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COMMITTEE CHAIR<\/strong>: Dr. Annamalai Annamalai<\/p>\n

TITLE<\/strong>: STRENGTHENING SMART GRID CYBER-PHYSICAL RESILIENCE THROUGH COMMUNICATION-PROTOCOL BASED DOS DETECTION AND PREDICTIVE MITIGATION<\/p>\n

ABSTRACT: <\/strong>This dissertation investigates the impact of communication-protocol-based Denial-of-Service (DoS) attacks on smart-grid cyber\u2013physical stability through a comparative evaluation of Secure Shell (SSH) and Telnet protocols. A hardware-in-the-loop (HIL) experimental platform was developed, featuring a multi-bus smart-grid system integrated with SCADA control, real-time network monitoring, and synchronized cyber\u2013physical data acquisition. Controlled attack scenarios\u2014specifically brute-force authentication and SYN flood resource exhaustion\u2014were executed under identical conditions. Experimental results demonstrate that communication-layer disruptions propagate directly into the physical grid, producing measurable instability. Frequency emerged as the dominant indicator of this instability, with deviations reaching up to 39 Hz, while voltage remained within bounded variations of \u00b11\u20135 V. Telnet-based attacks resulted in a mean frequency degradation of 24.96 Hz, compared to 16.28 Hz under SSH\u2014representing approximately 53% greater instability in the Telnet environment. Protocol performance analysis indicates that SSH maintains higher resilience despite increased computational load (CPU utilization up to 71%), whereas Telnet exhibits faster failure due to a lack of encryption and authentication controls. Network-level observations revealed increased latency, packet loss, and authentication failures, with a corresponding degradation in SCADA communication and control response. To quantify these interactions, a unified cyber\u2013physical modeling framework was developed, achieving predictive accuracy with coefficients of determination R^2 = 0.9174 (state model) and R^2 = 0.9329 (regression model). This framework integrates communication-layer metrics, system resource utilization, and electrical parameters to estimate attack intensity and predict instability. The primary contribution of this dissertation is the development and experimental validation of a protocol-aware cyber\u2013physical defense framework that quantitatively links communication-layer disruptions to power-system instability, enabling predictive resilience assessment in smart-grid environments.<\/p>\n

Keywords<\/strong>: Cybersecurity, SCADA, cyber-physical systems<\/p>\n

Room Location: <\/strong>Electrical and Computer Engineering Department Conference, Room 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Nzeako Anyakora Dissertation Defense, Wednesday, June 17, 2026 @ 11:00 am Central Time"},"content":{"rendered":"

COMMITTEE CHAIR<\/strong>: Dr. Cajetan Akujuobi<\/p>\n

TITLE<\/strong>: EDGE-NATIVE MULTI-AGENT SYSTEM FOR LATENCY-CRITICAL ANOMALY DETECTION AND QOS OPTIMIZATION IN 6G SMART MANUFACTURING<\/p>\n

ABSTRACT: T<\/strong>he increasing reliance on wireless connectivity in smart manufacturing places stringent demands on network latency, reliability, and adaptability that exceed the capabilities of static or threshold-based quality-of-service (QoS) control mechanisms. While 5G standalone networks enable industrial deployments, emerging 6G environments are expected to introduce greater variability, tighter latency constraints, and more frequent performance degradation events, necessitating autonomous and learning-driven control strategies. This dissertation proposes an intelligent multi-agent framework for real-time anomaly detection and QoS optimization in industrial wireless networks. The framework integrates edge-based monitoring, unsupervised anomaly detection, and reinforcement learning\u2013driven control to enable proactive and adaptive network management. Network telemetry collected from a Firecell 5G standalone testbed is used as an empirical baseline, while a scaled 6G-emulated dataset is employed to evaluate robustness under more stringent performance conditions. Anomaly detection is performed using Isolation Forest and deep autoencoder models to identify deviations in latency, throughput, and packet loss. QoS control is formulated as a Markov Decision Process and addressed using reinforcement learning agents that dynamically adjust network parameters to mitigate detected anomalies. The proposed multi-agent system is evaluated against static and threshold-based baselines across multiple operating regimes. Experimental results demonstrate that the learning-based approach achieves improved latency stability, faster throughput recovery, and reduced packet loss under both moderate and stress-intensive conditions. The framework maintains consistent control behavior across 5G and 6G-oriented scenarios, indicating scalability and robustness under increased network volatility. These results validate the effectiveness of multi-agent, learning-driven QoS control for next-generation industrial networks and provide a practical pathway toward autonomous network management in 6G-enabled smart manufacturing environments.<\/p>\n

Keywords<\/strong>: Intelligent multi-agent framework, edge-based monitoring, 5G standalone testbed, QoS, 6G, autonomous network management<\/p>\n

Room Location: <\/strong>Electrical and Computer Engineering Department Conference, Room 315D<\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":432,"featured_media":7636,"template":"","meta":{"_acf_changed":false,"rs_blank_template":"","rs_page_bg_color":"","slide_template_v7":"","_tribe_events_status":"","_tribe_events_status_reason":"","_tribe_events_is_hybrid":"","_tribe_events_is_virtual":"","_tribe_events_virtual_video_source":"","_tribe_events_virtual_embed_video":"","_tribe_events_virtual_linked_button_text":"","_tribe_events_virtual_linked_button":"","_tribe_events_virtual_show_embed_at":"","_tribe_events_virtual_show_embed_to":[],"_tribe_events_virtual_show_on_event":"","_tribe_events_virtual_show_on_views":"","_tribe_events_virtual_url":"","footnotes":""},"tags":[],"tribe_events_cat":[1296],"class_list":["post-8749","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","tribe_events_cat-main-events","cat_main-events"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events\/8749","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/users\/432"}],"version-history":[{"count":0,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events\/8749\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media\/7636"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media?parent=8749"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tags?post=8749"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events_cat?post=8749"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}},{"id":8745,"date":"2026-05-04T22:06:04","date_gmt":"2026-05-05T03:06:04","guid":{"rendered":"https:\/\/www.pvamu.edu\/graduateschool\/?post_type=tribe_events&p=8745"},"modified":"2026-05-19T12:30:53","modified_gmt":"2026-05-19T17:30:53","slug":"introduction-to-evidence-based-undergraduate-teaching-course-summer-2026-june-17-august-5","status":"publish","type":"tribe_events","link":"https:\/\/www.pvamu.edu\/graduateschool\/event\/introduction-to-evidence-based-undergraduate-teaching-course-summer-2026-june-17-august-5\/","title":{"rendered":"Introduction to Evidence-Based Undergraduate Teaching Course | Summer 2026 | June 17 – August 5"},"content":{"rendered":"

Dear Doctoral Students,<\/div>\n
If you are interested in teaching in higher education, this training offers an opportunity to learn, network, and add a professional development item to your vita.<\/div>\n
Sincerely,<\/div>\n
Gibson.<\/div>\n
>>>>>>>>>>>>>>>><\/div>\n
\n

Registration is now open for\u00a0An Introduction to Evidence-Based Undergraduate Teaching<\/b>. Completers of this weekly local learning community can earn a CIRTL Associate-level digital badge and certificate, which is recognized across the country for innovation in undergraduate education and teaching.<\/span><\/p>\n

\u00a0<\/span>Please share the registration form linked below with anyone on your campus who may benefit.\u00a0 Our first weekly meeting is on\u00a0Wednesday, June 17, 2026, at 3:00 pm;\u00a0<\/b>sessions conclude on August 5.\u00a0<\/b>There is no cost to participants who engage in this programming.<\/span><\/p>\n

https:\/\/gradconnect.tamu.edu\/register\/IntroSummer26<\/a>\u00a0<\/u><\/span><\/p>\n

\u00a0<\/span>There are also many offerings from the CIRTL National Network that may be of interest to your graduate communities. Program information and registration can be found here:\u00a0\u00a0<\/a><\/span>https:\/\/cirtl.net\/events\/<\/a><\/span>\u00a0<\/span><\/p>\n

\u00a0<\/span>We have been fortunate to welcome many participants from your campuses in the past, and their contributions have significantly enriched our CIRTL local learning community. We look forward to continuing this valuable collaboration!<\/span><\/p>\n

\u00a0<\/span>If you have any questions or need assistance, please contact our CIRTL team at\u00a0<\/span>CIRTL@tamu.edu<\/span><\/p>\n<\/div>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":432,"featured_media":8088,"template":"","meta":{"_acf_changed":false,"rs_blank_template":"","rs_page_bg_color":"","slide_template_v7":"","_tribe_events_status":"","_tribe_events_status_reason":"","_tribe_events_is_hybrid":"","_tribe_events_is_virtual":"","_tribe_events_virtual_video_source":"","_tribe_events_virtual_embed_video":"","_tribe_events_virtual_linked_button_text":"","_tribe_events_virtual_linked_button":"","_tribe_events_virtual_show_embed_at":"","_tribe_events_virtual_show_embed_to":[],"_tribe_events_virtual_show_on_event":"","_tribe_events_virtual_show_on_views":"","_tribe_events_virtual_url":"","footnotes":""},"tags":[],"tribe_events_cat":[1296],"class_list":["post-8745","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","tribe_events_cat-main-events","cat_main-events"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events\/8745","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/users\/432"}],"version-history":[{"count":0,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events\/8745\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media\/8088"}],"wp:attachment":[{"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/media?parent=8745"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tags?post=8745"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/www.pvamu.edu\/graduateschool\/wp-json\/wp\/v2\/tribe_events_cat?post=8745"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}]