With the rapid rate at which networking technologies are changing, there is a need to regularly update network activity datasets to accurately reflect the current state of network infrastructure/traffic. The uniqueness of this work was that this was the first network dataset collected using Zeek and labelled using the MITRE ATT&CK framework. In addition to identifying attack traffic, the MITRE ATT&CK framework allows for the detection of adversary behavior leading to an attack. It can also be used to develop user profiles of groups intending to perform attacks. This paper also outlined how both the cyber range and hadoop’s big data platform were used for creating this network traffic data repository. The data was collected using Security Onion in two formats: Zeek and PCAPs. Mission logs, which contained the MITRE ATT&CK data, were used to label the network attack data. The data was transferred daily from the Security Onion virtual machine running on a cyber range to the big-data platform, Hadoop’s distributed file system. This dataset, UWF-ZeekData22, is publicly available at datasets.uwf.edu.

While computer networks and the massive amount of communication taking place on these networks grow, the amount of damage that can be done by network intrusions grows in tandem. The need is for an effective and scalable intrusion detection system (IDS) to address these potential damages that come with the growth of these networks. A great deal of contemporary research on near real-time IDS focuses on applying machine learning classifiers to labeled network intrusion datasets, but these datasets need be relevant pertaining to the currency of the network intrusions. This paper focuses on a newly created dataset, UWF-ZeekData22, that analyzes data from Zeek’s Connection Logs collected using Security Onion 2 network security monitor and labelled using the MITRE ATT&CK framework TTPs. Due to the volume of data, Spark, in the big data framework, was used to run many of the well-known classifiers (naïve Bayes, random forest, decision tree, support vector classifier, gradient boosted trees, and logistic regression) to classify the reconnaissance and discovery tactics from this dataset. In addition to looking at the performance of these classifiers using Spark, scalability and response time were also analyzed.

Recent years have seen a disconnect between much-needed real-world skills and knowledge imparted to cybersecurity graduates by higher education institutions. As employers are shifting their focus to skills and competencies when hiring fresh graduates, higher education institutions are facing a call to action to design curricula that impart relevant knowledge, skills, and competencies to their graduates, and to devise effective means to assess them. Some institutions have successfully engaged with industry partners in creating apprenticeship programs and work-based learning for their students. However, not all educational institutions have similar capabilities and resources. A trend in engineering, computer science, and information technology programs across the United States is to design project-based or scenario-based curricula that impart relevant knowledge, skills, and competencies. At our institution, we have taken an innovative approach in designing our cybersecurity courses using scenario-based learning and assessing knowledge, skills, and competencies using scenario-guiding questions. We have used the National Initiative for Cybersecurity Education (NICE) Cybersecurity Workforce Framework and the Office of Personnel Management (OPM) Hiring Cybersecurity Workforce report for skills, knowledge, and competency mapping. This paper highlights our approach, presenting its overall design and two example mappings.

This article presents a statistical approach using entropy and classification-based analysis to detect anomalies in industrial control systems traffic. Several statistical techniques have been proposed to create baselines and measure deviation to detect intrusion in enterprise networks with a centralized intrusion detection approach in mind. Looking at traffic volume alone to find anomalous deviation may not be enough—it may result in increased false positives. The near real-time communication requirements, coupled with the lack of centralized infrastructure in operations technology and limited resources of the sensor motes, require an efficient anomaly detection system characterized by these limitations. This paper presents extended results from our previous work by presenting a detailed cluster-based entropy analysis on selected network traffic features. It further extends the analysis using a classification-based approach. Our detailed entropy analysis corroborates with our earlier findings that, although some degree of anomaly may be detected using univariate and bivariate entropy analysis for Denial of Service (DOS) and Man-in-the-Middle (MITM) attacks, not much information may be obtained for the initial reconnaissance, thus preventing early stages of attack detection in the Cyber Kill Chain. Our classification-based analysis shows that, overall, the classification results of the DOS attacks were much higher than the MITM attacks using two Modbus features in addition to the three TCP/IP features. In terms of classifiers, J48 and random forest had the best classification results and can be considered comparable. For the DOS attack, no resampling with the 60–40 (training/testing split) had the best results (average accuracy of 97.87%), but for the MITM attack, the 80–20 non-attack vs. attack data with the 75–25 split (average accuracy of 82.81%) had the best results.

Botnet communications are obfuscated within legitimate network protocols to avoid detection and remediation. Domain Name Service (DNS) is a protocol of choice to hide communication with Command & Control (C&C) servers, where botmasters tunnel these communications within DNS request and response. Since botnet communications are characterized by different features, botmasters may evade detection methods by modifying some of these features. This paper proposes a multi-staged detection approach for Domain Generation Algorithm (DGA) using domain fluxing, Fast Flux Service Network (FFSN), and encrypted DNS tunneled-based botnets using BRO Network Security Monitor. This approach is able to detect DNS-tunneled botnet communications by analyzing different techniques used to find C&C servers, and also using signature matching technique to detect DNS-tunneled SSH handshake between bots and C&C servers.