2024
1.
Al-Thaedan, Abbas; Shakir, Zaenab; Mjhool, Ahmed Yaseen; Alsabah, Ruaa; Al-Sabbagh, Ali; Nembhard, Fitzroy; Salah, Monera
A machine learning framework for predicting downlink throughput in 4G-LTE/5G cellular networks Journal Article
In: International Journal of Information Technology, vol. 16, pp. 651–657, 2024, ISSN: 2511-2112.
Abstract | Links | BibTeX | Tags: cellular networks, machine learning
@article{nokey,
title = {A machine learning framework for predicting downlink throughput in 4G-LTE/5G cellular networks},
author = {Abbas Al-Thaedan and Zaenab Shakir and Ahmed Yaseen Mjhool and Ruaa Alsabah and Ali Al-Sabbagh and Fitzroy Nembhard and Monera Salah },
editor = {M. N. Hoda},
doi = {https://doi.org/10.1007/s41870-023-01678-w},
issn = {2511-2112},
year = {2024},
date = {2024-01-02},
urldate = {2024-01-02},
journal = {International Journal of Information Technology},
volume = {16},
pages = {651–657},
abstract = {The current and next generations of cellular networks produce a massive amount of data. With this vast parameter increase, cellular communication networks have grown incredibly complicated. In addition, these cellular networks are unmanaged with conventional techniques, and a more advanced design and optimization methodology that depends on Machine Learning (ML) models is necessary. This work proposes a framework model for predicting downlink throughput (DL-Throughput) using ML models in fourth and fifth generations (4G/5G) cellular networks. The important parameters are selected from data measurements based on the correlation coefficient. The critical and effective parameters such as Reference Signal Received Power (RSRP), Signal to Interference and Noise Ratio(SINR), Received Signal Strength Indicator (RSSI), and Reference Signal Receive Quality (RSRQ) have been applied for the training model to predict the DL-Throughput in cellular networks. The prediction accuracy of the determination coefficient ranges between 89% and 96% from three different operators.},
keywords = {cellular networks, machine learning},
pubstate = {published},
tppubtype = {article}
}
The current and next generations of cellular networks produce a massive amount of data. With this vast parameter increase, cellular communication networks have grown incredibly complicated. In addition, these cellular networks are unmanaged with conventional techniques, and a more advanced design and optimization methodology that depends on Machine Learning (ML) models is necessary. This work proposes a framework model for predicting downlink throughput (DL-Throughput) using ML models in fourth and fifth generations (4G/5G) cellular networks. The important parameters are selected from data measurements based on the correlation coefficient. The critical and effective parameters such as Reference Signal Received Power (RSRP), Signal to Interference and Noise Ratio(SINR), Received Signal Strength Indicator (RSSI), and Reference Signal Receive Quality (RSRQ) have been applied for the training model to predict the DL-Throughput in cellular networks. The prediction accuracy of the determination coefficient ranges between 89% and 96% from three different operators.
2023
2.
Nembhard, Fitzroy D; Slhoub, Khaled A; Carvalho, Marco M
An Agent-Based Approach Toward Smart Software Testing Conference
Proceedings of the Future Technologies Conference, vol. 814, Lecture Notes in Networks and Systems book series (LNNS) Springer Nature Switzerland, 2023, ISBN: 978-3-031-47451-4.
Abstract | Links | BibTeX | Tags: AOSE, NLP, software testing, virtual assistant
@conference{nokey,
title = {An Agent-Based Approach Toward Smart Software Testing},
author = {Fitzroy D Nembhard and Khaled A Slhoub and Marco M Carvalho},
editor = {K. Arai},
doi = {https://doi.org/10.1007/978-3-031-47451-4_21},
isbn = {978-3-031-47451-4},
year = {2023},
date = {2023-11-01},
urldate = {2023-11-01},
booktitle = {Proceedings of the Future Technologies Conference},
volume = {814},
publisher = {Springer Nature Switzerland},
series = { Lecture Notes in Networks and Systems book series (LNNS)},
abstract = {As the field of software testing continues to advance, cooperative software testing and analysis has been proposed as a new methodology to help combat the challenges involved with performing highly effective software testing. Motivated by the understanding that auto-testing systems on their own are not powerful enough to address complications in testing complex real-world software, this model involves human-machine and machine-machine cooperation to make automated software testing processes more interactive and user-friendly. It is with this in mind that we propose an agent-based approach to software testing that involves teaming humans with virtual assistants on smart devices to help coordinate the tasks associated with testing complex real-world software. Currently, virtual assistants are widely used for interpersonal tasks such as purchasing items from restaurants, interfacing with mobile applications to create events and reminders, and composing and sending messages on behalf of the user. In this research, we create an agent-based framework and use it to demonstrate that a virtual assistant on a smart device can also be utilized to work closely with software testers to efficiently and effectively verify software functionality, generate reports, and communicate results with developers. We utilize unit testing to evaluate our proposed methodology by applying it to a set of Java projects. Our results show that virtual agents can be used to work with humans to coordinate tasks associated with unit-testing software.},
keywords = {AOSE, NLP, software testing, virtual assistant},
pubstate = {published},
tppubtype = {conference}
}
As the field of software testing continues to advance, cooperative software testing and analysis has been proposed as a new methodology to help combat the challenges involved with performing highly effective software testing. Motivated by the understanding that auto-testing systems on their own are not powerful enough to address complications in testing complex real-world software, this model involves human-machine and machine-machine cooperation to make automated software testing processes more interactive and user-friendly. It is with this in mind that we propose an agent-based approach to software testing that involves teaming humans with virtual assistants on smart devices to help coordinate the tasks associated with testing complex real-world software. Currently, virtual assistants are widely used for interpersonal tasks such as purchasing items from restaurants, interfacing with mobile applications to create events and reminders, and composing and sending messages on behalf of the user. In this research, we create an agent-based framework and use it to demonstrate that a virtual assistant on a smart device can also be utilized to work closely with software testers to efficiently and effectively verify software functionality, generate reports, and communicate results with developers. We utilize unit testing to evaluate our proposed methodology by applying it to a set of Java projects. Our results show that virtual agents can be used to work with humans to coordinate tasks associated with unit-testing software.
3.
Fitzroy D Nembhard, Marco M Carvalho
Teaming humans with virtual assistants to detect and mitigate vulnerabilities Book Chapter
In: Arai, K. (Ed.): vol. 711, Chapter Lecture Notes in Networks and Systems, pp. 565-576, Springer Nature Switzerland, 2023.
Abstract | Links | BibTeX | Tags: Human-machine teaming, virtual assistant, vulnerability detection, vulnerability mitigation
@inbook{nokey,
title = {Teaming humans with virtual assistants to detect and mitigate vulnerabilities},
author = {Fitzroy D Nembhard, Marco M Carvalho},
editor = {Arai, K.},
url = {978-3-031-37717-4},
doi = {https://doi.org/10.1007/978-3-031-37717-4_35},
year = {2023},
date = {2023-07-13},
urldate = {2023-07-13},
volume = {711},
pages = {565-576},
publisher = {Springer Nature Switzerland},
chapter = {Lecture Notes in Networks and Systems},
series = {SAI 2023},
abstract = {The use of virtual assistants has grown significantly in recent years. This growth can be attributed to the prevalence of mobile devices and advances in the Internet of Things (IoT). While virtual assistants are widely used for interpersonal and social purposes such as ordering items from restaurants, creating reminders, and communicating with peers, their use is limited in cybersecurity and other computational sciences. In this research, we develop a framework that teams humans with virtual assistants on mobile devices in an effort to encourage the use of vulnerability detectors to mitigate errors in software and their underlying networks and systems. Creating effective cyber defenses involves teaming humans with machines in a way that enables secure orchestration, real-time communication, and unity of action. We demonstrate that a seamless coordination between human and AI can help minimize the number of errors in software systems, which will ultimately reduce data breaches and other cyber-related challenges plaguing our world.},
keywords = {Human-machine teaming, virtual assistant, vulnerability detection, vulnerability mitigation},
pubstate = {published},
tppubtype = {inbook}
}
The use of virtual assistants has grown significantly in recent years. This growth can be attributed to the prevalence of mobile devices and advances in the Internet of Things (IoT). While virtual assistants are widely used for interpersonal and social purposes such as ordering items from restaurants, creating reminders, and communicating with peers, their use is limited in cybersecurity and other computational sciences. In this research, we develop a framework that teams humans with virtual assistants on mobile devices in an effort to encourage the use of vulnerability detectors to mitigate errors in software and their underlying networks and systems. Creating effective cyber defenses involves teaming humans with machines in a way that enables secure orchestration, real-time communication, and unity of action. We demonstrate that a seamless coordination between human and AI can help minimize the number of errors in software systems, which will ultimately reduce data breaches and other cyber-related challenges plaguing our world.
2021
4.
Nembhard, Fitzroy D.; Carvalho, Marco M.
A Smart and Defensive Human-Machine Approach to Code Analysis Proceedings Article
In: First International Workshop on Artificial Intelligence, IJCAI-ACD 2021, ijcai.org, 2021.
Abstract | BibTeX | Tags: agent, Google Assistant, NLP, virtual assistant, voice assistant, vulnerability detection
@inproceedings{IJCAINembhardCarvalho21,
title = {A Smart and Defensive Human-Machine Approach to Code Analysis},
author = {Fitzroy D. Nembhard and Marco M. Carvalho},
year = {2021},
date = {2021-08-20},
urldate = {2021-08-20},
booktitle = {First International Workshop on
Artificial Intelligence, IJCAI-ACD 2021},
publisher = {ijcai.org},
abstract = {Static analysis remains one of the most popular approaches for detecting and correcting poor or vulnerable program code. It involves the examination of code listings, test results, or other documentation to identify errors, violations of development standards, or other problems, with the ultimate goal of fixing these errors so that systems and software are as secure as possible. There exists a plethora of static analysis tools, which makes it challenging for businesses and programmers to select a tool to analyze their program code. It is imperative to find ways to improve code analysis so that it can be employed by cyber defenders to mitigate security risks. In this research, we propose a method that employs the use of virtual assistants to work with programmers to ensure that software are as safe as possible in order to protect safety-critical systems from data breaches and other attacks. The proposed method employs a recommender system that uses various metrics to help programmers select the most appropriate code analysis tool for their project and guides them through the analysis process. The system further tracks the user's behavior regarding the adoption of the recommended practices.},
keywords = {agent, Google Assistant, NLP, virtual assistant, voice assistant, vulnerability detection},
pubstate = {published},
tppubtype = {inproceedings}
}
Static analysis remains one of the most popular approaches for detecting and correcting poor or vulnerable program code. It involves the examination of code listings, test results, or other documentation to identify errors, violations of development standards, or other problems, with the ultimate goal of fixing these errors so that systems and software are as secure as possible. There exists a plethora of static analysis tools, which makes it challenging for businesses and programmers to select a tool to analyze their program code. It is imperative to find ways to improve code analysis so that it can be employed by cyber defenders to mitigate security risks. In this research, we propose a method that employs the use of virtual assistants to work with programmers to ensure that software are as safe as possible in order to protect safety-critical systems from data breaches and other attacks. The proposed method employs a recommender system that uses various metrics to help programmers select the most appropriate code analysis tool for their project and guides them through the analysis process. The system further tracks the user's behavior regarding the adoption of the recommended practices.
5.
Nembhard, Fitzroy D.; Carvalho, Marco M.
Conversational Code Analysis: The Future of Secure Coding Journal Article
In: IntechOpen, London, 2021.
Abstract | Links | BibTeX | Tags: Google Assistant, NLP, software security, virtual assistant, voice assistant, vulnerability detection
@article{nembhard2021conversational,
title = {Conversational Code Analysis: The Future of Secure Coding},
author = {Fitzroy D. Nembhard and Marco M. Carvalho},
doi = {10.5772/intechopen.98362},
year = {2021},
date = {2021-06-08},
urldate = {2021-06-08},
journal = {IntechOpen, London},
abstract = {The area of software development and secure coding can benefit significantly from advancements in virtual assistants. Research has shown that many coders neglect security in favor of meeting deadlines. This shortcoming leaves systems vulnerable to attackers. While a plethora of tools are available for programmers to scan their code for vulnerabilities, finding the right tool can be challenging. It is therefore imperative to adopt measures to get programmers to utilize code analysis tools that will help them produce more secure code. This chapter looks at the limitations of existing approaches to secure coding and proposes a methodology that allows programmers to scan and fix vulnerabilities in program code by communicating with virtual assistants on their smart devices. With the ubiquitous move towards virtual assistants, it is important to design systems that are more reliant on voice than on standard point-and-click and keyboard-driven approaches. Consequently, we propose MyCodeAnalyzer, a Google Assistant app and code analysis framework, which was designed to interactively scan program code for vulnerabilities and flaws using voice commands during development. We describe the proposed methodology, implement a prototype, test it on a vulnerable project and present our results.},
keywords = {Google Assistant, NLP, software security, virtual assistant, voice assistant, vulnerability detection},
pubstate = {published},
tppubtype = {article}
}
The area of software development and secure coding can benefit significantly from advancements in virtual assistants. Research has shown that many coders neglect security in favor of meeting deadlines. This shortcoming leaves systems vulnerable to attackers. While a plethora of tools are available for programmers to scan their code for vulnerabilities, finding the right tool can be challenging. It is therefore imperative to adopt measures to get programmers to utilize code analysis tools that will help them produce more secure code. This chapter looks at the limitations of existing approaches to secure coding and proposes a methodology that allows programmers to scan and fix vulnerabilities in program code by communicating with virtual assistants on their smart devices. With the ubiquitous move towards virtual assistants, it is important to design systems that are more reliant on voice than on standard point-and-click and keyboard-driven approaches. Consequently, we propose MyCodeAnalyzer, a Google Assistant app and code analysis framework, which was designed to interactively scan program code for vulnerabilities and flaws using voice commands during development. We describe the proposed methodology, implement a prototype, test it on a vulnerable project and present our results.
6.
Al-Thaedan, Abbas; Carvalho, Marco; Nembhard, Fitzroy
A Fast and Exact Motif Enumeration Algorithm for Dynamic Networks Proceedings Article
In: Arai, Kohei (Ed.): Advances in Information and Communication, pp. 123–141, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-73103-8.
Abstract | BibTeX | Tags: algorithms, bioinformatics, databases, dynamic networks, graphs, motifs, nauty, networks, protein interaction, transcription networks
@inproceedings{MotifEnumeration,
title = {A Fast and Exact Motif Enumeration Algorithm for Dynamic Networks},
author = {Abbas Al-Thaedan and Marco Carvalho and Fitzroy Nembhard},
editor = {Kohei Arai},
isbn = {978-3-030-73103-8},
year = {2021},
date = {2021-04-16},
urldate = {2021-04-16},
booktitle = {Advances in Information and Communication},
pages = {123--141},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Network motifs have been widely used in the analysis of various biological networks, including protein-protein interaction (PPI) networks, transcription regulation networks (TRNs), and metabolic networks. Counting network motif involves expensive enumeration of sub-patterns and graph isomorphism. Many protein-protein interaction databases are readily available online and are constantly updated over a period of time by either adding or removing proteins and the respective interactions between them. There exists many motif enumeration algorithms that can be applied to protein-protein interaction databases.
However, most existing algorithms run enumeration over the entire network every time a change is made to the network. This is not only computationally expensive but also highly inefficient as the size of the network and motif increases. In this work, we propose an exact and efficient algorithm for enumerating network motifs by utilizing only updated edges and vertices in a network. We demonstrate how our algorithm can quickly and robustly update the frequency of different sizes of network motifs in
dynamic networks. Experimental results show that our approach is successful in reducing the computational time by eliminating overlapped subgraphs. Runtime monitoring of network motif distributions is very
important in numerous practical domains where large networks are subject to localized changes, which is efficiently provided by the proposed algorithm.},
keywords = {algorithms, bioinformatics, databases, dynamic networks, graphs, motifs, nauty, networks, protein interaction, transcription networks},
pubstate = {published},
tppubtype = {inproceedings}
}
Network motifs have been widely used in the analysis of various biological networks, including protein-protein interaction (PPI) networks, transcription regulation networks (TRNs), and metabolic networks. Counting network motif involves expensive enumeration of sub-patterns and graph isomorphism. Many protein-protein interaction databases are readily available online and are constantly updated over a period of time by either adding or removing proteins and the respective interactions between them. There exists many motif enumeration algorithms that can be applied to protein-protein interaction databases.
However, most existing algorithms run enumeration over the entire network every time a change is made to the network. This is not only computationally expensive but also highly inefficient as the size of the network and motif increases. In this work, we propose an exact and efficient algorithm for enumerating network motifs by utilizing only updated edges and vertices in a network. We demonstrate how our algorithm can quickly and robustly update the frequency of different sizes of network motifs in
dynamic networks. Experimental results show that our approach is successful in reducing the computational time by eliminating overlapped subgraphs. Runtime monitoring of network motif distributions is very
important in numerous practical domains where large networks are subject to localized changes, which is efficiently provided by the proposed algorithm.
However, most existing algorithms run enumeration over the entire network every time a change is made to the network. This is not only computationally expensive but also highly inefficient as the size of the network and motif increases. In this work, we propose an exact and efficient algorithm for enumerating network motifs by utilizing only updated edges and vertices in a network. We demonstrate how our algorithm can quickly and robustly update the frequency of different sizes of network motifs in
dynamic networks. Experimental results show that our approach is successful in reducing the computational time by eliminating overlapped subgraphs. Runtime monitoring of network motif distributions is very
important in numerous practical domains where large networks are subject to localized changes, which is efficiently provided by the proposed algorithm.
2019
7.
Nembhard, Fitzroy D.; Carvalho, Marco M.; Eskridge, Thomas C.
Towards the Application of Recommender Systems to Secure Coding Journal Article
In: EURASIP Journal on Information Security, vol. 2019, no. 1, pp. 9, 2019, ISBN: 2510-523X.
Abstract | Links | BibTeX | Tags: ab testing, bugs, code security, intellisense, java, minhash, recommender systems, simhash, software quality, user study, vulnerability detection
@article{nembhard_recommender_journal,
title = {Towards the Application of Recommender Systems to Secure Coding},
author = {Fitzroy D. Nembhard and Marco M. Carvalho and Thomas C. Eskridge},
url = {https://doi.org/10.1186/s13635-019-0092-4},
doi = {10.1186/s13635-019-0092-4},
isbn = {2510-523X},
year = {2019},
date = {2019-06-13},
urldate = {2019-06-13},
journal = {EURASIP Journal on Information Security},
volume = {2019},
number = {1},
pages = {9},
abstract = {Secure coding is crucial for the design of secure and efficient software and computing systems. However, many programmers avoid secure coding practices for a variety of reasons. Some of these reasons are lack of knowledge of secure coding standards, negligence, and poor performance of and usability issues with existing code analysis tools. Therefore, it is essential to create tools that address these issues and concerns. This article features the proposal, development, and evaluation of a recommender system that uses text mining techniques, coupled with IntelliSense technology, to recommend fixes for potential vulnerabilities in program code. The resulting system mines a large code base of over 1.6 million Java files using the MapReduce methodology, creating a knowledge base for a recommender system that provides fixes for taint-style vulnerabilities. Formative testing and a usability study determined that surveyed participants strongly believed that a recommender system would help programmers write more secure code.},
keywords = {ab testing, bugs, code security, intellisense, java, minhash, recommender systems, simhash, software quality, user study, vulnerability detection},
pubstate = {published},
tppubtype = {article}
}
Secure coding is crucial for the design of secure and efficient software and computing systems. However, many programmers avoid secure coding practices for a variety of reasons. Some of these reasons are lack of knowledge of secure coding standards, negligence, and poor performance of and usability issues with existing code analysis tools. Therefore, it is essential to create tools that address these issues and concerns. This article features the proposal, development, and evaluation of a recommender system that uses text mining techniques, coupled with IntelliSense technology, to recommend fixes for potential vulnerabilities in program code. The resulting system mines a large code base of over 1.6 million Java files using the MapReduce methodology, creating a knowledge base for a recommender system that provides fixes for taint-style vulnerabilities. Formative testing and a usability study determined that surveyed participants strongly believed that a recommender system would help programmers write more secure code.
8.
Nembhard, Fitzroy; Carvalho, Marco
The Impact of Interface Design on the Usability of Code Analyzers Proceedings Article
In: 2019 SoutheastCon, pp. 1-6, 2019.
Links | BibTeX | Tags: ab testing, code analysis, code security, ui design, user study, vulnerability detection
@inproceedings{nembhard2019_analyzer_usability,
title = {The Impact of Interface Design on the Usability of Code Analyzers},
author = {Fitzroy Nembhard and Marco Carvalho},
doi = {10.1109/SoutheastCon42311.2019.9020339},
year = {2019},
date = {2019-04-11},
urldate = {2019-04-11},
booktitle = {2019 SoutheastCon},
pages = {1-6},
keywords = {ab testing, code analysis, code security, ui design, user study, vulnerability detection},
pubstate = {published},
tppubtype = {inproceedings}
}
9.
Slhoub, Khaled; Nembhard, Fitzroy; Carvalho, Marco
A Metrics Tracking Program for Promoting High-Quality Software Development Proceedings Article
In: 2019 SoutheastCon, pp. 1-8, 2019.
Abstract | Links | BibTeX | Tags: coding standards, defect density, eclipse, Goal-Questions-Metrics, GQM, java, logging standards, maintainability, plugin, Qualitative Risk Ranking Matrix, software engineering, software quality, software requirements, teamwork
@inproceedings{metricsTracking,
title = {A Metrics Tracking Program for Promoting High-Quality Software Development},
author = {Khaled Slhoub and Fitzroy Nembhard and Marco Carvalho},
doi = {10.1109/SoutheastCon42311.2019.9020395},
year = {2019},
date = {2019-04-11},
urldate = {2019-04-11},
booktitle = {2019 SoutheastCon},
pages = {1-8},
abstract = {There has been substantial focus on software metrics over the last few decades. However, many activities within software engineering are often qualitative and are not consonant with automated approaches. Consequently, there are few tools to measure software development quality or to assess teamwork contribution. This paper uses ideas from the Goal-Questions-Metrics (GQM) paradigm to propose a set of metrics to track product and process quality throughout the software development process. The proposed metrics program consists of a set of quality metrics and associated standards that will encourage software development teams to produce high-quality products. We also propose a framework for a tool that implements the metrics tracking program and demonstrate its utility by developing an Eclipse plugin based on the proposed quality metrics.},
keywords = {coding standards, defect density, eclipse, Goal-Questions-Metrics, GQM, java, logging standards, maintainability, plugin, Qualitative Risk Ranking Matrix, software engineering, software quality, software requirements, teamwork},
pubstate = {published},
tppubtype = {inproceedings}
}
There has been substantial focus on software metrics over the last few decades. However, many activities within software engineering are often qualitative and are not consonant with automated approaches. Consequently, there are few tools to measure software development quality or to assess teamwork contribution. This paper uses ideas from the Goal-Questions-Metrics (GQM) paradigm to propose a set of metrics to track product and process quality throughout the software development process. The proposed metrics program consists of a set of quality metrics and associated standards that will encourage software development teams to produce high-quality products. We also propose a framework for a tool that implements the metrics tracking program and demonstrate its utility by developing an Eclipse plugin based on the proposed quality metrics.
10.
Slhoub, Khaled; Carvalho, Marco; Nembhard, Fitzroy
Evaluation and Comparison of Agent-Oriented Methodologies: A Software Engineering Viewpoint Proceedings Article
In: 2019 IEEE International Systems Conference (SysCon), pp. 1-8, 2019.
Abstract | Links | BibTeX | Tags: agent, AOSE, MaSE, PASSI, Prometheus, software engineering, software quality, software requirements, standards, SWEBOK
@inproceedings{AOSEEvaluation,
title = {Evaluation and Comparison of Agent-Oriented Methodologies: A Software Engineering Viewpoint},
author = {Khaled Slhoub and Marco Carvalho and Fitzroy Nembhard},
doi = {10.1109/SYSCON.2019.8836962},
year = {2019},
date = {2019-04-08},
urldate = {2019-04-08},
booktitle = {2019 IEEE International Systems Conference (SysCon)},
pages = {1-8},
abstract = {Numerous agent-oriented methodologies that offer a rich pool of resources to support developers of agent-based systems have been proposed. However, the use of existing methodologies in industrial settings is still limited due to the large volume of methodologies, diversity of covered scopes, ambiguity in concepts, and lack of maturity. This makes it difficult for agent technology practitioners to choose the appropriate methodology that best fits their given development context. To eliminate such agent-based development bottleneck, it is important to introduce suitable methods for evaluating, comparing, and classifying agent-oriented methodologies in order to leverage their usage among practitioners. Having systems to evaluate methodologies can effectively help developers better understand existing methodologies, realize their benefits, outline their pros and cons, and assist practitioners with selecting the best-fit methodology for a specific agent-based project. In response, this paper proposes a novel criteria-based evaluation that is influenced by software engineering practices to assess and compare agent-oriented methodologies. The proposed evaluation is derived from the software engineering body of knowledge (SWEBOK) and provides a simplified method to assess the coverage degree of an agent-oriented methodology with respect to major software knowledge areas such as the requirements and testing phases. We demonstrate the applicability of the proposed evaluation by applying it to three agent-oriented methodologies (PASSI, MaSE, and Prometheus) in the software engineering requirements and testing phases.},
keywords = {agent, AOSE, MaSE, PASSI, Prometheus, software engineering, software quality, software requirements, standards, SWEBOK},
pubstate = {published},
tppubtype = {inproceedings}
}
Numerous agent-oriented methodologies that offer a rich pool of resources to support developers of agent-based systems have been proposed. However, the use of existing methodologies in industrial settings is still limited due to the large volume of methodologies, diversity of covered scopes, ambiguity in concepts, and lack of maturity. This makes it difficult for agent technology practitioners to choose the appropriate methodology that best fits their given development context. To eliminate such agent-based development bottleneck, it is important to introduce suitable methods for evaluating, comparing, and classifying agent-oriented methodologies in order to leverage their usage among practitioners. Having systems to evaluate methodologies can effectively help developers better understand existing methodologies, realize their benefits, outline their pros and cons, and assist practitioners with selecting the best-fit methodology for a specific agent-based project. In response, this paper proposes a novel criteria-based evaluation that is influenced by software engineering practices to assess and compare agent-oriented methodologies. The proposed evaluation is derived from the software engineering body of knowledge (SWEBOK) and provides a simplified method to assess the coverage degree of an agent-oriented methodology with respect to major software knowledge areas such as the requirements and testing phases. We demonstrate the applicability of the proposed evaluation by applying it to three agent-oriented methodologies (PASSI, MaSE, and Prometheus) in the software engineering requirements and testing phases.
11.
Nembhard, Fitzroy; Carvalho, Marco M; Tegos, Kleanthis Zisis
The Deployment of RFID Technology on Small Farms in Holopaw, FL: A Community-Centered Effort Proceedings Article
In: 2019 IEEE International Conference on RFID (RFID) (IEEE RFID 2019), Phoenix, USA, 2019.
Abstract | BibTeX | Tags: cyber physical systems, farming, food, IOT, pasture design, RFID, sensors, smart communities, sustainability, UHF
@inproceedings{RFIDDeployment,
title = {The Deployment of RFID Technology on Small Farms in Holopaw, FL: A Community-Centered Effort},
author = {Fitzroy Nembhard and Marco M Carvalho and Kleanthis Zisis Tegos},
year = {2019},
date = {2019-04-02},
urldate = {2019-04-02},
booktitle = {2019 IEEE International Conference on RFID (RFID) (IEEE RFID 2019)},
address = {Phoenix, USA},
abstract = {This paper discusses a solution that deploys passive UHF RFID technology and wireless sensors on small farms to provide intelligence that will help mitigate some disparate socioeconomic conditions in farming communities. Starting with a collaborative effort with farmers in the community of Holopaw, Florida, in the United States, we first propose experiments to observe the feeding patterns of livestock on farms of less than 150 acres in order to improve pasture design. We hypothesize that intelligent pasture design will result in improved utilization, which has far-reaching implications such as improved growth and health of livestock, profitability, and ultimately mitigation of socioeconomic conditions of residents in a given city.},
keywords = {cyber physical systems, farming, food, IOT, pasture design, RFID, sensors, smart communities, sustainability, UHF},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper discusses a solution that deploys passive UHF RFID technology and wireless sensors on small farms to provide intelligence that will help mitigate some disparate socioeconomic conditions in farming communities. Starting with a collaborative effort with farmers in the community of Holopaw, Florida, in the United States, we first propose experiments to observe the feeding patterns of livestock on farms of less than 150 acres in order to improve pasture design. We hypothesize that intelligent pasture design will result in improved utilization, which has far-reaching implications such as improved growth and health of livestock, profitability, and ultimately mitigation of socioeconomic conditions of residents in a given city.
2018
12.
Nembhard, Fitzroy; Carvalho, Marco; Eskridge, Thomas
Extracting Knowledge from Open Source Projects to Improve Program Security Proceedings Article
In: SoutheastCon 2018, pp. 1-7, 2018.
Abstract | Links | BibTeX | Tags: code repositories, code security, data mining, knowledge extraction, software security, SQL injection, SQLI, text mining
@inproceedings{ExtractingKnowledge,
title = {Extracting Knowledge from Open Source Projects to Improve Program Security},
author = {Fitzroy Nembhard and Marco Carvalho and Thomas Eskridge},
doi = {10.1109/SECON.2018.8478906},
year = {2018},
date = {2018-04-19},
urldate = {2018-04-19},
booktitle = {SoutheastCon 2018},
pages = {1-7},
abstract = {Open source repositories contain a wealth of unstructured and unlabeled data from which useful knowledge can be extracted. This knowledge can be applied in a wide range of applications such as discovering how programmers improve their programs over time and finding patterns to detect and mitigate vulnerabilities. In this work, we propose to use text mining and machine learning to extract knowledge from open source code in order to categorize and structure source code. By mining a subset (over 600,000 Java files) of a 2011 dataset that contains over 70,000 open source projects, we present a case study showing that useful patterns can be extracted from source code and that these patterns can be used to create a recommender system to help programmers avoid unsafe practices. We demonstrate the utility of our proposed techniques by applying them to the detection of SOL Injection.},
keywords = {code repositories, code security, data mining, knowledge extraction, software security, SQL injection, SQLI, text mining},
pubstate = {published},
tppubtype = {inproceedings}
}
Open source repositories contain a wealth of unstructured and unlabeled data from which useful knowledge can be extracted. This knowledge can be applied in a wide range of applications such as discovering how programmers improve their programs over time and finding patterns to detect and mitigate vulnerabilities. In this work, we propose to use text mining and machine learning to extract knowledge from open source code in order to categorize and structure source code. By mining a subset (over 600,000 Java files) of a 2011 dataset that contains over 70,000 open source projects, we present a case study showing that useful patterns can be extracted from source code and that these patterns can be used to create a recommender system to help programmers avoid unsafe practices. We demonstrate the utility of our proposed techniques by applying them to the detection of SOL Injection.
2017
13.
Nembhard, Fitzroy; Carvalho, Marco; Eskridge, Thomas
A Hybrid Approach to Improving Program Security Proceedings Article
In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017.
Abstract | BibTeX | Tags: code security, cybersecurity, recommender systems, topic modeling, vulnerability detection, vulnerability mitigation
@inproceedings{nembhard_hybrid_2017,
title = {A Hybrid Approach to Improving Program Security},
author = {Fitzroy Nembhard and Marco Carvalho and Thomas Eskridge},
year = {2017},
date = {2017-11-27},
urldate = {2017-11-27},
booktitle = {2017 IEEE Symposium Series on Computational Intelligence (SSCI)},
abstract = {The security of computer programs and systems is a very critical issue. With the number of attacks launched on computer networks and software, businesses and IT professionals are taking steps to ensure that their information systems are as secure as possible. However, many programmers do not think about adding security to their programs until their projects are near completion. This is a major mistake because a system is as secure as its weakest link. If security is viewed as an afterthought, it is highly likely that the resulting system will have a large number of vulnerabilities, which could be exploited by attackers. One of the reasons programmers overlook adding security to their code is because it is viewed as a complicated or time-consuming process. This paper presents a tool that will help programmers think more about security and add security tactics to their code with ease. We created a model that learns from existing open source projects and documentation using machine learning and text mining techniques. Our tool contains a module that runs in the background to analyze code as the programmer types and offers suggestions of where security could be included. In addition, our tool fetches existing open source implementations of cryptographic algorithms and sample code from repositories to aid programmers in adding security easily to their projects.},
keywords = {code security, cybersecurity, recommender systems, topic modeling, vulnerability detection, vulnerability mitigation},
pubstate = {published},
tppubtype = {inproceedings}
}
The security of computer programs and systems is a very critical issue. With the number of attacks launched on computer networks and software, businesses and IT professionals are taking steps to ensure that their information systems are as secure as possible. However, many programmers do not think about adding security to their programs until their projects are near completion. This is a major mistake because a system is as secure as its weakest link. If security is viewed as an afterthought, it is highly likely that the resulting system will have a large number of vulnerabilities, which could be exploited by attackers. One of the reasons programmers overlook adding security to their code is because it is viewed as a complicated or time-consuming process. This paper presents a tool that will help programmers think more about security and add security tactics to their code with ease. We created a model that learns from existing open source projects and documentation using machine learning and text mining techniques. Our tool contains a module that runs in the background to analyze code as the programmer types and offers suggestions of where security could be included. In addition, our tool fetches existing open source implementations of cryptographic algorithms and sample code from repositories to aid programmers in adding security easily to their projects.
2015
14.
Eskridge, Thomas C.; Carvalho, Marco; Nembhard, Fitzroy; Thotempudi, Hari; Polack, Peter J.
Interactive Visualization of Netflow Traffic Proceedings Article
In: 2015 European Intelligence and Security Informatics Conference, pp. 188-188, 2015.
Abstract | Links | BibTeX | Tags: cybersecurity, netflows, network security, networks, Parallel Coordinate Planes, visualization
@inproceedings{Netflows,
title = {Interactive Visualization of Netflow Traffic},
author = {Thomas C. Eskridge and Marco Carvalho and Fitzroy Nembhard and Hari Thotempudi and Peter J. Polack},
doi = {10.1109/EISIC.2015.51},
year = {2015},
date = {2015-09-07},
urldate = {2015-09-07},
booktitle = {2015 European Intelligence and Security Informatics Conference},
pages = {188-188},
abstract = {We introduce a novel tool for visualizing netflow traffic on enterprise networks called 3D Parallel Coordinate Planes (3DPCP). This tool provides operators with the ability to manipulate the visual flow of network traffic information by arranging two-dimensional planes along a vertical time axis in 3D space.},
keywords = {cybersecurity, netflows, network security, networks, Parallel Coordinate Planes, visualization},
pubstate = {published},
tppubtype = {inproceedings}
}
We introduce a novel tool for visualizing netflow traffic on enterprise networks called 3D Parallel Coordinate Planes (3DPCP). This tool provides operators with the ability to manipulate the visual flow of network traffic information by arranging two-dimensional planes along a vertical time axis in 3D space.