SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank's control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naive Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environmentsComment: E-Preprin

Multi-aspect rule-based AI: Methods, taxonomy, challenges and directions towards automation, intelligence and transparent cybersecurity modeling for critical infrastructures

Critical infrastructure (CI) typically refers to the essential physical and virtual systems, assets, and services that are vital for the functioning and well-being of a society, economy, or nation. However, the rapid proliferation and dynamism of today\u27s cyber threats in digital environments may disrupt CI functionalities, which would have a debilitating impact on public safety, economic stability, and national security. This has led to much interest in effective cybersecurity solutions regarding automation and intelligent decision-making, where AI-based modeling is potentially significant. In this paper, we take into account “Rule-based AI” rather than other black-box solutions since model transparency, i.e., human interpretation, explainability, and trustworthiness in decision-making, is an essential factor, particularly in cybersecurity application areas. This article provides an in-depth study on multi-aspect rule based AI modeling considering human interpretable decisions as well as security automation and intelligence for CI. We also provide a taxonomy of rule generation methods by taking into account not only knowledge-driven approaches based on human expertise but also data-driven approaches, i.e., extracting insights or useful knowledge from data, and their hybridization. This understanding can help security analysts and professionals comprehend how systems work, identify potential threats and anomalies, and make better decisions in various real-world application areas. We also cover how these techniques can address diverse cybersecurity concerns such as threat detection, mitigation, prediction, diagnosis for root cause findings, and so on in different CI sectors, such as energy, defence, transport, health, water, agriculture, etc. We conclude this paper with a list of identified issues and opportunities for future research, as well as their potential solution directions for how researchers and professionals might tackle future generation cybersecurity modeling in this emerging area of study

Applications of Machine Learning to Threat Intelligence, Intrusion Detection and Malware

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or reduce analyst workload, it also introduces new attack surfaces

Exploring Cybertechnology Standards Through Bibliometrics: Case of National Institute of Standards and Technology

Cyber security is one of the topics that gain importance today. It is necessary to determine the basic components, basic dynamics, and main actors of the Cyber security issue, which is obvious that it will have an impact in many areas from social, social, economic, environmental, and political aspects, as a hot research topic. When the subject literature is examined, it has become a trend-forming research subject followed by institutions and organizations that produce R&D policy, starting from the level of governments. In this study, cybersecurity research is examined in the context of 5 basic cyber security functions specified in the cyber security standard (CSF) defined by the National Institute of Standards and Technology (NIST). It is aimed to determine the research topics emerging in the international literature, to identify the most productive countries, to determine the rankings created by these countries according to their functions, to determine the research clusters and research focuses. In the study, several quantitative methods were used, especially scientometrics, social network analysis (SNA) line theory and structural hole analysis. Statistical tests (Log-Likelihood Ratio) were used to reveal the prominent areas, and the text mining method was also used. we first defined a workflow according to the “Identify”, “Protect”, “Detect”, “Respond” and “Recover” setups, and conducted an online search on the Web of Science (WoS) to access the information on the publications on the relevant topics It is seen that actors, institutions and research create different densities according to various geographical regions in the 5 functions defined within the framework of cybersecurity. It is possible to say that infiltration detection, the internet of things and the concept of artificial intelligence are among the other prominent research focuses, although it is seen that smart grids are among the most prominent research topics. In the first clustering analysis we performed, we can say that 17 clusters are formed, especially when we look under the definition function. The largest of these clusters has 32 data points, so-called decision making models

Novel Alert Visualization: The Development of a Visual Analytics Prototype for Mitigation of Malicious Insider Cyber Threats

Cyber insider threat is one of the most difficult risks to mitigate in organizations. However, innovative validated visualizations for cyber analysts to better decipher and react to detected anomalies has not been reported in literature or in industry. Attacks caused by malicious insiders can cause millions of dollars in losses to an organization. Though there have been advances in Intrusion Detection Systems (IDSs) over the last three decades, traditional IDSs do not specialize in anomaly identification caused by insiders. There is also a profuse amount of data being presented to cyber analysts when deciphering big data and reacting to data breach incidents using complex information systems. Information visualization is pertinent to the identification and mitigation of malicious cyber insider threats. The main goal of this study was to develop and validate, using Subject Matter Experts (SME), an executive insider threat dashboard visualization prototype. Using the developed prototype, an experimental study was conducted, which aimed to assess the perceived effectiveness in enhancing the analysts’ interface when complex data correlations are presented to mitigate malicious insiders cyber threats. Dashboard-based visualization techniques could be used to give full visibility of network progress and problems in real-time, especially within complex and stressful environments. For instance, in an Emergency Room (ER), there are four main vital signs used for urgent patient triage. Cybersecurity vital signs can give cyber analysts clear focal points during high severity issues. Pilots must expeditiously reference the Heads Up Display (HUD), which presents only key indicators to make critical decisions during unwarranted deviations or an immediate threat. Current dashboard-based visualization techniques have yet to be fully validated within the field of cybersecurity. This study developed a visualization prototype based on SME input utilizing the Delphi method. SMEs validated the perceived effectiveness of several different types of the developed visualization dashboard. Quantitative analysis of SME’s perceived effectiveness via self-reported value and satisfaction data as well as qualitative analysis of feedback provided during the experiments using the prototype developed were performed. This study identified critical cyber visualization variables and identified visualization techniques. The identifications were then used to develop QUICK.v™ a prototype to be used when mitigating potentially malicious cyber insider threats. The perceived effectiveness of QUICK.v™ was then validated. Insights from this study can aid organizations in enhancing cybersecurity dashboard visualizations by depicting only critical cybersecurity vital signs

Malware in the Future? Forecasting of Analyst Detection of Cyber Events

There have been extensive efforts in government, academia, and industry to anticipate, forecast, and mitigate cyber attacks. A common approach is time-series forecasting of cyber attacks based on data from network telescopes, honeypots, and automated intrusion detection/prevention systems. This research has uncovered key insights such as systematicity in cyber attacks. Here, we propose an alternate perspective of this problem by performing forecasting of attacks that are analyst-detected and -verified occurrences of malware. We call these instances of malware cyber event data. Specifically, our dataset was analyst-detected incidents from a large operational Computer Security Service Provider (CSSP) for the U.S. Department of Defense, which rarely relies only on automated systems. Our data set consists of weekly counts of cyber events over approximately seven years. Since all cyber events were validated by analysts, our dataset is unlikely to have false positives which are often endemic in other sources of data. Further, the higher-quality data could be used for a number for resource allocation, estimation of security resources, and the development of effective risk-management strategies. We used a Bayesian State Space Model for forecasting and found that events one week ahead could be predicted. To quantify bursts, we used a Markov model. Our findings of systematicity in analyst-detected cyber attacks are consistent with previous work using other sources. The advanced information provided by a forecast may help with threat awareness by providing a probable value and range for future cyber events one week ahead. Other potential applications for cyber event forecasting include proactive allocation of resources and capabilities for cyber defense (e.g., analyst staffing and sensor configuration) in CSSPs. Enhanced threat awareness may improve cybersecurity.Comment: Revised version resubmitted to journa