Carlos Alvarez College of Business Faculty Research
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12588/251
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Browsing Carlos Alvarez College of Business Faculty Research by Department "Information Systems and Cyber Security"
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Item A Novel Certificateless Signature Scheme for Smart Objects in the Internet-of-Things(2017-05-01) Yeh, Kuo-Hui; Su, Chunhua; Choo, Kim-Kwang Raymond; Chiu, WayneRapid advances in wireless communications and pervasive computing technologies have resulted in increasing interest and popularity of Internet-of-Things (IoT) architecture, ubiquitously providing intelligence and convenience to our daily life. In IoT-based network environments, smart objects are embedded everywhere as ubiquitous things connected in a pervasive manner. Ensuring security for interactions between these smart things is significantly more important, and a topic of ongoing interest. In this paper, we present a certificateless signature scheme for smart objects in IoT-based pervasive computing environments. We evaluate the utility of the proposed scheme in IoT-oriented testbeds, i.e., Arduino Uno and Raspberry PI 2. Experiment results present the practicability of the proposed scheme. Moreover, we revisit the scheme of Wang et al. (2015) and revealed that a malicious super type I adversary can easily forge a legitimate signature to cheat any receiver as he/she wishes in the scheme. The superiority of the proposed certificateless signature scheme over relevant studies is demonstrated in terms of the summarized security and performance comparisons.Item Can Hierarchical Transformers Learn Facial Geometry?(2023-01-13) Young, Paul; Ebadi, Nima; Das, Arun; Bethany, Mazal; Desai, Kevin; Najafirad, PeymanHuman faces are a core part of our identity and expression, and thus, understanding facial geometry is key to capturing this information. Automated systems that seek to make use of this information must have a way of modeling facial features in a way that makes them accessible. Hierarchical, multi-level architectures have the capability of capturing the different resolutions of representation involved. In this work, we propose using a hierarchical transformer architecture as a means of capturing a robust representation of facial geometry. We further demonstrate the versatility of our approach by using this transformer as a backbone to support three facial representation problems: face anti-spoofing, facial expression representation, and deepfake detection. The combination of effective fine-grained details alongside global attention representations makes this architecture an excellent candidate for these facial representation problems. We conduct numerous experiments first showcasing the ability of our approach to address common issues in facial modeling (pose, occlusions, and background variation) and capture facial symmetry, then demonstrating its effectiveness on three supplemental tasks.Item Deep Learning-Based Intrusion Detection for Distributed Denial of Service Attack in Agriculture 4.0(2021-05-25) Ferrag, Mohamed Amine; Shu, Lei; Djallel, Hamouda; Choo, Kim-Kwang RaymondSmart Agriculture or Agricultural Internet of things, consists of integrating advanced technologies (e.g., NFV, SDN, 5G/6G, Blockchain, IoT, Fog, Edge, and AI) into existing farm operations to improve the quality and productivity of agricultural products. The convergence of Industry 4.0 and Intelligent Agriculture provides new opportunities for migration from factory agriculture to the future generation, known as Agriculture 4.0. However, since the deployment of thousands of IoT based devices is in an open field, there are many new threats in Agriculture 4.0. Security researchers are involved in this topic to ensure the safety of the system since an adversary can initiate many cyber attacks, such as DDoS attacks to making a service unavailable and then injecting false data to tell us that the agricultural equipment is safe but in reality, it has been theft. In this paper, we propose a deep learning-based intrusion detection system for DDoS attacks based on three models, namely, convolutional neural networks, deep neural networks, and recurrent neural networks. Each model’s performance is studied within two classification types (binary and multiclass) using two new real traffic datasets, namely, CIC-DDoS2019 dataset and TON_IoT dataset, which contain different types of DDoS attacks.Item Emerging Technologies for Future Sensor Networks—Selected Papers from ICGHIT 2019(2019-09-06) Kim, Byung-Seo; Kim, Sung Won; Zhang, Chi; Guo, Yuanxiong; Umer, TariqThe International Conference on Green and Human Information Technology (ICGHIT) is an international conference focusing on green and information technologies oriented toward humanity [...]Item Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing(SpringerOpen, 2016-05-10) Osanaiye, Opeyemi; Cai, Haibin; Choo, Kim-Kwang Raymond; Dehghantanha, Ali; Xu, Zheng; Dlodlo, MqheleWidespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud’s bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.Item Impact and Key Challenges of Insider Threats on Organizations and Critical Businesses(2020-09-07) Saxena, Neetesh; Hayes, Emma; Bertino, Elisa; Ojo, Patrick; Choo, Kim-Kwang Raymond; Burnap, PeteThe insider threat has consistently been identified as a key threat to organizations and governments. Understanding the nature of insider threats and the related threat landscape can help in forming mitigation strategies, including non-technical means. In this paper, we survey and highlight challenges associated with the identification and detection of insider threats in both public and private sector organizations, especially those part of a nation's critical infrastructure. We explore the utility of the cyber kill chain to understand insider threats, as well as understanding the underpinning human behavior and psychological factors. The existing defense techniques are discussed and critically analyzed, and improvements are suggested, in line with the current state-of-the-art cyber security requirements. Finally, open problems related to the insider threat are identified and future research directions are discussed.Item Participatory sensing-based semantic and spatial analysis of urban emergency events using mobile social media(SpringerOpen, 2016-02-09) Xu, Zheng; Zhang, Hui; Sugumaran, Vijayan; Choo, Kim-Kwang Raymond; Mei, Lin; Zhu, YiweiWith the advances of information communication technologies, it is critical to improve the efficiency and accuracy of emergency management systems through modern data processing techniques. Geographic information system (GIS) models and simulation capabilities are used to exercise response and recovery plans during non-disaster times. They help the decision-makers understand near real-time possibilities during an event. In this paper, a participatory sensing-based model for mining spatial information of urban emergency events is introduced. Firstly, basic definitions of the proposed method are given. Secondly, positive samples are selected to mine the spatial information of urban emergency events. Thirdly, location and GIS information are extracted from positive samples. At last, the real spatial information is determined based on address and GIS information. Moreover, this study explores data mining, statistical analysis, and semantic analysis methods to obtain valuable information on public opinion and requirements based on Chinese microblog data. Typhoon Chan-hom is used as an example. Semantic analysis on microblog data is conducted and high-frequency keywords in different provinces are extracted for different stages of the event. With the geo-tagged and time-tagged data, the collected microblog data can be classified into different categories. Correspondingly, public opinion and requirements can be obtained from the spatial and temporal perspectives to enhance situation awareness and help the government offer more effective assistance.Item Secure and privacy-preserving 3D vehicle positioning schemes for vehicular ad hoc network(SpringerOpen, 2018-11-29) Pei, Qianwen; Kang, Burong; Zhang, Lei; Choo, Kim-Kwang RaymondIndustrial wireless networks (IWNs) have applications in areas such as critical infrastructure sectors and manufacturing industries such as car manufacturing. In car manufacturing, IWNs can facilitate manufacturers to improve the design of the vehicles by collecting vehicular status and other related data (such an IWN is also known as vehicular ad hoc networks—VANETs). Vehicle positioning is a key functionality in VANETs. Most existing vehicle positioning systems are capable of providing accurate 2D positioning, but the demand for accurate 3D positioning has increased sharply in recent times (e.g., due to the building of more elevated roads). There are, however, security and privacy concerns relating to 3D positioning systems in VANET. In this paper, we propose two secure and privacy-preserving 3D positioning schemes based on vehicle-to-roadside (V2R) and vehicle-to-vehicle (V2V) communications, respectively. Our schemes are based on the round trip time ranging technique which is used to achieve 3D position. The security and the privacy of vehicles in our schemes are guaranteed through a newly designed one-pass authenticated key agreement protocol. Using experiments, we show that a vehicle can determine whether it is on or under an elevated road in a short period of time.Item Testing the Strength of Hospital Accreditation as a Signal of the Quality of Care in Romania: Do Patients’ and Health Professionals’ Perceptions Align?(2020-09-19) Druică, Elena; Wu, Bingyi; Cepoi, Vasile; Mihăilă, Viorel; Burcea, MarinHospital accreditation, as a quality signal, is gaining its popularity among low- and middle-income countries, such as Romania, despite its costly nature. Nevertheless, its effectiveness as a quality signal in driving patients' choice of hospital services remains unclear. In this study, we intend to empirically explore the perceptions of both healthcare professionals and patients toward Romanian hospital accreditation and identify perception gaps between the two parties. Exploratory and confirmatory factor analyses were carried out to extract the latent constructs of health professionals' perceived effects of hospital accreditation. The Wilcoxon rank-sum test and Kruskal–Wallis test were used to identify correlations between patients' sociodemographic characteristics and their behavioral intentions when confronted with low-quality services. We found that health professionals believe that hospital accreditation plays a positive role in improving patient satisfaction, institutional reputation, and healthcare services quality. However, we found a lack of awareness of hospital accreditation status among patients, indicating the existence of the perception gap of the accreditation effectiveness as a market signal. Our results suggest that the effect of interpersonal trust in current service providers may distract patients from the accreditation status. Our study provides important practical implications for Romanian hospitals on enhancing the quality of accreditation signal and suggests practical interventions.Item Understanding and Analyzing COVID-19-related Online Hate Propagation Through Hateful Memes Shared on Twitter(Association for Computing Machinery, 2024-03-15) Vishwamitra, Nishant; Guo, Keyan; Liao, Song; Mu, Jaden; Ma, Zheyuan; Cheng, Long; Zhao, Ziming; Hu, HongxinRecent studies regarding the COVID-19 pandemic have revealed the widespread propagation of hateful content during this period. While significant research has focused on COVID-19-related online hate in text (e.g., text-based tweets), the role of memes in propagating online hate during the pandemic has been largely overlooked. Memes are a popular mechanism used by Internet users to convey their thoughts and opinions on a variety of topics. However, memes have emerged as an important mechanism through which ideologically potent and hateful content spreads on social media platforms. In this work, we focus on investigating the role of memes in the propagation of online hate during the COVID-19 pandemic. We first collect a novel dataset of 4,001 COVID-19-related hateful memes and their replies over a 3-year period from Twitter. Then, we carry out the first large-scale investigation into the impact of these memes on Twitter users, by studying the psychological reactions of Twitter users to these memes using various text analysis methods. We find that COVID-19-related hateful memes have a significantly greater negative impact on Twitter users in comparison to text-based hateful tweets, and increasing negativity towards such memes over the 3-year period. Our new dataset of COVID-19-related hateful memes and findings from our work pave the way for studying the dissemination and moderation of COVID-19-related online hate through the medium of memes.