The growing interest in Honeypots has resulted in increased research, and consequently, a large number of research surveys and/or reviews. Most Honeypot surveys and/or reviews focus on specific and narrow Honeypot res...The growing interest in Honeypots has resulted in increased research, and consequently, a large number of research surveys and/or reviews. Most Honeypot surveys and/or reviews focus on specific and narrow Honeypot research areas. This study aims at exploring and presenting advances and trends in Honeypot’s research and development areas. To this end, a systematic methodology and meta-review analysis were applied to the selection, evaluation, and qualitative examination of the most influential Honeypot surveys and/or reviews available in scientific bibliographic databases. A total of 188 papers have been evaluated and 22 research papers are found by this study to have a higher impact. The findings of the study suggest that the Honeypot survey and/or review papers of considerable relevance to the research community were mostly published in 2018, by IEEE, in conferences organized in India, and included in the IEEE Xplore database. Also, there have been few qualities Honeypot surveys and/or reviews published after 2018. Furthermore, the study identified 10 classes of vital and emerging themes and/or key topics in Honeypot research. This work contributes to research efforts employing established systematic review and reporting methods in Honeypot research. We have included our meta-review methodology, in order to allow further work in this area aiming at a better understanding of the progression of Honeypot research and advances.展开更多
Mitigating increasing cyberattack incidents may require strategies such as reinforcing organizations’ networks with Honeypots and effectively analyzing attack traffic for detection of zero-day attacks and vulnerabili...Mitigating increasing cyberattack incidents may require strategies such as reinforcing organizations’ networks with Honeypots and effectively analyzing attack traffic for detection of zero-day attacks and vulnerabilities. To effectively detect and mitigate cyberattacks, both computerized and visual analyses are typically required. However, most security analysts are not adequately trained in visualization principles and/or methods, which is required for effective visual perception of useful attack information hidden in attack data. Additionally, Honeypot has proven useful in cyberattack research, but no studies have comprehensively investigated visualization practices in the field. In this paper, we reviewed visualization practices and methods commonly used in the discovery and communication of attack patterns based on Honeypot network traffic data. Using the PRISMA methodology, we identified and screened 218 papers and evaluated only 37 papers having a high impact. Most Honeypot papers conducted summary statistics of Honeypot data based on static data metrics such as IP address, port, and packet size. They visually analyzed Honeypot attack data using simple graphical methods (such as line, bar, and pie charts) that tend to hide useful attack information. Furthermore, only a few papers conducted extended attack analysis, and commonly visualized attack data using scatter and linear plots. Papers rarely included simple yet sophisticated graphical methods, such as box plots and histograms, which allow for critical evaluation of analysis results. While a significant number of automated visualization tools have incorporated visualization standards by default, the construction of effective and expressive graphical methods for easy pattern discovery and explainable insights still requires applied knowledge and skill of visualization principles and tools, and occasionally, an interdisciplinary collaboration with peers. We, therefore, suggest the need, going forward, for non-classical graphical methods for visualizing attack patterns and communicating analysis results. We also recommend training investigators in visualization principles and standards for effective visual perception and presentation.展开更多
In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact infor...In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.展开更多
文摘The growing interest in Honeypots has resulted in increased research, and consequently, a large number of research surveys and/or reviews. Most Honeypot surveys and/or reviews focus on specific and narrow Honeypot research areas. This study aims at exploring and presenting advances and trends in Honeypot’s research and development areas. To this end, a systematic methodology and meta-review analysis were applied to the selection, evaluation, and qualitative examination of the most influential Honeypot surveys and/or reviews available in scientific bibliographic databases. A total of 188 papers have been evaluated and 22 research papers are found by this study to have a higher impact. The findings of the study suggest that the Honeypot survey and/or review papers of considerable relevance to the research community were mostly published in 2018, by IEEE, in conferences organized in India, and included in the IEEE Xplore database. Also, there have been few qualities Honeypot surveys and/or reviews published after 2018. Furthermore, the study identified 10 classes of vital and emerging themes and/or key topics in Honeypot research. This work contributes to research efforts employing established systematic review and reporting methods in Honeypot research. We have included our meta-review methodology, in order to allow further work in this area aiming at a better understanding of the progression of Honeypot research and advances.
文摘Mitigating increasing cyberattack incidents may require strategies such as reinforcing organizations’ networks with Honeypots and effectively analyzing attack traffic for detection of zero-day attacks and vulnerabilities. To effectively detect and mitigate cyberattacks, both computerized and visual analyses are typically required. However, most security analysts are not adequately trained in visualization principles and/or methods, which is required for effective visual perception of useful attack information hidden in attack data. Additionally, Honeypot has proven useful in cyberattack research, but no studies have comprehensively investigated visualization practices in the field. In this paper, we reviewed visualization practices and methods commonly used in the discovery and communication of attack patterns based on Honeypot network traffic data. Using the PRISMA methodology, we identified and screened 218 papers and evaluated only 37 papers having a high impact. Most Honeypot papers conducted summary statistics of Honeypot data based on static data metrics such as IP address, port, and packet size. They visually analyzed Honeypot attack data using simple graphical methods (such as line, bar, and pie charts) that tend to hide useful attack information. Furthermore, only a few papers conducted extended attack analysis, and commonly visualized attack data using scatter and linear plots. Papers rarely included simple yet sophisticated graphical methods, such as box plots and histograms, which allow for critical evaluation of analysis results. While a significant number of automated visualization tools have incorporated visualization standards by default, the construction of effective and expressive graphical methods for easy pattern discovery and explainable insights still requires applied knowledge and skill of visualization principles and tools, and occasionally, an interdisciplinary collaboration with peers. We, therefore, suggest the need, going forward, for non-classical graphical methods for visualizing attack patterns and communicating analysis results. We also recommend training investigators in visualization principles and standards for effective visual perception and presentation.
文摘In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.
