Computer Science and Engineering

Permanent URI for this collection

https://hdl.handle.net/10323/11886

Browse

Browsing Computer Science and Engineering by Subject "Artificial intelligence"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Detecting and Classifying Malware in Electrical Power Grids Via Cyberdeception
    (2024-01-01) Omar, Tallal Mohamed; Zohdy, Mohamed A; Edwards, William; Caushaj, Eralda; Sutton, Sara
    Artificial intelligence (AI) has become an essential instrument for enterprises aiming to protect their digital assets within a progressively aggressive cyber landscape. As the dependence on digital technologies increases among companies and individuals, the risks associated with cyberattacks are also advancing in terms of complexity and magnitude. AI and the proliferation of technology has led to a significant concern over security, mostly due to the escalating prevalence of malware on industrial computers. This has resulted in potential physical harm to computer systems and the individuals involved. Malware is a collection of malicious programming code that aims to inflict harm against computer systems, programs, or online apps. These applications lack the ability to differentiate between legitimate system calls and those that are intended to cause harm. Therefore, it is imperative to ensure that computer systems and online applications are constructed in a manner that enables the identification and differentiation of malicious activities from legitimate application activities. The utilization of AI in the realm of cybersecurity is revolutionizing the domain of digital protection. There are various techniques that can be used to identify malicious activity, leveraging innovative concepts such as AI, machine learning, and deep learning. The present study presents a proposal for utilizing AI approaches to identify and mitigate malware activity in computer memory, with the aim of safeguarding against unauthorized access to and manipulation of physical data within the system. This research aims to combine the traditional K-means algorithm with other methods and functionalities to perform data aggregation tasks on a physical dataset. The primary objective is to identify anomalies in the dataset using clustering techniques. These anomalies will serve as triggers for creating a replica of the main process as a decoy thread. The decoy thread will be equipped with decoy sensors and actuators. The analysis will be conducted on the decoy thread rather than the main process, allowing for intrusive observation. The same host environment will be provided to memory-resident malware, enabling it to continue operating within the main operating system process. The analysis process involves utilizing a replicated instance of malware that resides within a deceptive thread.
  • Thumbnail Image
    Item
    Resilient Suppliers Selection System Using Machine Learning Algorithms and Risk Assessment Methodology
    (2022-01-01) Albadrani, Abdullah Meteb; Zohdy, Mohamed A.; Edwards, William; Ruegg, Erica
    Demand, supply, pricing, and lead time are all unpredictable in the manufacturingindustry, and the manufacturer must function in this environment. Because of the enormous amount of data available and the introduction of new technologies, such as the internet of things (IoT), machine learning (ML), and Blockchain, administrators and government officials are better able to deal with uncertainty by applying intelligent decision-making principles to their situations. All supply chains must make use of new technology and analyze previous data to forecast and improve the success of future operations. At the moment, we rely on the supply chain and its facilities for healthcare when we receive our vaccine, for our food when we go grocery shopping, and for transportation when we drive our cars. Supplier selection is exposed to the three most significant factors: quality, delivery, and performance history, which are all evaluated separately. The use of data analytic capabilities in the selection of robust supplier portfolios has not been thoroughly investigated. Manufacturers typically have three to four resilient suppliers for the same item, but occasionally one or two of them will fail, causing a ripple effect throughout the entire supply chain. This is a frequent problem that the supply chain must deal with on a daily basis. Supply chain resilience, on the other hand, ignores or is incompatible with the risk profiles of suppliers’ performance.
  • Thumbnail Image
    Item
    Semantic and Temporal Graph Neural Networks for Supply Chain Risk Quantification
    (2023-01-01) Matovski, Svetle; Nezamoddini, Nasim; Sengupta, Sankar; Lipták, László; Fu, Huirong
    Calculating supply chain risk management values requires a granular set of parameters. Failure risk at each supply chain entity is a dependent value influenced by entities within a supply chain. Predicting future risk values from historical data is based on trends and patterns therein. On the surface, historical data does not show how the data interlink and connects. To understand the problem this research starts by examining the underlying data structure that a supply chain uses to store data. Mining the data requires understanding its relationship to other data within the structure. This understanding allows the system or user to make better decisions. The research brings together four different pieces to show a risk value at each node within a supply chain network. This research generates a dataset using seed data and trend data to build a supply chain network. This research then predicts future risk values at a node level using ARMIA and a graph neural network. Node centrality values help show the importance of nodes to a supply chain. The centrality methods this research uses are betweenness, degree, eigenvector, and Katz centrality. Each one gives an importance value based on a different view of centrality. This research then looks for the vi influence of a node on a customer. Calculating an influence value by using Bayesian networks. The last value calculated is the profit loss at a customer node. All these different pieces are brought together in the risk calculation equation to give a final node-per-node risk value. The risk calculations align well with the structure of a graph. This research will show that a graph neural network and a graph database are useful for supply chain problems.