Efforts and Suggestions for Improving Cybersecurity Education

Alrabaee, Saed; Al-Kfairy, Mousa; Barka, Ezedin

doi:10.1109/educon52537.2022.9766653

Cited by 11 publications

(4 citation statements)

References 30 publications

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“…Sanzo et al (2021) reiterated that while schools have tried to update their information security courses, the scarcity of specialists has made it virtually impossible for schools to maintain pace with the changing cybersecurity environment. Alrabaee et al (2022) concurred with Sanzo et al (2021) that the lack of standardized cybersecurity educational programs and challenges experienced when updating the courses and the notion that cybersecurity graduates are ill-equipped with the skills needed for the job market discouraged students from pursuing cybersecurity as a career.…”

Section: Introductionmentioning

confidence: 94%

“…For instance, the National Initiative for Cybersecurity Education, directed by the National Institute of Standards and Technology in the Department of Commerce, has collaborated with high schools and colleges to design courses and programs that will attract students and provide them the training that befits a cybersecurity specialist (Wright, 2016). The DHS has partnered with K-12 schools to supply information technology teachers with the necessary academic materials and training to train their students and increase cybersecurity awareness (Alrabaee et al, 2022). The programs designed by the collaboration of government agencies incorporate complex cybersecurity scenarios, practical cyberattack training, and the skills to counter such attacks.…”

Section: Addressing the Scarcity Of Cybersecurity Professionalsmentioning

confidence: 99%

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Addressing Cybersecurity Challenges in Education

Triplett

2023

ijses

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This study aimed to address the challenges of cybersecurity in education. As kindergarten through twelfth-grade education shifts to online and remote learning, educators and governments are increasingly vulnerable to the risks of cyberattacks and cybercrimes. This study focused on the strategies that institutions can employ to increase their students’ cybersecurity awareness and simultaneously motivate them to pursue cybersecurity as a career. To achieve the research objectives, a systematic review of ten studies was performed, and the results showed that game-based strategies were effective in increasing students’ awareness about cybersecurity and their interest in pursuing cybersecurity as a career. The study’s implications suggest that game designers and developers may want to develop advanced games that gauge students’ cybersecurity skills and ability to respond to aggressive forms of cyberattacks in addition to enhancing their knowledge of cybersecurity.

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Section: Introductionmentioning

confidence: 94%

Section: Addressing the Scarcity Of Cybersecurity Professionalsmentioning

confidence: 99%

Addressing Cybersecurity Challenges in Education

Triplett

2023

ijses

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show abstract

“…Conversely, port-based traffic identification systems operate under the assumption that applications utilize established Transmission Control Protocol/User Datagram Protocol (TCP/UDP) port numbers designated by the Internet Assigned Numbers Authority (IANA). Consequently, when apps deviate from IANA rules by utilizing dynamic ports or encryption protocols like Peer to Peer (P2P) protocol, the methods used to identify traffic and applications based on ports become ineffective [ 19 ]. Various technologies are available for implementing traffic encryption, including SSL, Transport Layer Security (TLS), Virtual Private Network (VPN), Secure Shell Protocol (SSH), and P2P.…”

Section: Related Workmentioning

confidence: 99%

Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning

Alwhbi,

Zou,

Alharbi

2024

Sensors

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Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, and mobile applications, understanding and classifying encrypted traffic are crucial for network administrators, cybersecurity professionals, and policy enforcement entities. This paper presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. The primary goals of our survey are two-fold: First, we present the overall procedure and provide a detailed explanation of utilizing machine learning in analyzing and classifying encrypted network traffic. Second, we review state-of-the-art techniques and methodologies in traffic analysis. Our aim is to provide insights into current practices and future directions in encrypted traffic analysis and classification, especially machine-learning-based analysis.

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“…Classifying this traffic can enable better optimization by internet service providers (ISPs) to prioritize real-time applications with more suitable network nodes [1]. Moreover, classifying network traffic may aid in malicious network traffic interception, which local governments typically mandate [3]- [5]. Automation of this task has garnered greater interest as the scale of network traffic increases and new threats to network security reveal themselves.…”

Section: Introductionmentioning

confidence: 99%

Tabular-to-Image Transformations for the Classification of Anonymous Network Traffic Using Deep Residual Networks

Briner,

Cullen,

Halladay

et al. 2023

IEEE Access

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With the meteoric rise in anonymous network traffic data, there is a considerable need for effective automation in traffic identification tasks. Though many shallow and deep machine learning network traffic classification solutions have been proposed, they often rely on tabular data, making them unable to detect complex spatial relationships. However, recent advancements in computer processing power have increased the viability of transforming tabular data into images for training deep convolutional neural networks, transforming structured data problems into spatial ones. To identify the most effective methods for representing tabular anonymous network traffic data as images, we compared five deep learning classifiers trained on data from different tabular-to-image algorithms-Image Generator for Tabular Data (IGTD), DeepInsight, vector-of-feature wrapping (normalized and non-normalized), and our newly introduced Binary Image Encoding (BIE) technique in the classification of eight network application types. Furthermore, we examine whether deep residual models trained on tabular-to-image data can outperform the top-performing shallow learner, XGBoost, at classifying anonymous network traffic. We found that ResNet-50, a pre-trained instance of deep residual network, trained on image datasets using IGTD and the novel Binary Image Encoding outperformed XGBoost trained on tabular data. Our ResNet-50 models trained using IGTD and BIE achieved F1-scores of 96.0% and 98.49% respectively, improving on the baseline of 95.1% achieved by XGBoost.

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Efforts and Suggestions for Improving Cybersecurity Education

Cited by 11 publications

References 30 publications

Addressing Cybersecurity Challenges in Education

Addressing Cybersecurity Challenges in Education

Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning

Tabular-to-Image Transformations for the Classification of Anonymous Network Traffic Using Deep Residual Networks

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