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ANSWER : generating information dissemination network on campus

- Qing, Qing, Guo, Teng, Zhang, Dongyu, Xia, Feng

  • Authors: Qing, Qing , Guo, Teng , Zhang, Dongyu , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 32nd Australasian Database Conference, ADC 2021 Vol. 12610 LNCS, p. 74-86
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  • Description: Information dissemination matters, both on an individual and group level. For college students who are physically and mentally immature, they are more sensitive and susceptible to unnormal information like rumors. However, current researches focus on large-scale online message sharing networks like Facebook and Twitter, rather than profile the information dissemination on campus, which fail to provide any references for daily campus management. Against this background, we propose a framework to generate the information dissemination network on campus, named ANSWER (cAmpus iNformation diSsemination netWork gEneRation), based on multimodal data including behavior data, appearance data, and psychological data. The construction of the ANSWER is listed as four steps. First, we use a convolutional autoencoder to extract the students’ facial features. Second, we process the behavior data to construct a friendship network. Third, heterogeneous information is embedded in the low-dimensional vector space by using network representation learning to obtain embedding vectors. Fourth, we use the deep learning model to predict. The experiment results show that ANSWER outperforms other methods in multiple feature fusion and prediction of information dissemination relationship performance. © 2021, Springer Nature Switzerland AG.

As simple as pressing a button? A review of the literature on BigBlueButton

- Ukoha, Chukwuma

  • Authors: Ukoha, Chukwuma
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 6th Information Systems International Conference, ISICO 2021, Virtual, Online 7 August 2021through 8 August 2021, Procedia Computer Science Vol. 197, p. 503-511
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  • Description: BigBlueButton is an open source virtual classroom software. Since this software was released in 2009, many studies have explored how to use it, especially for e-learning. However, to date, there is no published systematic synthesis of the relevant literature on the subject. This literature review appraises the effectiveness of BigBlueButton in educational settings and pulls relevant pieces of information together into a readable format. The main conclusion is that BigBlueButton is intuitive, interoperable with other software and has the potential to positively affect the learning performance of students. Despite the features and functionalities of BigBlueButton, several limitations are apparent: web conference educators have less control over online teaching compared with their face-to-face counterparts, practical subjects are difficult to teach through web conferencing, technical challenges may affect web-conferencing sessions, web conferencing requires skills additional to those of conventional teaching, cultural differences may affect students' attitudes towards web conference-based learning and educators that teach through web conferencing may feel isolated in their role, both geographically and collegially. By reviewing the features, potential impacts and limitations of BigBlueButton, this study contributes to the growing literature on web conferencing systems and provides insights into the role of BigBlueButton in e-learning. © 2021 The Authors. Published by Elsevier B.V.

Assessing reliability of smart grid against cyberattacks using stability index

- Rashed, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder, Islam, Syed

  • Authors: Rashed, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder , Islam, Syed
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 31st Australasian Universities Power Engineering Conference, AUPEC 2021, Virtual, Online 26 to 30 September 2021, Proceedings of 2021 31st Australasian Universities Power Engineering Conference, AUPEC 2021
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  • Description: The degradation of stability index within smart grid leads to incorrect power generation and poor load balancing. The remote data dependency of the central energy management system (CEMS) causes communication delay that further leads to poor synchronization within the system. This becomes worse in the presence of cyber-attacks such as stealth or false data injection attack (FDIA). We used dynamic estimation to obtain state data after the inception of false data attack and analyzed its impact on the stability index of the smart grid. A lookup table was constructed based on the fluctuations within the voltage estimates of IEEE-Bus system. An index number was assigned to output estimates at the bus that highlights the level of severity within the grid. We used IEEE-57 Bus using MATLAB to capture and plot the results related to voltage estimates, latency, and inception time delay. The results demonstrate a clear relationship between stability index and state estimates especially when the system is under the influence of a cyber-attack. © 2021 IEEE.

Benefit based transmission expansion planning for ASEAN power grid

- Ahmed, Tofael, Mekhilef, Saad, Shah, Rakibuzzaman

Carbon offsetting in the road transport industry : issues and challenges of meeting the objectives

- Peters, Lies, Chattopadhyay, Gopinath, Kandra, Harpreet

  • Authors: Peters, Lies , Chattopadhyay, Gopinath , Kandra, Harpreet
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2021; Ballarat; 12-15 December 2021
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  • Description: The overall performance of the transport industry is generally reported in terms of moving goods, or freight or people from any location to their final destination in a safe, reliable, cost-effective, and timely manner. However, greenhouse gas and exhaust fumes resulting from their operations has an adverse impact on climate and health of people living around transport corridors. Carbon dioxide (CO2) in exhaust fumes, if not contained, can cause poisonous air pollution and contributes to global warming. Australia's Department of Agriculture, Water and the Environment statistics predict that in 2030, CO2 emissions from the road transport industry through the usage of articulated and rigid trucks, are expected to grow by 37 percent from 2015 levels and is 80 percent higher compared to 2006 levels. In various parts of the world, there are annual mandatory road worthiness and exhaust testing requirements for responding to these problems. Some countries like Singapore (5.4 years) Luxembourg (6.5 years) and Austria (8.3 years), replace vehicles in five to 10 years on an average. Affordability, easy access, congestions, greenhouse gasses, impacts on health and safety are some of the important factors in asset management that can potentially impact climate change. Fleet asset management considers capital investments, operational and maintenance costs for informed decision making based on risks, life cycle costs and performances. This paper presents a review of studies on the impact of carbon offsetting on the transport infrastructure. Issues and challenges of alternative options for reducing risks and lifecycle costs along with approaches for enhancing performance covering reduction of greenhouse gasses and adverse impacts on human health have also been presented. © 2021 IEEE.

Choosing VET as a post-school activity: What are some influences on non-metropolitan students?

- Smith, Erica, Foley, Annette

  • Authors: Smith, Erica , Foley, Annette
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: AVETRA 21 Virtual conference: recover, rethink, rebuild: all eyes on VET, 19-23 April 2021
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  • Description: This paper draws on data from recently-completed research funded by the Victorian Department of Education and Training (DET) and undertaken in the State of Victoria, in six non-metropolitan communities: three in rural/regional areas and three in peri-urban areas. The rationale for the research was that, despite decades of effort, education outcomes for rural and regional areas in Australia remain well under the Australian average (Napthine et al, 2019), partly because so many young people need to leave home to attend tertiary education (McKenzie, 2014). There is almost no specific research on peri-urban areas. For this paper we have extracted data, from selected phases of the project, specifically to find out why young people may or may not make VET choices. The method for this paper comprised analysis of data from each site, consisting of: • Interviews with VET-sector organisations; • ‘Snapshot surveys’, completed, prior to interviews and focus groups, by 80 young people in schools and 32 in their second-year out; • Publicly-available government ‘On-Track’ data (DET, 2018), of young people in their first year out of school. Recent related literature looks at VET choices in terms of the perceived and actual financial rewards of VET choices (e.g. Norton & Charastidtham, 2019); or in terms of the perceived status of VET choices (e.g. Billett, Choy & Hodge, 2019). Our research showed a complex picture with a number of factors (personal, environmental, cultural background and geographic) influencing choices; and also a perception that VET means apprenticeships, almost to the exclusion of traineeships or full-time VET. The agency of individual schools and of VET providers or apprenticeship organisations was also found to be important. The findings have clear implications for both policy and practice.

Churn prediction in telecom industry using machine learning ensembles with class balancing

- Chowdhury, Abdullahi, Kaisar, Shahriar, Rashid, Md Mamunur, Shafin, Sakib, Kamruzzaman, Joarder

  • Authors: Chowdhury, Abdullahi , Kaisar, Shahriar , Rashid, Md Mamunur , Shafin, Sakib , Kamruzzaman, Joarder
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021, Brisbane, 8-10 December 2021
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  • Description: Telecommunication service providers are going through a very competitive and challenging time to retain existing customers by offering new and attractive services (e.g., unlimited local and international calls, high-speed internet, new phones). It is therefore imperative to analyse and predict customer churn behaviour more accurately. One of the major challenges to analyse churn data and build better prediction model is the imbalance nature of the data. Customer behaviour for churn and non-churn scenarios may contain resembling features. Using a single classifier or simple oversampling method to handle data imbalance often struggles to identify the minority (churn) class data. To overcome the issue, we introduce a model that uses sophisticated oversampling technique in conjunction with ensemble methods, namely Random Forest, Gradient Boost, Extreme Gradient Boost, and AdaBoost. The hyperparameters of the baseline ensemble methods and the oversampling methods were tuned in several ways to investigate their impact on prediction performances. Using a widely used publicly available customer churn dataset, prediction performance of the proposed model was evaluated in term of various metrics, namely, accuracy, precision, recall, F-1 score, AUC under ROC curve. Our model outperformed the existing models and significantly reduced both false positive and false negative prediction. © IEEE 2022.

Coding observer nodes for sybil attacks detection in mobile wireless sensor networks

- Sassani Sarrafpour, Bahman, Alomirah, Alomirah, Pang, Shaning, Sarrafpour, Soshian

  • Authors: Sassani Sarrafpour, Bahman , Alomirah, Alomirah , Pang, Shaning , Sarrafpour, Soshian
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 19th IEEE International Conference on Embedded and Ubiquitous Computing, EUC 2021, Shenyang, China. 20-22 October 2021, Proceedings - 2021 IEEE 19th International Conference on Embedded and Ubiquitous Computing, EUC 2021 p. 87-94
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  • Description: Sybil attack is one of the most common and serious attacks in wireless sensor networks, in which a malicious node illegitimately forges several (fake) identities. These fake copies confuse and collapse the network. Sybil attack causes too many threats to the routing algorithm, data aggregation, fair resource allocation, voting system, and misbehavior detection. In this paper, we propose a new lightweight algorithm for detecting the Sybil attack in mobile wireless sensor networks using observer nodes. Observer nodes are normal, trustful nodes which have been initially programmed to observe the network and report malicious behaviors. An observer node counts the number of times a node has appeared as a common neighbor between itself and its neighbors. After collecting some information about its neighbors, each observer node considers the nodes whose counters are above a threshold as critical, and nodes having all critical nodes in their neighborhood are considered suspicious nodes. The results show that the true detection rate of the proposed algorithm is 98.1%, and its false detection rate is 0.5%, while similar algorithms could not achieve better than 95.4% and 1.2% for these metrics, respectively. In addition, the proposed algorithm outperforms other algorithms in terms of overhead. © 2021 IEEE.

Conceptual modelling of railway infrastructure cost-a system dynamics approach

- Mafokosi, Katleho, Pretorius, Jan-Harm, Chattopadhyay, Gopinath

  • Authors: Mafokosi, Katleho , Pretorius, Jan-Harm , Chattopadhyay, Gopinath
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Conference on Maintenance and Intelligent Asset Management; ICMIAM 2021; Ballarat, 12-15 December 2021
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  • Description: The investment decision making in railway infrastructure plays a vital role in safe and reliable utilization of the infrastructure. There is a considerable contribution to the country's economy from railway transport infrastructure, which requires continuous investment in the railway infrastructure. The infrastructure investment in maintenance, renewal and construction increases the availability and capacity of the railway infrastructure. Economic indicators are used to analyze and evaluate the infrastructure life cycle cost in order to justify the investment decisions. System dynamics modelling is used to outline the feedback structure of railway infrastructure life cycle cost. The system structure, system component relationships, and system behavior are defined using systems thinking in order to analyze the causality between different variables. System dynamic modelling shows how different variables influence the railway infrastructure life cycle cost and investment decision making. The understanding of how the system works enables well-informed decision making. The application of the system dynamics approach in the conceptual modelling of the railway infrastructure cost will enable past system behavior or occurrences to be explained and the future ones to be predicted. © 2021 IEEE.

Cost effective annotation framework using zero-shot text classification

- Kasthuriarachchy, Buddhika, Chetty, Madhu, Shatte, Adrian, Walls, Darren

  • Authors: Kasthuriarachchy, Buddhika , Chetty, Madhu , Shatte, Adrian , Walls, Darren
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Joint Conference on Neural Networks, IJCNN 2021 Vol. 2021-July
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  • Description: Manual and high-quality annotation of social media data has enabled companies and researchers to develop improved implementations using natural language processing. However, human text-annotation is expensive and time-consuming. Crowd-sourcing platforms such as Amazon's Mechanical Turk (MTurk) can be leveraged for the creation of large training corpora for text classification tasks using social media data. Nevertheless, the quality of annotations can vary significantly, based on the interpretations and motivations of annotators completing the tasks. Further, the labelling cost of data through MTurk will increase if target messages are small and having a significant amount of noise (e.g. promotional messages on Twitter). In this work, we propose a new annotation framework to create high-quality human-annotated datasets for text classification from social media data. We present a zero-shot text classification based pre-annotation technique reducing the adverse effects arising due to the highly skewed distribution of data across target classes. The proposed framework significantly reduces the cost and time while maintaining the quality of the annotations. Being generic, it can be applied to annotating text data from any discipline. Our experiment with a Twitter data annotation using the proposed annotation framework shows a cost reduction of 80% with no compromise to quality. © 2021 IEEE.

Cross network representation matching with outliers

- Hou, Mingliang, Ren, Jing, Febrinanto, Febrinanto, Shehzad, Ahsan, Xia, Feng

  • Authors: Hou, Mingliang , Ren, Jing , Febrinanto, Febrinanto , Shehzad, Ahsan , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021, Virtual, online, 7-10 December 2021, IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December, p. 951-958
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  • Description: Research has revealed the effectiveness of network representation techniques in handling diverse downstream machine learning tasks upon graph structured data. However, most network representation methods only seek to learn information in a single network, which fails to learn knowledge across different networks. Moreover, outliers in real-world networks pose great challenges to match distribution shift of learned embeddings. In this paper, we propose a novel joint learning framework, called CrossOSR, to learn network-invariant embeddings across different networks in the presence of outliers in the source network. To learn outlier-aware representations, a modified graph convolutional network (GCN) layer is designed to indicate the potential outliers. To learn more fine-grained information between different domains, a subdomain matching is adopted to align the shift distribution of learned vectors. To learn robust network representations, the learned indicator is utilized to smooth the noise effect from source domain to target domain. Extensive experimental results on three real-world datasets in the node classification task show that the proposed framework yields state-of-the-art cross network representation matching performance with outliers in the source network. © 2021 IEEE.

Cybersecurity risks in meat processing plant and impacts on total productive maintenance

- Chundhoo, Vickram, Chattopadhyay, Gopinath, Karmakar, Gour, Appuhamillage, Gayan

  • Authors: Chundhoo, Vickram , Chattopadhyay, Gopinath , Karmakar, Gour , Appuhamillage, Gayan
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Conference on Maintenance and Intelligent Asset Management; ICMIAM 2021, Ballarat; 12-15 December 2021
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  • Description: Technological changes have been happening in production facilities including food manufacturing industries in an ever-increasing rate. This includes advancement in data capture devices, signal processing, communication capabilities and automated process control systems such as Internet of Things. It is more challenging where production systems are highly reliant on automation and robotics. Remote performance monitoring and controls are becoming progressively vulnerable due to risks associated with cyber security and corporate espionage. May 2021 cyber-Attack forced JBS meats USA to pay 11m in ransom money to stop any further disruptions in services. This heavily impacted JBS global operations including JBS Australian food manufacturing facilities. Food production facilities in Australia have critical control points supported by smart technologies as part of their food safety management systems. Cyber-Attacks on production facilities could result in financial, operational, health and safety consequences. As survey by the Australian Cyber Security Centre in 2020 revealed that Australian small businesses are impacted by cybercime each year with a loss of 300m. To present the potential cyber security threats and their associated risk level, a case study is presented based on the processing and manufacture of meat products in Australia. From this case study, to protect the meat industries from attacks, we identify cyber security attacks and their possible mitigation strategies. This research shows cyber security attacks can severely affect Overall Equipment Effectiveness which motivate us to embed cyber security as an additional pillar in existing 8 pillars Total Productive Maintenance. If cyber security is added as additional pillar, it will improve the quality of end products and overall productivity of manufacturing industries. © 2021 IEEE.

D-optimal design for information driven identification of static nonlinear elements

- Ulapane, Nalika, Thiyagarajan, Karthick, Kodagoda, Sarath, Nguyen, Linh

  • Authors: Ulapane, Nalika , Thiyagarajan, Karthick , Kodagoda, Sarath , Nguyen, Linh
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021 p. 492-497
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  • Description: Identification of static nonlinear elements (i.e., nonlinear elements whose outputs depend only on the present value of inputs) is crucial for the success of system identification tasks. Identification of static nonlinear elements though can pose several challenges. Two of the main challenges are: (1) mathematical models describing the elements being unknown and thus requiring black-box identification; and (2) collection of sufficiently informative measurements. With the aim of addressing the two challenges, we propose in this paper a method of predetermining informative measurement points offline (i.e., prior to conducting experiments or seeing any measured data), and using those measurements for online model calibration. Since we deal with an unknown model structure scenario, a high order polynomial model is assumed. Over fit and under fit avoidance are achieved via checking model convergence via an iterative means. Model dependent information maximization is done via a D-optimal design of experiments strategy. Due to experiments being designed offline and being designed prior to conducting measurements, this method eases off the computation burden at the point of conducting measurements. The need for in-the-loop information maximization while conducting measurements is avoided. We conclude by comparing the proposed D-optimal design method with a method of in-the-loop information maximization and point out the pros and cons. The method is demonstrated for the single-input-single-output (SISO) static nonlinear element case. The method can be extended to MISO systems as well. © 2021 IEEE.

Decision behavior based private vehicle trajectory generation towards smart cities

- Chen, Qiao, Ma, Kai, Hou, Mingliang, Kong, Xiangjie, Xia, Feng

  • Authors: Chen, Qiao , Ma, Kai , Hou, Mingliang , Kong, Xiangjie , Xia, Feng
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 18th International Conference on Web Information Systems and Applications, WISA 2021 Vol. 12999 LNCS, p. 109-120
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  • Description: In contrast with the condition that the trajectory dataset of floating cars (taxis) can be easily obtained from the Internet, it is hard to get the trajectory data of social vehicles (private vehicles) because of personal privacy and government policies. This paper absorbs the idea of game theory, considers the influence of individuals in the group, and proposes a decision behavior based dataset generation (DBDG) model of vehicles to predict future inter-regional traffic. In addition, we adopt simulation tools and generative adversarial networks to train the trajectory prediction model so that the private vehicle trajectory dataset conforming to social rules (e.g., collisionless) is generated. Finally, we construct from macroscopic and microscopic perspectives to verify dataset generation methods proposed in this paper. The results show that the generated data not only has high accuracy and is valuable but can provide strong data support for the Internet of Vehicles and transportation research work. © 2021, Springer Nature Switzerland AG.

Decision-making in complex asset life extension

- Morey, Stephen, Chattopadhyay, Gopinath, Larkins, Jo-Ann

  • Authors: Morey, Stephen , Chattopadhyay, Gopinath , Larkins, Jo-Ann
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2021; Ballarat; 12-15 December 2021
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  • Description: Complex, long-life assets present challenges for life cycle management, particularly decisions about life extension at the end of the asset life. Multiple interlinked risk factors drive the decision-making process. Compounding the difficulty, few ready-To-use life extension methods exist in literature. Complex decision analysis processes which are technically superior, but difficult to follow, may have the effect of alienating decision-makers who do not understand them. A method developed for NASA for risk informed decision making appears more suitable for guiding decision-making in life extension problems. This paper proposes a framework for decision-making in life extension of complex, long-life, capital-intensive assets, addressing some of the important challenges in life extension decision-making. © 2021 IEEE.

Deep video anomaly detection : opportunities and challenges

- Ren, Jing, Xia, Feng, Liu, Yemeng, Lee, Ivan

  • Authors: Ren, Jing , Xia, Feng , Liu, Yemeng , Lee, Ivan
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021, Virtual, Online 7-10 December 2021, IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December, p. 959-966
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  • Description: Anomaly detection is a popular and vital task in various research contexts, which has been studied for several decades. To ensure the safety of people's lives and assets, video surveillance has been widely deployed in various public spaces, such as crossroads, elevators, hospitals, banks, and even in private homes. Deep learning has shown its capacity in a number of domains, ranging from acoustics, images, to natural language processing. However, it is non-trivial to devise intelligent video anomaly detection systems cause anomalies significantly differ from each other in different application scenarios. There are numerous advantages if such intelligent systems could be realised in our daily lives, such as saving human resources in a large degree, reducing financial burden on the government, and identifying the anomalous behaviours timely and accurately. Recently, many studies on extending deep learning models for solving anomaly detection problems have emerged, resulting in beneficial advances in deep video anomaly detection techniques. In this paper, we present a comprehensive review of deep learning-based methods to detect the video anomalies from a new perspective. Specifically, we summarise the opportunities and challenges of deep learning models on video anomaly detection tasks, respectively. We put forth several potential future research directions of intelligent video anomaly detection system in various application domains. Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection. © 2021 IEEE.

Delivery of online electronics and mechatronics labs during lockdowns

- Jayawardena, Amal, Kahandawa, Gayan, Piyathilaka, Lasitha

Detection of android malware using tree-based ensemble stacking model

- Shafin, Sakib, Ahmed, Md Maroof, Pranto, Mahmud, Chowdhury, Abdullahi

  • Authors: Shafin, Sakib , Ahmed, Md Maroof , Pranto, Mahmud , Chowdhury, Abdullahi
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021, Brisbane, 8-10 December 2021
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  • Description: Increasing use of smartphones for everyday activities from banking, education to social networking is putting our personal information at risk as smartphone operating systems and applications are vulnerable to various types of attacks including malware attack. To this end Android operating system is particularly targeted as it is the most widely used mobile operating system. Building a robust detection system that can provide protection against recent attacks and can deliver not only accurate detection but also the type of the attack in order to protect the system is vital. In this study, we propose a twolayer Machine Learning detection model based on Ensemble Learning and Stacked Generalization method to accurately predict and classify the growing attacks on Android smartphones. We evaluated the proposed model on a very recent dataset, named CIC-Maldroid-2020, which contains 11,598 samples with various malicious attack types. The performance of our proposed model was evaluated on widely used metrics, like accuracy, precision, recall & F1-score. It outperforms previous studies done on the same dataset and achieves an accuracy of 99.49% in classifying each attack type. © IEEE 2022.

Detection of Malleefowl Mounds from Point Cloud Data

- Parvin, Nahida, Awrangjeb, Mohammad, Irvin, Marc, Florentine, Singarayer, Murshed, Manzur, Lu, Guojun

  • Authors: Parvin, Nahida , Awrangjeb, Mohammad , Irvin, Marc , Florentine, Singarayer , Murshed, Manzur , Lu, Guojun
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2021, Gold Coast, 29 November to 1 December 2021
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  • Description: Airborne light detection and ranging (LiDAR) data have become cost and time-efficient means for estimating the size of timid fauna populations through the identification of artefacts that evidence their occurrence in a large, hostile geographic area. The unobtrusive detection method helps conservation managers to assess the stability of a population and to design appropriate conservation programs. Here we propose a mound (nest) detection method for Australia's native iconic bird, the Malleefowl, from point cloud data, which can be manipulated to act as a surrogate for population data. Existing detection methods are largely through manual observations, and are therefore not efficient for covering large and remote areas. The proposed mound detection method can identify mound feature based on height and intensity values provided by the point cloud data. Each candidate mound point is initially selected by applying a height threshold utilising the classified ground points and their corresponding digital elevation model (DEM). Then, another threshold based on intensity range derived from ground truth mound area analysis is applied on the extracted initial mound points to find the final candidate mound points. These extracted points are then used to generate a binary mask where the potential mound points are found sparse. To connect those points, a morphological filter is applied on the binary image and found the mound separated from other remaining non-mound objects. To obtain the mound from other non-mound objects, a morphological cleaning operation and a connected component analysis are carried out on the mask. The non-mound objects are removed from the mask utilising the area property of mound derived from the empirical analysis of ground-truth observations. Finally, the effectiveness of the proposed technique is calculated based on ground truth. Although the mound shapes and structures are highly variable in nature, our height and intensity-based mound point extraction method detected 55 % of the ground-truthed mounds. © 2021 IEEE.

Distributed denial of service attack detection using machine learning and class oversampling

- Shafin, Sakib, Prottoy, Shafin, Abbas, Saif, Hakim, Safayat, Chowdhury, Abdullahi, Rashid, Md Mamanur

  • Authors: Shafin, Sakib , Prottoy, Shafin , Abbas, Saif , Hakim, Safayat , Chowdhury, Abdullahi , Rashid, Md Mamanur
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: First International Conference on Applied Intelligence and Informatics, AII 2021, Nottingham, UK, July 30-31, 2021 Vol. 1435, p. 247-259
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  • Description: Distributed Denial of Services (DDoS) attack, one of the most dangerous types of cyber attack, has been reported to increase during the COVID-19 pandemic. Machine learning techniques have been proposed in the literature to build models to detect DDoS attacks. Existing works in literature tested their models with old datasets where DDoS attacks are not specific. These works mainly focus on detecting the presence of an attack rather than the type of DDoS attacks. However, detection of the attack type is vital for the review and analysis of enterprise-level security policy. Cyber-attacks are inherently an imbalanced data problem, but none of the models treated DDoS attack detection from this perspective. In this work, we present a machine learning model that takes the imbalance nature of the DDoS attack data into consideration for both presence/absence and the type of DDoS attack detection. Extensive experiment analysis with the recent and DDoS attack-specific dataset shows that the proposed technique can effectively identify DDoS attacks. © 2021, Springer Nature Switzerland AG.