding, Feng , Kong, Xiangjie , Zhao, Zhehuan , Xia, Feng , Liu, Anfu , Bai, Chenxu , Xu, Bo , Liu, Xiaoxia , Sang, Shengtian , Lin, Hongfei , Yang, Zhihao , Wang, Jian
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); San Diego, CA, USA; 18-21 November 2019 p. 2021-2028
- Full Text: false
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- Description: In order to save time and cost, many disease gene prediction methods have been proposed in recent years. However, the traditional network model uses a binary relationship to represent the relationship between different proteins or gene molecules and phenotypes, which leads to the loss of information. Recently, hypergraph shows that it can overcome this loss of information to some extent and preserve the multivariate relationship, so we transformed the disease gene prediction problem into the problem of ranking the multivariate-relationship object. In this paper, we propose a method of Heterogeneous Probabilistic Hypergraph Ranking (HPHR) to predict disease genes. Firstly, fix a graph centroid for each hyperedge and according to different associations, and add other nodes related to the graph centroid to hyperedges with a certain probability. Then transform the problem of predicting disease genes into the problem of ranking heterogeneous objects, and the candidate genes are sorted by hypergraph ranking. The method is then applied to the integrated disease gene network. Compared with other prediction methods achieved better results, which was verified by this experiment.
Distortion robust image classification using deep convolutional neural network with discrete cosine transform
- Authors: Hossain, Md Tahmid , Teng, Shyh Wei , Zhang, Dengsheng , Lim, Suryani , Lu, Guojun
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 IEEE International Conference on Image Processing (ICIP);Taipei, Taiwan; 22-25 Sept, 2019 p. 659-663
- Full Text: false
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- Description: Convolutional Neural Networks are highly effective for image classification. However, it is still vulnerable to image distortion. Even a small amount of noise or blur can severely hamper the performance of these CNNs. Most work in the literature strives to mitigate this problem simply by fine-tuning a pre-trained CNN on mutually exclusive or a union set of distorted training data. This iterative fine-tuning process with all known types of distortion is exhaustive and the network struggles to handle unseen distortions. In this work, we propose distortion robust DCT-Net, a Discrete Cosine Transform based module integrated into a deep network which is built on top of VGG16 [1]. Unlike other works in the literature, DCT-Net is "blind" to the distortion type and level in an image both during training and testing. The DCT-Net is trained only once and applied in a more generic situation without further retraining. We also extend the idea of dropout and present a training adaptive version of the same. We evaluate our proposed DCT-Net on a number of benchmark datasets. Our experimental results show that once trained, DCT-Net not only generalizes well to a variety of unseen distortions but also outperforms other comparable networks in the literature.
Enhancing branch predictors using genetic algorithm
- Authors: Haque, Md Sarwar , Hassan, Md Rafiul , Sulaiman, Muhammad , Onoruoiza, Salami , Kamruzzaman, Joarder , Arifuzzaman, Md
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 8th International Conference on Modeling Simulation and Applied Optimization, ICMSAO 2019
- Full Text: false
- Reviewed:
- Description: Dynamic branch prediction is a hardware technique used to speculate the direction of control branches. Inaccurate prediction will make all speculative works useless while accurate prediction will significantly improve microprocessors performance. In this work, we have shown that Genetic Algorithm (GA) can be used to select (near) optimal parameters for branch predictors in most cases. The GA-enhanced predictors take time to find suitable parameters, but once the values of these parameters are determined, the GA-enhanced predictors take the same time to execute as the basic predictors with increased accuracy. © 2019 IEEE.
- Description: E1
Evolved similarity techniques in malware analysis
- Authors: Black, Paul , Gondal, Iqbal , Vamplew, Peter , Lakhotia, Arun
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 18th IEEE International Conference On Trust, Security And Privacy; published in In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 5-8th Aug, 2019 p. 404-410
- Full Text: false
- Reviewed:
- Description: Malware authors are known to reuse existing code, this development process results in software evolution and a sequence of versions of a malware family containing functions that show a divergence from the initial version. This paper proposes the term evolved similarity to account for this gradual divergence of similarity across the version history of a malware family. While existing techniques are able to match functions in different versions of malware, these techniques work best when the version changes are relatively small. This paper introduces the concept of evolved similarity and presents automated Evolved Similarity Techniques (EST). EST differs from existing malware function similarity techniques by focusing on the identification of significantly modified functions in adjacent malware versions and may also be used to identify function similarity in malware samples that differ by several versions. The challenge in identifying evolved malware function pairs lies in identifying features that are relatively invariant across evolved code. The research in this paper makes use of the function call graph to establish these features and then demonstrates the use of these techniques using Zeus malware.
Gaussian mixture marginal distributions for modelling remaining metallic pipe wall thickness
- Authors: Nguyen, Linh , Miro, Jaime Valls , Shi, Lei , Vidal-Calleja, Teresa
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM);Bangkok, Thailand; 18-20 November 2019 p. 257-262
- Full Text: false
- Reviewed:
- Description: Rapidly estimating the remaining wall thickness (RWT) is paramount for the non-destructive condition assessment evaluation of large critical metallic pipelines. A robotic vehicle with embedded magnetism-based sensors has been developed to traverse the inside of a pipeline and conduct inspections at the location of a break. However, its sensing speed is constrained by the magnetic principle of operation, thus slowing down the overall operation in seeking dense RWT mapping. To ameliorate this drawback, this work proposes the partial scanning of the pipe and then employing Gaussian Processes (GPs) to infer RWT at the unseen pipe sections. Since GP prediction assumes to have normally distributed input data - which does correspond with real RWT measurements - Gaussian mixture (GM) models are proven in this work as fitting marginal distributions to effectively capture the probability of any RWT value in the inspected data. The effectiveness of the proposed approach is validated from real-world data collected in collaboration with a water utility from a cast iron water main pipeline in Sydney, Australia.
Generating linked data repositories using UML artifacts
- Authors: Khan, Aqsa , Malik, Saleem
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 1st Intelligent Technologies and Applications, Intap 2018; Bahawalpur, Pakistan; 23rd-25th October 2018; published in Communications in Computer and Information Science book Series Vol. 932, p. 142-152
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- Description: The usability of diagrams and models is increasing day by day, because of this we experience problem in searching and accessing from large size repositories of diagrams and models of software systems. This research might be helpful to search and access the diagrams and models in bigger repositories. For this purpose, this research developed linked data repositories which contain UML (Unified Modeling Language) artifacts, these artifacts are being organized with using UML class model. In particular, UML is being broadly applied to data modeling in many application domains, and generating linked data repositories from the UML class model is becoming a challenging task in the context of semantic web. This paper proposes an approach, in which we will build a construction tool by joining the characteristics of RDF (Resource Description Framework) and UML. Firstly we will formally define design artifacts and linked data repositories. After that we will propose a construction tool in which we will extract UML artifacts, these UML class model further transforms into the corresponding RDFs. The generated RDF linked data then will be verified by using W3C RDF, this is a validating service used to generate and verify the RDF triples and graphs. Finally, the proposed construction tool will be implemented with few experiments and research is validated using W3C RDF validating service. The proposed approach aims to give such a design that may facilitate the users to customize linked data repositories so that diagrams and models could be examined from large size data.
Generative malware outbreak detection
- Authors: Park, Sean , Gondal, Iqbal , Kamruzzaman, Joarder , Oliver, Jon
- Date: 2019
- Type: Text , Conference proceedings , Conference paper
- Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019 Vol. 2019-February, p. 1149-1154
- Full Text: false
- Reviewed:
- Description: Recently several deep learning approaches have been attempted to detect malware binaries using convolutional neural networks and stacked deep autoencoders. Although they have shown respectable performance on a large corpus of dataset, practical defense systems require precise detection during the malware outbreaks where only a handful of samples are available. This paper demonstrates the effectiveness of the latent representations obtained through the adversarial autoencoder for malware outbreak detection. Using instruction sequence distribution mapped to a semantic latent vector, the model provides a highly effective neural signature that helps detecting variants of a previously identified malware within a campaign mutated with minor functional upgrade, function shuffling, or slightly modified obfuscations. The method demonstrates how adversarial autoencoder can turn a multiclass classification task into a clustering problem when the sample set size is limited and the distribution is biased. The model performance is evaluated on OS X malware dataset against traditional machine learning models. © 2019 IEEE.
- Description: E1
Green Motivation in China: Insights from a large hybrid mixture of ownership and corporate governance state-owned Cashmere Producer
- Authors: Song-Turner, Helen , Moyeen, Abdul
- Date: 2019
- Type: Text , Conference proceedings
- Relation: The Components of sustainable development approaches to global sustainability, markets and governance; Singapore; 2019 p. 103-129
- Full Text: false
- Reviewed:
- Description: Evidence shows the understanding of green marketing and corporate green decision-making in China is still underdeveloped. The purpose of this paper is to investigate the perception, motivation and marketing practices of a large joint-operated cashmere firm in the textile industry. Both Chinese domestic factors and international economic trends have contributed to the rapid restructuring of the Chinese cashmere industry into a highly dynamic, flexible and international openness towards green development. Drawing on the literature on firms’ motivations to go green and characteristics of firms which induce green initiatives, this paper selected a large hybrid mixture of ownership and corporate governance state-owned cashmere producer as basis for a case study. This study illustrates the central role of the top management team as the firm operator within the jointly owned state-owned firm and the influence of firm’s past history and value in their green initiatives. While findings from this case have confirmed some of the literature on green motivation, they also exposed the reform of state-owned firms’ ownership and governance for the firm contributes to more effective management and more efficient operations pursuing cost saving and profit-making, more responsiveness to market demand and in turn to increase the resource utilization efficiency and environmental performance. This research has hence provided new insights and policy implication for a successful transition towards a market economy in China with firms that are both economically strong and also socially and ecologically sustainable.