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Full-Text Articles in Engineering
Generative Machine Learning For Cyber Security, James Halvorsen, Dr. Assefaw Gebremedhin
Generative Machine Learning For Cyber Security, James Halvorsen, Dr. Assefaw Gebremedhin
Military Cyber Affairs
Automated approaches to cyber security based on machine learning will be necessary to combat the next generation of cyber-attacks. Current machine learning tools, however, are difficult to develop and deploy due to issues such as data availability and high false positive rates. Generative models can help solve data-related issues by creating high quality synthetic data for training and testing. Furthermore, some generative architectures are multipurpose, and when used for tasks such as intrusion detection, can outperform existing classifier models. This paper demonstrates how the future of cyber security stands to benefit from continued research on generative models.
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn
Self-Learning Algorithms For Intrusion Detection And Prevention Systems (Idps), Juan E. Nunez, Roger W. Tchegui Donfack, Rohit Rohit, Hayley Horn
SMU Data Science Review
Today, there is an increased risk to data privacy and information security due to cyberattacks that compromise data reliability and accessibility. New machine learning models are needed to detect and prevent these cyberattacks. One application of these models is cybersecurity threat detection and prevention systems that can create a baseline of a network's traffic patterns to detect anomalies without needing pre-labeled data; thus, enabling the identification of abnormal network events as threats. This research explored algorithms that can help automate anomaly detection on an enterprise network using Canadian Institute for Cybersecurity data. This study demonstrates that Neural Networks with Bayesian …
Supporting The Discovery, Reuse, And Validation Of Cybersecurity Requirements At The Early Stages Of The Software Development Lifecycle, Jessica Antonia Steinmann
Supporting The Discovery, Reuse, And Validation Of Cybersecurity Requirements At The Early Stages Of The Software Development Lifecycle, Jessica Antonia Steinmann
Doctoral Dissertations and Master's Theses
The focus of this research is to develop an approach that enhances the elicitation and specification of reusable cybersecurity requirements. Cybersecurity has become a global concern as cyber-attacks are projected to cost damages totaling more than $10.5 trillion dollars by 2025. Cybersecurity requirements are more challenging to elicit than other requirements because they are nonfunctional requirements that requires cybersecurity expertise and knowledge of the proposed system. The goal of this research is to generate cybersecurity requirements based on knowledge acquired from requirements elicitation and analysis activities, to provide cybersecurity specifications without requiring the specialized knowledge of a cybersecurity expert, and …
Exploring Artificial Intelligence (Ai) Techniques For Forecasting Network Traffic: Network Qos And Security Perspectives, Ibrahim Mohammed Sayem
Exploring Artificial Intelligence (Ai) Techniques For Forecasting Network Traffic: Network Qos And Security Perspectives, Ibrahim Mohammed Sayem
Electronic Thesis and Dissertation Repository
This thesis identifies the research gaps in the field of network intrusion detection and network QoS prediction, and proposes novel solutions to address these challenges. Our first topic presents a novel network intrusion detection system using a stacking ensemble technique using UNSW-15 and CICIDS-2017 datasets. In contrast to earlier research, our proposed novel network intrusion detection techniques not only determine if the network traffic is benign or normal, but also reveal the type of assault in the flow. Our proposed stacking ensemble model provides a more effective detection capability than the existing works. Our proposed stacking ensemble technique can detect …
Cybersecurity Risk Assessment Using Graph Theoretical Anomaly Detection And Machine Learning, Goksel Kucukkaya
Cybersecurity Risk Assessment Using Graph Theoretical Anomaly Detection And Machine Learning, Goksel Kucukkaya
Engineering Management & Systems Engineering Theses & Dissertations
The cyber domain is a great business enabler providing many types of enterprises new opportunities such as scaling up services, obtaining customer insights, identifying end-user profiles, sharing data, and expanding to new communities. However, the cyber domain also comes with its own set of risks. Cybersecurity risk assessment helps enterprises explore these new opportunities and, at the same time, proportionately manage the risks by establishing cyber situational awareness and identifying potential consequences. Anomaly detection is a mechanism to enable situational awareness in the cyber domain. However, anomaly detection also requires one of the most extensive sets of data and features …
Role Of Artificial Intelligence In The Internet Of Things (Iot) Cybersecurity, Murat Kuzlu, Corinne Fair, Ozgur Guler
Role Of Artificial Intelligence In The Internet Of Things (Iot) Cybersecurity, Murat Kuzlu, Corinne Fair, Ozgur Guler
Engineering Technology Faculty Publications
In recent years, the use of the Internet of Things (IoT) has increased exponentially, and cybersecurity concerns have increased along with it. On the cutting edge of cybersecurity is Artificial Intelligence (AI), which is used for the development of complex algorithms to protect networks and systems, including IoT systems. However, cyber-attackers have figured out how to exploit AI and have even begun to use adversarial AI in order to carry out cybersecurity attacks. This review paper compiles information from several other surveys and research papers regarding IoT, AI, and attacks with and against AI and explores the relationship between these …
Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi
Dynamic Adversarial Mining - Effectively Applying Machine Learning In Adversarial Non-Stationary Environments., Tegjyot Singh Sethi
Electronic Theses and Dissertations
While understanding of machine learning and data mining is still in its budding stages, the engineering applications of the same has found immense acceptance and success. Cybersecurity applications such as intrusion detection systems, spam filtering, and CAPTCHA authentication, have all begun adopting machine learning as a viable technique to deal with large scale adversarial activity. However, the naive usage of machine learning in an adversarial setting is prone to reverse engineering and evasion attacks, as most of these techniques were designed primarily for a static setting. The security domain is a dynamic landscape, with an ongoing never ending arms race …
Improving Satellite Security Through Incremental Anomaly Detection On Large, Static Datasets, Connor Hamlet, Matthew Russell, Jeremy Straub, Scott Kerlin
Improving Satellite Security Through Incremental Anomaly Detection On Large, Static Datasets, Connor Hamlet, Matthew Russell, Jeremy Straub, Scott Kerlin
Jeremy Straub
Anomaly detection is a widely used technique to detect system intrusions. Anomaly detection in Intrusion Detection and Prevent Systems (IDPS) works by establishing a baseline of normal behavior and classifying points that are at a farther distance away as outliers. The result is an “anomaly score”, or how much a point is an outlier. Recent work has been performed which has examined use of anomaly detection in data streams [1]. We propose a new incremental anomaly detection algorithm which is up to 57,000x faster than the non-incremental version while slightly sacrificing the accuracy of results. We conclude that our method …
Scada System Security: Accounting For Operator Error And Malicious Intent, Ryan Kilbride, Jeremy Straub, Eunjin Kim
Scada System Security: Accounting For Operator Error And Malicious Intent, Ryan Kilbride, Jeremy Straub, Eunjin Kim
Jeremy Straub
Supervisory control and data acquisition (SCADA) systems are becoming more and more com-monplace in many industries today. Industries are making better use of software and large scale control systems to run efficiently, without the need for large amounts of oversight. Security is a particularly large issue with such systems, however. A human must still be involved to ensure smooth operation in the event of catastrophic system error, or unusual circumstanc-es. Human involvement presents problems: operators could make mistakes, configure the system to operate sub-optimally or take malicious actions. This imple-mentation of SCADA security aims to combat these problems.
Pattern Recognition And Expert Systems For Microwave Wireless Power Transmission Failure Prevention, Cameron Kerbaugh, Allen Mcdermott, Jeremy Straub, Eunjin Kim
Pattern Recognition And Expert Systems For Microwave Wireless Power Transmission Failure Prevention, Cameron Kerbaugh, Allen Mcdermott, Jeremy Straub, Eunjin Kim
Jeremy Straub
Wireless power transfer (WPT) can be used to deliver space-generated power to ground stations through the use of microwave beams. WPT satellite power delivery systems have two major failure states: misdi-recting a beam and failing to send power to a station. This project has implemented an expert system to perform pattern recognition in an effort to prevent failures by analyzing the system state and predicting potential failures before they happen in support of space-based testing [1] and deployment [2].