Browsing by Author "Lotfalizadeh, Hamidreza"

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    Force-directed graph embedding with hops distance
    (IEEE, 2023-12) Lotfalizadeh, Hamidreza; Al Hasan, Mohammad; Computer Science, Luddy School of Informatics, Computing, and Engineering
    Graph embedding has become an increasingly important technique for analyzing graph-structured data. By representing nodes in a graph as vectors in a low-dimensional space, graph embedding enables efficient graph processing and analysis tasks like node classification, link prediction, and visualization. In this paper, we propose a novel force-directed graph embedding method that utilizes the steady acceleration kinetic formula to embed nodes in a way that preserves graph topology and structural features. Our method simulates a set of customized attractive and repulsive forces between all node pairs with respect to their hop-distance. These forces are then used in Newton’s second law to obtain the acceleration of each node. The method is intuitive, parallelizable, and highly scalable. We evaluate our method on several graph analysis tasks and show that it achieves competitive performance compared to state-of-the-art unsupervised embedding techniques.
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    Investigating Real-Time Entropy Features of DDoS Attack Based on Categorized Partial-Flows
    (IEEE, 2020-01) Lotfalizadeh, Hamidreza; Kim, Dongso S.; Electrical and Computer Engineering, School of Engineering and Technology
    With the advent of IoT devices and exponential growth of nodes on the internet, computer networks are facing new challenges, with one of the more important ones being DDoS attacks. In this paper, new features to detect initiation and termination of DDoS attacks are investigated. The method to extract these features is devised with respect to some openflowbased switch capabilities. These features provide us with a higher resolution to view and process packet count entropies, thus improving DDoS attack detection capabilities. Although some of the technical assumptions are based on SDN technology and openflow protocol, the methodology can be applied in other networking paradigms as well.
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    Toward Network-based DDoS Detection in Software-defined Networks
    (ACM, 2018) Jevtic, Stefan; Lotfalizadeh, Hamidreza; Kim, Dongsoo S.; Electrical and Computer Engineering, School of Engineering and Technology
    To combat susceptibility of modern computing systems to cyberattack, identifying and disrupting malicious traffic without human intervention is essential. To accomplish this, three main tasks for an effective intrusion detection system have been identified: monitor network traffic, categorize and identify anomalous behavior in near real time, and take appropriate action against the identified threat. This system leverages distributed SDN architecture and the principles of Artificial Immune Systems and Self-Organizing Maps to build a network-based intrusion detection system capable of detecting and terminating DDoS attacks in progress.