Pub Date : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00059
Xiali Li, Yandong Chen, Yanyin Zhang, Bo Liu, Licheng Wu
The rules of the two phases of Tibetan Jiu chess, layout and battle, are very different, and using the same UCT search algorithm globally will result in a large overhead of time and storage space in the search process, so a phased game algorithm for Tibetan Jiu chess is proposed, with different strategies designed for the layout and battle phases, respectively. First, the layout phase uses a combination of Gaussian distribution and fast online estimation to improve the UCT algorithm, thus generating the optimal action selection scheme. Second, in order to take full advantage of reinforcement learning and deep learning, a neural network model with residual network structure is used in the battle phase to guide the search of Monte Carlo trees, and the default strategy is improved by "pruning" in the expansion step to improve the quality of the expanded nodes. The dataset is generated by self-play and used to train the neural network model to obtain the optimal model. It is verified through experiments that the phased gaming algorithm proposed in this study effectively reduces the process of blindly exploring the board state during the layout and battle phases of the UCT search algorithm, and improves the quality of the layout and the self-learning efficiency of the neural network model.
{"title":"A phased game algorithm combining deep reinforcement learning and UCT for Tibetan Jiu chess","authors":"Xiali Li, Yandong Chen, Yanyin Zhang, Bo Liu, Licheng Wu","doi":"10.1109/COMPSAC57700.2023.00059","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00059","url":null,"abstract":"The rules of the two phases of Tibetan Jiu chess, layout and battle, are very different, and using the same UCT search algorithm globally will result in a large overhead of time and storage space in the search process, so a phased game algorithm for Tibetan Jiu chess is proposed, with different strategies designed for the layout and battle phases, respectively. First, the layout phase uses a combination of Gaussian distribution and fast online estimation to improve the UCT algorithm, thus generating the optimal action selection scheme. Second, in order to take full advantage of reinforcement learning and deep learning, a neural network model with residual network structure is used in the battle phase to guide the search of Monte Carlo trees, and the default strategy is improved by \"pruning\" in the expansion step to improve the quality of the expanded nodes. The dataset is generated by self-play and used to train the neural network model to obtain the optimal model. It is verified through experiments that the phased gaming algorithm proposed in this study effectively reduces the process of blindly exploring the board state during the layout and battle phases of the UCT search algorithm, and improves the quality of the layout and the self-learning efficiency of the neural network model.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"2018 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128072639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00206
You Liang, A. Thavaneswaran, Alex Paseka, Sulalitha Bowala, Juan Liyau
A profitable data-driven algorithmic trading algorithm will benefit from a dynamic system that can produce accurate hedge ratio estimates and short-term innovation volatility forecasts. Commonly used pairs and multiple trading strategies are constructed using the Kalman Filter (KF) and exploiting mean reversion in co-integrated nonstationary stock prices. However, KFs are sensitive to model errors. Misspecified modelling produces unstable solutions for dynamic systems. Fading-Memory Filter (FMF) uses a discounting weight to past observations. Compared to a standard KF, FMF addresses more recent observations and is more resilient (less sensitive) to modelling errors. However, the FMF algorithm does not provide slope parameter covariance matrix updates and innovation volatility forecasts. This paper proposes a novel resilient FMF algorithm for pairs trading and multiple trading by defining an appropriate data-driven innovation volatility forecasting model. The FMF-based strategies are implemented through some experiments on the hourly prices (high-frequency data) of Bitcoin, Ethereum and Litecoin. It is shown that the proposed FMF trading strategies outperform the existing KF trading strategies and they are more profitable in the bear market over time, especially for continuous falling of prices and the short-lived and sharp rally recovery where prices are not stationary.
{"title":"A Novel Fading-Memory Filter Multiple Trading Strategy with Data-Driven Innovation Volatility","authors":"You Liang, A. Thavaneswaran, Alex Paseka, Sulalitha Bowala, Juan Liyau","doi":"10.1109/COMPSAC57700.2023.00206","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00206","url":null,"abstract":"A profitable data-driven algorithmic trading algorithm will benefit from a dynamic system that can produce accurate hedge ratio estimates and short-term innovation volatility forecasts. Commonly used pairs and multiple trading strategies are constructed using the Kalman Filter (KF) and exploiting mean reversion in co-integrated nonstationary stock prices. However, KFs are sensitive to model errors. Misspecified modelling produces unstable solutions for dynamic systems. Fading-Memory Filter (FMF) uses a discounting weight to past observations. Compared to a standard KF, FMF addresses more recent observations and is more resilient (less sensitive) to modelling errors. However, the FMF algorithm does not provide slope parameter covariance matrix updates and innovation volatility forecasts. This paper proposes a novel resilient FMF algorithm for pairs trading and multiple trading by defining an appropriate data-driven innovation volatility forecasting model. The FMF-based strategies are implemented through some experiments on the hourly prices (high-frequency data) of Bitcoin, Ethereum and Litecoin. It is shown that the proposed FMF trading strategies outperform the existing KF trading strategies and they are more profitable in the bear market over time, especially for continuous falling of prices and the short-lived and sharp rally recovery where prices are not stationary.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126000441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00200
Karima Boutalbi, Faiza Loukil, H. Verjus, David Telisson, Kave Salamatian
Anomaly detection is a common task in various domains, which has attracted significant research efforts in recent years. Existing reviews mainly focus on structured data, such as numerical or categorical data. Several studies treated review of anomaly detection in general on heterogeneous data or concerning a specific domain. However, anomaly detection on unstructured textual data is less treated. In this work, we target textual anomaly detection. Thus, we propose a systematic review of anomaly detection solutions in the text. To do so, we analyze the included papers in our survey in terms of anomaly detection types, feature extraction methods, and machine learning methods. We also introduce a web scrapping to collect papers from digital libraries and propose a clustering method to classify selected papers automatically. Finally, we compare the proposed automatic clustering approach with manual classification, and we show the interest of our contribution.
{"title":"Machine Learning for Text Anomaly Detection: A Systematic Review","authors":"Karima Boutalbi, Faiza Loukil, H. Verjus, David Telisson, Kave Salamatian","doi":"10.1109/COMPSAC57700.2023.00200","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00200","url":null,"abstract":"Anomaly detection is a common task in various domains, which has attracted significant research efforts in recent years. Existing reviews mainly focus on structured data, such as numerical or categorical data. Several studies treated review of anomaly detection in general on heterogeneous data or concerning a specific domain. However, anomaly detection on unstructured textual data is less treated. In this work, we target textual anomaly detection. Thus, we propose a systematic review of anomaly detection solutions in the text. To do so, we analyze the included papers in our survey in terms of anomaly detection types, feature extraction methods, and machine learning methods. We also introduce a web scrapping to collect papers from digital libraries and propose a clustering method to classify selected papers automatically. Finally, we compare the proposed automatic clustering approach with manual classification, and we show the interest of our contribution.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126061557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00245
Yu Yang, Lu Wang, Na Cha, Hua Li
In order to improve the efficiency of regression testing in the cloud-network convergence platform, a test case prioritization method based on reinforcement learning and a genetic algorithm is proposed. The classical genetic algorithm of initial population and selection operations are improved by incorporating an ant colony algorithm of solutions to form a part of the starting population in the genetic algorithm. The selection process employs an "elite retention strategy" to avoid the classical genetic algorithm of the problem of getting trapped in locally optimal solutions. The improved algorithm is applied to test the cloud-network convergence platform, and the optimization-seeking abilities of the classical genetic algorithm, the ant colony genetic algorithm, and the reinforcement learning-based ant colony genetic algorithm are compared and analyzed. The findings reveal that the reinforcement learning-based ant colony genetic algorithm outperforms the other two algorithms by finding the best test case for the test case prioritization problem.
{"title":"A Test Case Prioritization Based on Genetic Algorithm With Ant Colony and Reinforcement Learning Improvement","authors":"Yu Yang, Lu Wang, Na Cha, Hua Li","doi":"10.1109/COMPSAC57700.2023.00245","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00245","url":null,"abstract":"In order to improve the efficiency of regression testing in the cloud-network convergence platform, a test case prioritization method based on reinforcement learning and a genetic algorithm is proposed. The classical genetic algorithm of initial population and selection operations are improved by incorporating an ant colony algorithm of solutions to form a part of the starting population in the genetic algorithm. The selection process employs an \"elite retention strategy\" to avoid the classical genetic algorithm of the problem of getting trapped in locally optimal solutions. The improved algorithm is applied to test the cloud-network convergence platform, and the optimization-seeking abilities of the classical genetic algorithm, the ant colony genetic algorithm, and the reinforcement learning-based ant colony genetic algorithm are compared and analyzed. The findings reveal that the reinforcement learning-based ant colony genetic algorithm outperforms the other two algorithms by finding the best test case for the test case prioritization problem.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127038983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00289
Doriana Medic, Marco Aldinucci
Designing complex applications and executing them on large-scale topologies of heterogeneous architectures is becoming increasingly crucial in many scientific domains. As a result, diverse workflow modelling paradigms are developed, most of them with no formalisation provided. In these circumstances, comparing two different models or switching from one system to the other becomes a hard nut to crack.This paper investigates the capability of process algebra to model a location aware workflow system. Distributed π-calculus is considered as the base of the formal model due to its ability to describe the communicating components that change their structure as an outcome of the communication. Later, it is discussed how the base model could be extended or modified to capture different features of location aware workflow system.The intention of this paper is to highlight the fact that due to its flexibility, π-calculus, could be a good candidate to represent the behavioural perspective of the workflow system.
{"title":"Towards formal model for location aware workflows","authors":"Doriana Medic, Marco Aldinucci","doi":"10.1109/COMPSAC57700.2023.00289","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00289","url":null,"abstract":"Designing complex applications and executing them on large-scale topologies of heterogeneous architectures is becoming increasingly crucial in many scientific domains. As a result, diverse workflow modelling paradigms are developed, most of them with no formalisation provided. In these circumstances, comparing two different models or switching from one system to the other becomes a hard nut to crack.This paper investigates the capability of process algebra to model a location aware workflow system. Distributed π-calculus is considered as the base of the formal model due to its ability to describe the communicating components that change their structure as an outcome of the communication. Later, it is discussed how the base model could be extended or modified to capture different features of location aware workflow system.The intention of this paper is to highlight the fact that due to its flexibility, π-calculus, could be a good candidate to represent the behavioural perspective of the workflow system.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127199012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00123
Stian Hagbø Olsen, T. OConnor
IoT malware has accompanied the rapid growth of embedded devices over the last decade. Previous work has proposed static and dynamic detection and classification techniques for IoT malware. However, this work requires a diverse and fine-grained set of malware-specific characteristics. This paper presents a longitudinal, diverse, and open-source IoT malware dataset. To demonstrate the depth of the dataset, we propose an approach for recovering symbol tables and detecting the intent of stripped IoT malware binaries using function signature libraries and 14 defining Linux malware features with corresponding regular expressions. We publish a dataset with 65,956 IoT malware binaries detected over 14 years, containing 1006 unique malware threat labels designed for 15 different architectures. Our results indicate that our feature-specific regular expressions can detect the intent of an IoT malware binary. However, further work on function signature matching is needed to recover a feature-revealing symbol table in stripped IoT malware binaries.
{"title":"Toward a Labeled Dataset of IoT Malware Features","authors":"Stian Hagbø Olsen, T. OConnor","doi":"10.1109/COMPSAC57700.2023.00123","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00123","url":null,"abstract":"IoT malware has accompanied the rapid growth of embedded devices over the last decade. Previous work has proposed static and dynamic detection and classification techniques for IoT malware. However, this work requires a diverse and fine-grained set of malware-specific characteristics. This paper presents a longitudinal, diverse, and open-source IoT malware dataset. To demonstrate the depth of the dataset, we propose an approach for recovering symbol tables and detecting the intent of stripped IoT malware binaries using function signature libraries and 14 defining Linux malware features with corresponding regular expressions. We publish a dataset with 65,956 IoT malware binaries detected over 14 years, containing 1006 unique malware threat labels designed for 15 different architectures. Our results indicate that our feature-specific regular expressions can detect the intent of an IoT malware binary. However, further work on function signature matching is needed to recover a feature-revealing symbol table in stripped IoT malware binaries.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127502627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00093
A. Omotosho, Yaman Qendah, Christian Hammer
Yearly, the number of connected Internet of Things (IoT) devices is growing. The attack surface is also increasing because IoT is generally functionality-centric and security is usually an after-thought. Therefore, memory corruption attacks, man-in-the-middle attacks, and distributed denial of service attacks are a few of the attacks that have been widely exploited on these devices communicating via Message Queue Telemetry Transport (MQTT), which is the most commonly used messaging protocol in IoT. However, much of the research on MQTT intrusion detection has either covered a smaller number of attacks, completely ignored memory attacks, or used inadequate classification evaluation metrics (e.g., only accuracy). In this paper, we design and simulate an MQTT IoT network and present IDS-MA, an intrusion detection system for MQTT attacks by training both centralized and federated learning models. Seven different MQTT attacks were implemented with the models evaluated with metrics such as accuracy, precision, and recall. Our evaluation results show high detection scores on MQTT attacks (including memory attacks). We also obtain an average model detection accuracy of over 80% on 2,210,797 real attacks from the MQTT-IoT-IDS2020 benchmark for both centralized and federated models.
{"title":"IDS-MA: Intrusion Detection System for IoT MQTT Attacks Using Centralized and Federated Learning","authors":"A. Omotosho, Yaman Qendah, Christian Hammer","doi":"10.1109/COMPSAC57700.2023.00093","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00093","url":null,"abstract":"Yearly, the number of connected Internet of Things (IoT) devices is growing. The attack surface is also increasing because IoT is generally functionality-centric and security is usually an after-thought. Therefore, memory corruption attacks, man-in-the-middle attacks, and distributed denial of service attacks are a few of the attacks that have been widely exploited on these devices communicating via Message Queue Telemetry Transport (MQTT), which is the most commonly used messaging protocol in IoT. However, much of the research on MQTT intrusion detection has either covered a smaller number of attacks, completely ignored memory attacks, or used inadequate classification evaluation metrics (e.g., only accuracy). In this paper, we design and simulate an MQTT IoT network and present IDS-MA, an intrusion detection system for MQTT attacks by training both centralized and federated learning models. Seven different MQTT attacks were implemented with the models evaluated with metrics such as accuracy, precision, and recall. Our evaluation results show high detection scores on MQTT attacks (including memory attacks). We also obtain an average model detection accuracy of over 80% on 2,210,797 real attacks from the MQTT-IoT-IDS2020 benchmark for both centralized and federated models.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124572349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00087
Samer Y. Khamaiseh, Derek Bagagem, Abdullah Al-Alaj, Mathew Mancino, Hakem Alomari, Ahmed Aleroud
Deep neural networks (DNNs) have achieved a series of significant successes in a wide spectrum of critical domains. For instance, in the field of computer vision, DNNs become the first choice in developing image recognition and classification solutions. However, DNNs have been recently found vulnerable to manipulations of input samples, called adversarial images. The adversarial images can be classified into two categories: untargeted adversarial images which aim to manipulate the output of the DNNs to any incorrect label and targeted adversarial images which force the prediction of the DNNs to a specified target label predefined by the adversary. That being said, the construction of targeted adversarial images requires careful crafting of the targeted perturbations. Different research works have been done to generate targeted adversarial images. However, the majority of them have two limitations: (1) adding large size of perturbations to generate successfully targeted images, and (2) they require extensive computational resources to be utilized in large-scale datasets. This paper introduces Target-X, a novel and fast method for the construction of adversarial targeted images on large-scale datasets that can fool the state-of-the-art image classification neural networks. We evaluate the performance of Target-X using the well-trained image classification neural networks of different architectures and compare it with the well-known T-FGSM and T-UAP targeted attacks. The reported results demonstrate that Target-X can generate targeted adversarial images with the least perturbations on large-scale datasets that can fool the image classification neural networks and significantly outperform the T-FGSM and T-UAP attacks.
{"title":"Target-X: An Efficient Algorithm for Generating Targeted Adversarial Images to Fool Neural Networks","authors":"Samer Y. Khamaiseh, Derek Bagagem, Abdullah Al-Alaj, Mathew Mancino, Hakem Alomari, Ahmed Aleroud","doi":"10.1109/COMPSAC57700.2023.00087","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00087","url":null,"abstract":"Deep neural networks (DNNs) have achieved a series of significant successes in a wide spectrum of critical domains. For instance, in the field of computer vision, DNNs become the first choice in developing image recognition and classification solutions. However, DNNs have been recently found vulnerable to manipulations of input samples, called adversarial images. The adversarial images can be classified into two categories: untargeted adversarial images which aim to manipulate the output of the DNNs to any incorrect label and targeted adversarial images which force the prediction of the DNNs to a specified target label predefined by the adversary. That being said, the construction of targeted adversarial images requires careful crafting of the targeted perturbations. Different research works have been done to generate targeted adversarial images. However, the majority of them have two limitations: (1) adding large size of perturbations to generate successfully targeted images, and (2) they require extensive computational resources to be utilized in large-scale datasets. This paper introduces Target-X, a novel and fast method for the construction of adversarial targeted images on large-scale datasets that can fool the state-of-the-art image classification neural networks. We evaluate the performance of Target-X using the well-trained image classification neural networks of different architectures and compare it with the well-known T-FGSM and T-UAP targeted attacks. The reported results demonstrate that Target-X can generate targeted adversarial images with the least perturbations on large-scale datasets that can fool the image classification neural networks and significantly outperform the T-FGSM and T-UAP attacks.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123768301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00217
Mohammad Yousef Alkhawaldeh, M. Subu, Nabeel Al-Yateem, S. Rahman, F. Ahmed, J. Dias, M. AbuRuz, A. Saifan, Amina Al-Marzouqi, Heba H Hijazi, Mohamad Qasim Alshabi, A. Hossain
Introduction: Telehealth technology and its use are not new to the field of medicine in general and OB in particular. To reduce the potential risks, make telehealth more feasible, and reduce the costs associated with its rapid adoption, it is essential to establish high-quality, evidence-based procedures for OB services. Aims: This qualitative study explored patients’ experience of receiving obstetrics and gynecological treatment via telehealth. Methods: We adopted a qualitative design. We recruited 18 women receiving care at the obstetrics and maternal and fetal medicine clinics at UMass Memorial Medical Center, Massachusetts. Semi-structured interviews were conducted and data was analyzed using qualitative thematic analysis. Results: The participants’ experience of using telehealth services emerged from the data in three themes: the experience of using modern telehealth platforms, telehealth and its perceived benefits, and telehealth and its perceived challenges. Conclusion: The overall positive experiences and consistent perceived benefits reported by most participants suggest that telehealth can be an important tool in the healthcare delivery for certain patients and situations in a post-pandemic world. This study highlighted several challenges that need to be addressed for telehealth to achieve maximum effectiveness and functionality in the future.
{"title":"Telehealth for obstetrics and gynecology outpatinets: Improving patients’ experiences during the COVID-19 pandemic","authors":"Mohammad Yousef Alkhawaldeh, M. Subu, Nabeel Al-Yateem, S. Rahman, F. Ahmed, J. Dias, M. AbuRuz, A. Saifan, Amina Al-Marzouqi, Heba H Hijazi, Mohamad Qasim Alshabi, A. Hossain","doi":"10.1109/COMPSAC57700.2023.00217","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00217","url":null,"abstract":"Introduction: Telehealth technology and its use are not new to the field of medicine in general and OB in particular. To reduce the potential risks, make telehealth more feasible, and reduce the costs associated with its rapid adoption, it is essential to establish high-quality, evidence-based procedures for OB services. Aims: This qualitative study explored patients’ experience of receiving obstetrics and gynecological treatment via telehealth. Methods: We adopted a qualitative design. We recruited 18 women receiving care at the obstetrics and maternal and fetal medicine clinics at UMass Memorial Medical Center, Massachusetts. Semi-structured interviews were conducted and data was analyzed using qualitative thematic analysis. Results: The participants’ experience of using telehealth services emerged from the data in three themes: the experience of using modern telehealth platforms, telehealth and its perceived benefits, and telehealth and its perceived challenges. Conclusion: The overall positive experiences and consistent perceived benefits reported by most participants suggest that telehealth can be an important tool in the healthcare delivery for certain patients and situations in a post-pandemic world. This study highlighted several challenges that need to be addressed for telehealth to achieve maximum effectiveness and functionality in the future.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121678788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/COMPSAC57700.2023.00089
Mordechai Guri
This paper presents AirKeyLogger - a novel radio frequency (RF) keylogging attack for air-gapped computers.Our keylogger exploits radio emissions from a computer’s power supply to exfiltrate real-time keystroke data to a remote attacker. Unlike hardware keylogging devices, our attack does not require physical hardware. Instead, it can be conducted via a software supply-chain attack and is solely based on software manipulations. Malware on a sensitive, air-gap computer can intercept keystroke logging by using global hooking techniques or injecting malicious code into a running process. To leak confidential data, the processor’s working frequencies are manipulated to generate a pattern of electromagnetic emissions from the power unit modulated by keystrokes. The keystroke information can be received at distances of several meters away via an RF receiver or a smartphone with a simple antenna. We provide related work, discuss keylogging methods and present multi-key modulation techniques. We evaluate our method at various typing speeds and on-screen keyboards as well. We show the design and implementation of transmitter and receiver components and present evaluation findings. Our tests show that malware can eavesdrop on keylogging data in real-time over radio signals several meters away and behind concrete walls from highly secure and air-gapped systems.
{"title":"AirKeyLogger: Hardwareless Air-Gap Keylogging Attack","authors":"Mordechai Guri","doi":"10.1109/COMPSAC57700.2023.00089","DOIUrl":"https://doi.org/10.1109/COMPSAC57700.2023.00089","url":null,"abstract":"This paper presents AirKeyLogger - a novel radio frequency (RF) keylogging attack for air-gapped computers.Our keylogger exploits radio emissions from a computer’s power supply to exfiltrate real-time keystroke data to a remote attacker. Unlike hardware keylogging devices, our attack does not require physical hardware. Instead, it can be conducted via a software supply-chain attack and is solely based on software manipulations. Malware on a sensitive, air-gap computer can intercept keystroke logging by using global hooking techniques or injecting malicious code into a running process. To leak confidential data, the processor’s working frequencies are manipulated to generate a pattern of electromagnetic emissions from the power unit modulated by keystrokes. The keystroke information can be received at distances of several meters away via an RF receiver or a smartphone with a simple antenna. We provide related work, discuss keylogging methods and present multi-key modulation techniques. We evaluate our method at various typing speeds and on-screen keyboards as well. We show the design and implementation of transmitter and receiver components and present evaluation findings. Our tests show that malware can eavesdrop on keylogging data in real-time over radio signals several meters away and behind concrete walls from highly secure and air-gapped systems.","PeriodicalId":296288,"journal":{"name":"2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121545019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：