SACM - United States of America
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Item Restricted INVESTIGATING THE MECHANISMS AND DYNAMICS OF POST-TRAUMATIC BRAIN INJURY PATHOLOGIES: FROM ASTROCYTE REACTIVITY TO SEIZURE-LIKE ACTIVITY(Saudi Digital Library, 2026) Mufti, Shatha; Shi, RiyiTraumatic brain injury (TBI) is a leading cause of death and long-term disability worldwide, frequently resulting in complex secondary injuries and chronic neurological conditions such as post-traumatic epilepsy (PTE). Despite the high prevalence of TBI, identifying effective treatments for the subsequent sequalae has been challenging due to the unclear mechanisms of how TBI leads to conditions like PTE. My work addresses this gap by utilizing an in vitro TBI-on-a-chip model to simulate concussive impacts on primary cortical networks and conduct targeted mechanistic investigations into the biochemical and electrophysiological mechanisms driving TBI-induced pathologies like PTE. In the first study, a combination of electrophysiological microelectrode array (MEA) recordings and immunocytochemistry are used to investigate the role of the toxic reactive aldehyde acrolein in promoting injury-induced pathologies. The results identify acrolein as a primary driver of post-TBI astrocyte reactivity and neuronal network hyperexcitability and show that sequestering acrolein pharmacologically with hydralazine effectively mitigates these pathological changes. Furthermore, a novel machine learning (ML) framework using convolutional neural networks and Grad-CAM was employed to analyze astrocyte structural remodeling and pinpoint specific morphological features associated with injury and treatment conditions. In the second study, MEA recordings capturing changes in firing and bursting dynamics after impact are analyzed to examine the mechanisms underlying the transition of injured networks into seizure-like activity (SLA) using custom algorithms to quantify cross-correlogram shapes and identify bursts in network firing. The findings show that injury not only increases the synchronization of neuronal firing, which is a hallmark of SLA, but also reorganizes firing hierarchies and alters leader-follower relationships among neurons. Examining multiple dimensions of network activity simultaneously provides a more comprehensive understanding of how TBI alters neuronal communication and network dynamics and promotes SLA. In the third study, further investigations into altered neuronal interactions that lead to SLA are performed by applying a multilayered ML pipeline, which includes LSTM autoencoders, UMAP clustering, and deep Granger causality to MEA recordings of neuronal networks treated with bicuculline, a standardized experimental model of SLA. The results reveal that distinct neuronal subpopulations with unique activity profiles emerge during SLA, even in states of global network synchronization. Collectively, the findings in this thesis demonstrate that post-traumatic pathologies, especially PTE, are driven by identifiable molecular targets and structured network reorganization, suggesting a variety of options for therapeutic targeting. Furthermore, this work highlights the TBI-on-a-chip system as a versatile platform for investigating the pathophysiology of both TBI and PTE, enabling mechanistic studies into injury progression, the identification of novel therapeutic targets and biomarkers, the screening of new drugs, and the development of promising diagnostic tools.13 0Item Restricted DYNAMIC REFINEMENT OF SOCKPUPPET DETECTION MODELS WITH HUMAN-IN-THE-LOOP PROCESSES GUIDED BY MACHINE LEARNING ENGINEERING RULES(Saudi Digital Library, 2025) Baamer, Rafeef Abdullah B; Boicu, MihaiIn recent years, people have increasingly relied on Online Social Networks (OSNs) for various aspects of their daily lives, including communication, information sharing, and entertainment. Although these platforms provide many benefits, their massive and continuous use has also caused negative behaviors and malicious activities. One of the most critical challenges is the growing presence of malicious accounts that undermine the trustworthiness and integrity of online interactions and communication. Such accounts include personal spammers, impersonators, and cyborgs. However, one of the most harmful and complex types is the sockpuppet account. Sockpuppet accounts refer to accounts created by an individual or a coordinated group for deceptive or manipulative purposes, such as spreading misinformation or promoting specific agendas. The term encompasses several subtypes, which are impersonation sockpuppets, fake-profile sockpuppets, promotional or antagonistic sockpuppets, misinformation sockpuppets, troll sockpuppets, and spam sockpuppets. These accounts negatively affect OSNs in multiple ways: they reduce the authenticity and integrity of online communication, degrade information quality by disseminating false or biased content, manipulate public opinion by supporting certain agendas or campaigns, and contribute to community disruption and toxicity through hate speech or coordinated harassment. While prior studies have achieved promising results in sockpuppet account detection, several limitations and research gaps remain. First, most existing approaches focus on identifying a specific type of sockpuppet account—such as spammers or fake reviewers—which limits the generalizability of their models. Second, only a few studies have explored or implemented hybrid detection techniques, as most rely on a single methodological approach. Third, many models are tested on a single platform or dataset, which restricts their scalability and cross-platform applicability. Moreover, no prior research has proposed a detection model specifically designed for Arabic sockpuppet accounts. Finally, there has been limited involvement of human expertise and underutilization of Human-in-the-Loop (HITL) analysis in refining and validating detection outcomes. To address these limitations, this dissertation presents three major experiments conducted across Wikipedia, Reddit, and X/Twitter platforms, targeting different categories of sockpuppets—general, troll, and spammer accounts. In these three experiments, various detection approaches were employed, including individual machine learning classifiers, ensemble voting, deep learning, and transformer-based (AraBERT) models, to detect and classify sockpuppet accounts across multiple platforms. These models were subsequently integrated into a Human-in-the-Loop analysis framework to enhance their performance through multiple refinement cycles, identifying and applying machine learning engineering rules (MLE), e.g., mixed-initiative feature optimization, data improvement, and hyperparameter tuning of classifiers. The process involved iterative model tuning and evaluation, resulting in the formulation of MLE rules derived from both model insights and human feedback. This research yields several contributions: it developed generalizable hybrid detection techniques that increased the performance in sockpuppet accounts detection (as measured by accuracy, precision, recall, and F-Score); second, it introduced a validation process for sockpuppet datasets combining transformer-based model for posts labeling and Human-in-the-Loop analysis and review which also resulted in the first Arabic labeled sockpuppet accounts dataset, addressing a major linguistic and cultural gap in existing research; third, it established a systematic approach for identifying borderline cases that require human review and translating these insights into model-refinement and MLE rules to enhance overall detection performance and generalizability; finally, it developed a Human-in-the-Loop process for analysts for model development and dynamic refinement that was tested across multiple datasets representing diverse online platforms and different types of sockpuppet accounts.22 0Item Restricted Machine Learning Systems for Unsupervised Time Series Anomaly Detection(Saudi Digital Library, 2025) Alnegheimish, Sarah; Veeramachaneni, KalyanModern assets – from launched satellites to electric vehicles – output dense, multivariate time series data that must be monitored for deviations from “normal” behavior. This monitoring task is referred to as time series anomaly detection. The current state of the industry still depends on fixed or heuristic thresholds that often drown operators in false alarms, and can miss the subtle, context-dependent faults that matter most. This thesis addresses unsupervised time series anomaly detection as an end-to-end problem, asking how we can learn, evaluate, and deploy models that judiciously flag anomalies while remaining intuitive to the end user. This thesis provides contributions in the form of both algorithms and systems. First, it introduces three models that enlarge the design space of unsupervised time series anomaly detection: TadGAN, which leverages adversarial reconstruction; AER, which unifies predictive and reconstructive objectives in a single hybrid score; and MixedLSTM, which explicitly incorporates interdependencies to improve anomaly detection in multivariate time series. We propose two range-based evaluation metrics that quantify detection quality over temporal intervals. Second, it presents our system Orion, which abstracts anomaly detection pipelines as directed acyclic graphs of reusable primitives, providing user-friendly APIs and enabling interactive visual inspection. Building on this infrastructure, OrionBench performs periodic, fully reproducible benchmarks, producing leaderboards that align research innovations with the needs of end users. Third, the thesis explores a new paradigm – foundation models for unsupervised time series anomaly detection – by formulating SigLLM, which employs large language models and time series foundation models for zero-shot anomaly detection via prompting and forecasting. This paradigm indicates a promising path to developing scalable models for anomaly detection. Finally, beyond evaluating our systems on publicly available datasets, we provide extensive experiments on two industrial case studies that demonstrate improved detection accuracy and practical usability of our system.25 0Item Restricted Evaluating Machine Learning for Intrusion Detection in CAN Bus for in-Vehicle Security(Saudi Digital Library, 2025) Alfardus, Asma; Rawat, DandaThe past decade has seen a potential rise in the automobile industry accompanied by some serious challenges and threats. Increased demand for intelligent transportation system facilities has given a boom to the automotive industry. A safer and better experience is much sought from vehicles. It opens opportunities of including autonomous vehicles and Vehicle to Everything technologies in the automotive sector. Enabling vehicles to connect to various services exposes to compromise and misuse by the adversaries. There are numerous electronic devices in the modern vehicle which communicate with each other using multiple standard communication protocols. State-of-the-art vehicles are the assembly of complex mechanical devices with the sophisticated technology of electronic devices and connections to the external world. Controller Area Network (CAN) is one of the widely used protocols for in-vehicle communications. However, the lack of some fundamental security features such as encryption and authentication in CAN makes it vulnerable to security attacks. The backbone of connecting autonomous vehicles is CAN with limited bandwidth and exposure to unauthorized access. Various attacks compromise the confidentiality, integrity, and availability of vehicular data through intrusions which may endanger the physical safety of vehicles and passengers. These security shortcomings, therefore, lead to accidents and financial loss to the users of vehicles. To protect the in-vehicle electronic devices, researchers have proposed several security countermeasures. In this work, we discuss various security vulnerabilities and potential solutions to CAN’s. Further, a machine learning-based approach is also developed to devise an Intrusion Detection System for the CAN bus network. This study aims to explore the adaptability of the proposed intrusion detection system across diverse vehicular architectures and operational conditions. Furthermore, the findings contribute to advancing the state-ofthe-art in automotive cybersecurity, fostering safer and more resilient transportation ecosystems. Moreover, it investigates the scalability of the intrusion detection system to handle the increasing complexity and volume of data generated by modern vehicles.25 0Item Restricted Towards Industrially Adoptable Generation Invariant Reprocessable Polydicyclopentadiene Thermoset Plastics(Saudi Digital Library, 2025-05) Alfaraj, Yasmeen; Johnson, JeremiahThe industrial transition to sustainable polymer technologies necessitates novel end-of-life approaches for historically un-recyclable thermoset plastics. Polydicyclopentadiene (pDCPD), a high-performance thermoset known for its superior mechanical and thermal properties represents a compelling target for sustainability-oriented innovation due to its established industrial use, diverse manufacturing methods, historic challenges in reprocessing, and an increased interest from its relevant industries to recover valuable fillers and reinforcing materials from pDCPD carbon-fiber-reinforced polymers (CFRPs). Recent reports exhibit the ability to deconstruct pDCPD through a cleavable comonomer (CC) approach; however, we currently lack cost-effective strategies for scaling its deconstruction and recycling. This thesis addresses the fundamental barriers to industrial implementation of deconstructable pDCPD thermosets through a comprehensive, three-pronged approach that integrates data-driven molecular design, drop-in strategies for multigenerational recyclability, and cost-informed evaluation of CCs. In the first part of this work, a closed-loop experimental–computational platform is developed to predict glass transition temperatures (Tg) in deconstructable pDCPD networks incorporating bifunctional silyl ether (BSE) CCs and cleavable cross-linkers. Leveraging a curated dataset of 101 compositionally diverse pDCPD-based thermosets, machine learning model ensembling and strong regularization techniques are implemented to mitigate overfitting and quantify predictive uncertainty. Experimental validation of model predictions shows that the resulting models achieve accurate Tg predictions for variable CC and cleavable cross-linker loadings, novel CCs, and previously unseen related classes of strand cleaving cross-linkers. This chapter demonstrated the viability of predictive informatics in navigating the vast chemical and compositional space of deconstructable thermosets. The second segment presents a minimally chemically intensive, drop-in strategy for pDCPD recyclability. Using cleavable BSE comonomers and cross-linkers, networks with up to 20 wt% recycled oligomeric fragments are synthesized and evaluated. These materials exhibit thermomechanical properties and deconstructability that remain invariant across three generations of recycling. Furthermore, the incorporation of a cleavable cross-linker, dimethyl di-dicyclopentadiene silyl ether (DDMS), not only preserves but enhances bulk properties such as Tg in virgin and recycled samples, and addresses issues of oligomer incorporation in recycled samples as evidenced by gel fraction analysis. The ability to maintain and tune materials properties without post-processing or structural reformulation underscores the industrial potential of the drop-in CC approach for scalable, circular thermoset manufacturing. The final component of the thesis evaluates MeSi7, a seven-membered BSE CC, as a low-cost, synthetically accessible, and possibly scalable alternative to existing CCs. Thermodynamic polymerization parameters and CC performance under industrial thermoset cure conditions are assessed. We find that high-temperature cure conditions enable sufficient incorporation into the pDCPD network strands for deconstruction with as low as 5 mol% loading of MeSi7. These samples retain Tg values above 100 °C, with a moderate reduction relative to non-deconstructable analogues. Assessment of performance in industrial formulations also shows comparable deconstructability thresholds and modest impact on Tg. Importantly, MeSi7 is projected to cost less than 2% of iPrSi8 based on raw material pricing, offering a highly attractive economic profile for broader market applications. Together, these contributions deliver a framework for the rational design, performance prediction, and techno-economic evaluation of cleavable, recyclable thermosets through a convergence of data science, molecular design, and systems-level engineering considerations.10 0Item Restricted SEVERITY GRADING AND EARLY DETECTION OF ALZHEIMER’S DISEASE THROUGH TRANSFER LEARNING(Saudi Digital Library, 2025) Alqahtani, Saeed; Zohdy, MohamedAlzheimer’s disease (AD) is a neurological disorder that predominantly affects individuals aged 65 and older. It is one of the primary causes of dementia, and it contributes significantly and progressively to impairing and destroying brain cells. Recently, efforts to mitigate the impact of AD have focused with particular emphasis on early detection through computer aided diagnosis (CAD) tools. This study aims to develop deep learning models for the early detection and classification of AD cases into four categories: non-demented, moderate-demented, mild-demented, and very mild demented. Using Transfer Learning technique (TL), several models were implemented including AlexNet, ResNet-50, GoogleNet (InceptionV3), and SqueezeNet, by leveraging magnetic resonance images (MRI) and applying image augmentation techniques. A total of 12,800 images across the four classifications that were preprocessed to ensure balance and meet the specific requirements of each model. The dataset was split into 80% for training and 20% for testing. AlexNet achieved an average accuracy of 98.05%, GoogleNet (InceptionV3) reached 97.80%, ResNet-50 attained 91.11%, and SqueezeNet 86.37%. The use of transfer learning method addresses data limitations, allowing effective model training without the need for building from scratch, thereby enhancing the potential for early and accurate diagnosis of Alzheimer’s disease [1].17 0Item Restricted OPTIMIZING INTRUSION DETECTION IN IOT NETWORK ENVIRONMENTS THROUGH DIVERSE DETECTION TECHNIQUES(Florida Atlantic University, 2025-03-11) Al Hanif, Abdulelah; Ilyas, MohammadThe rapid proliferation of Internet of Things (IoT) environments has revolutionized numerous areas by facilitating connectivity, automation, and efficient data transfer. However, the widespread adoption of these devices poses significant security risks. This is primarily due to insufficient security measures within the devices and inherent weaknesses in several communication network protocols, such as the Message Queuing Telemetry Transport (MQTT) protocol. MQTT is recognized for its lightweight and efficient machine-to-machine communication characteristics in IoT environments. However, this flexibility also makes it susceptible to significant security vulnerabilities that can be exploited. It is necessary to counter and identify these risks and protect IoT network systems by developing effective intrusion detection systems (IDS) to detect attacks with high accuracy. This dissertation addresses these challenges through several vital contributions. The first approach concentrates on improving IoT traffic detection efficiency by utilizing a balanced binary MQTT dataset. This involves effective feature engineering to select the most important features and implementing appropriate machine learning methods to enhance security and identify attacks on MQTT traffic. This includes using various evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, demonstrating excellent performance in every metric. Moreover, another approach focuses on detecting specific attacks, such as DoS and brute force, through feature engineering to select the most important features. It applies supervised machine learning methods, including Random Forest, Decision Trees, k-Nearest Neighbors, and Xtreme Gradient Boosting, combined with ensemble classifiers such as stacking, voting, and bagging. This results in high detection accuracy, demonstrating its effectiveness in securing IoT networks within MQTT traffic. Additionally, the dissertation presents a real-time IDS for IoT attacks using the voting classifier ensemble technique within the spark framework, employing the real-time IoT 2022 dataset for model training and evaluation to classify network traffic as normal or abnormal. The voting classifier achieves exceptionally high accuracy in real-time, with a rapid detection time, underscoring its efficiency in detecting IoT attacks. Through the analysis of these approaches and their outcomes, the dissertation highlights the significance of employing machine learning techniques and demonstrates how advanced algorithms and metrics can enhance the security and detection efficiency of general IoT network traffic and MQTT protocol network traffic.38 0Item Restricted Automating the Resolutions for Software Merge Conflicts(Virginia Polytechnic Institute and State University, 2024-11-22) Aldndni, Waad; Meng, Na; Servant, FranciscoDuring collaborative software development, developers engage in parallel work on separate branches, which are eventually merged at regular intervals. However, conflicts can arise when edits from different branches overlap in the text. Resolving such conflicts involves three strategies: keeping the local version only (KL), keeping the remote version only (KR), or manually editing them (ME). Nonetheless, manually resolving merge conflicts can be a laborious and error-prone process. Thus, researchers proposed tools to aid in conflict resolution by combining edits from both branches as many as possible, although these tools often fail to consider the preferences of the developers involved adequately. Recent studies show that developers predominantly resolve textual conflicts via KL or KR. This suggests that existing tools do not fully consider the resolution preferences of developers but only focus on the technical feasibility of merging branch edits. Our research focuses on predicting developers’ resolutions automatically for software merge conflicts and suggesting resolution edits to developers. We designed and implemented three tools to automatically predict resolution strategies for merge conflicts and to automatically apply some of the strategies by producing merged versions. The tool evaluation shows promising results. Our research will help developers resolve conflicts effectively and efficiently; it will also shed light on future research for software merge and automatic conflict resolution.21 0Item Restricted Behavior and Design of Composite Rebars Interfaced with Concrete(university of colorado Denver, 2024) Alatify, Ali; Kim, JimmyAbstract This dissertation studies different aspects of the interfacial behavior of composite reinforcement embedded in concrete. GFRP rebars are known for its none-corrosiveness, light weight, and high strength compared to conventional steel rebars, and became predominantly employed in different structural applications such as bridge construction. Thus, the serviceability and interfacial behavior of GFRP bars in different structural applications is investigated in four phases in this research. Chapter three presents an experimental study on the residual bond of glass fiber reinforced polymer (GFRP) rebars embedded in ultra-high performance concrete (UHPC) subjected to elevated temperatures, including a comparison with ordinary concrete. Based on the range of thermal loading from 25oC (77oF) to 300oC (572oF), material and push-out tests are conducted to examine the temperature-dependent properties of the constituents and the behavior of the interface. Also performed are chemical and radiometric analyses. The average specific heat and thermal conductivity of UHPC are 12.1% and 6.1% higher than those of the ordinary concrete, respectively. The temperature-induced reduction of density in these mixtures ranges between 5.4% and 6.2% at 300oC (572oF). Thermal damage to GFRP, in the context of microcracking, is observed after exposure to 150°C (302°F). Fourier transform infrared spectroscopy reveals prominent wavenumbers at 668 cm-1 (263 in.-1) and 2,360 cm-1 (929 in.-1), related to the bond between the fibers and resin in the rebars, while spectroradiometry characterizes the thermal degradation of GFRP through diminished reflectivity in conjunction with the peak wavelength positions of 584 nm (2,299×10-8 in.) and 1,871 nm (7,366×10-8 in.). The linearly ascending bond-slip response of the interface alters after reaching the maximum shear stresses, leading to gradual and abrupt declines for the ordinary concrete and UHPC, respectively. The failure mode of the ordinary concrete interface is temperature-sensitive; however, spalling in the bonded region is consistently noticed in the UHPC interface. The fracture energy of the interface with UHPC exceeds that of the interface with the ordinary concrete beyond 150oC (302oF). Design recommendations are provided for estimating reductions in the residual bond of the GFRP system exposed to elevated temperatures. The interface shear between ordinary concrete and ultra-high-performance concrete (UHPC) connected with glass fiber reinforced polymer (GFRP) rebars is presented in chapter four. Following ancillary tests on the fracture of the rebars under in-plane shear loading, concrete-rebar assemblies are loaded to examine capacities and failure modes that are dependent upon the size, spacing, and number of the rebars. While the transition of load-resisting axes in the glass fibers and their quantity dominates the shear behavior of the bare rebars, the size and spacing of the reinforcement control the capacities of the interface by altering load-transfer mechanisms from the rebar to the concrete. The degree of stress distribution affects the load-displacement response of the interface, which is characterized in terms of quasi-steady, kinetic, and failure regions. The primary failure modes of the interface comprise rebar rupture and concrete splitting. The formation of cracks between ordinary concrete and UHPC results from interfacial deformations, leading to spalling damage when applied loads exceed service levels. An analytical model is formulated alongside an optimization technique. The capacities of the interface in relation to the rebar rupture and concrete splitting failure modes are predicted. Furthermore, a machine learning algorithm is utilized to define a failure envelope and propose practice guidelines through parametric investigations. The serviceability of concrete beams with continuous and spliced glass fiber reinforced polymer (GFRP) rebars is investigated and detailed in chapter five. An experimental program is undertaken using 18 beams incorporating various reinforcing schemes to examine the effects of rebar distribution and spacing on flexural and cracking responses. The cracking load of the beams with the continuous rebars (Category C) is 24.2% higher than that of the beams with the spliced rebars (Category S) experiencing stress concentrations. The distributed configuration of the rebars enhances interactions between the concrete and reinforcement, thereby increasing bond transfer in the beams. Contrary to the linear load-displacement behavior of the C-category beams after cracking, parabolic trends are observed in the S-category beams owing to the slip of the spliced rebars, which degrades composite action at the rebar-concrete interface and reduces the flexural rigidity of the beams. The crack opening of the C-category beams under service loading is within the tolerable limits of published guidelines, whereas the opening of the S-category beams exceeds the limits. Through statistical characterization, the significance of the rebar distribution in crack opening and depth is demonstrated at a 5% significance level (95% confidence interval). Design recommendations include a slip multiplier of 0.63 for calculating the stress of spliced GFRP rebars and a bond coefficient of 0.88 for determining the flexural capacity of beams with this type of reinforcement. The implications of variable bond for the behavior of concrete beams with glass fiber reinforced polymer (GFRP) bars alongside shear-span-dependent load-bearing mechanisms is evaluated in chapter six. Experimental programs are undertaken to examine element- and structural-level responses incorporating fully and partially bonded rebars, which are intended to represent sequential bond damage. Conforming to published literature, three shear-span-to-depth (av/d) ratios are considered: arch action (av/d < 2.0), beam action (3.5 ≤ av/d), and a transition from arch to beam actions (2.0 ≤ av/d < 3.5). When sufficient bond is provided for the element-level testing (over 75% of 5db, where db is the rebar diameter), the interfacial failure of GFRP is brittle against a concrete substrate. An increase in the shear-span-to-depth ratio, aligning with a change from arch action to beam action, decreases the load-carrying capacity of the beams and the slippage of the partially bonded rebars dominates their flexural stiffness. Compared with the case of beams under beam action, the mutual dependency of the bond length and shear span is apparent for those under arch action. As far as failure characteristics are concerned, the absence of bond in the arch-action beam prompts crack localization; by contrast, partially bonded ones demonstrate diagonal tension cracking adjacent to the compression strut that transmits applied load to the nearby support. The developmental process of rebar stress is dependent upon the shear-span-to-depth ratios and, in terms of utilizing the strength of GFRP, beam action is favorable relative to arch action. Analytical modeling suggests design recommendations, including degradation factors for the calculation of rebar stresses with bond damage when subjected to arch and beam actions.37 0Item Embargo ENHANCING LOCATION INFORMATION PRIVACY AND SECURITY IN IoBT USING DECEPTION-BASED TECHNIQUES(Florida Atlantic Uniiversity, 2024-09) Alkanjr, Basmh; Imadeldin, MahgoubIoBT stands for the Internet of Battlefield Things. This concept extends the principles of the Internet of Things (IoT) for military and defense use. IoBT integrates smart devices, sensors, and technology on the battlefield to improve situational awareness, communication, and decision-making in military operations. Sensitive military data typically includes information crucial to national security, such as the location of soldiers and equipment. Unauthorized access to location data may compromise operational confidentiality and impede the element of surprise in military operations. Therefore, ensuring the security of location data is crucial for the success and efficiency of military operations. We propose two systems to address this issue. First, we propose a novel deception-based scheme to enhance the location-information security of IoBT nodes. The proposed scheme uses a novel encryption method, dummy IDs, and dummy packets technology. We develop a mathematical model to evaluate our scheme in terms of safety time (ST), probability of failure (PF), and the probability of identifying the real packet in each location information update (PIRP). Then, we develop NetLogo simulations to validate the mathematical model. The proposed scheme increases ST, reduces PF and PIRP. We develop a scheme to protect the node's identity using dummy ID, silence period, and sensitive area’s location privacy enhancement concepts. We generate a pseudonym location for each node in the IoBT environment to protect the node's real location information. We propose a new metric called the average probability of linkability per dummy ID (DID) change to assess the attacker's effectiveness in linking the source node with its new DID following the silent period. We develop Matlab simulations to evaluate our scheme in terms of average anonymity and average probability of linkability per DID change. The results showed further privacy enhancement by applying the sensitive area concept. Tampering with location information, such as falsification attacks, can lead to inaccurate battlefield assessments and personnel safety risks. Thus, we design ANFIS and ensemble methods for detecting position falsification attacks in IoBT. Using the VeReMi dataset, our method achieved high detection accuracy while reducing false negative rate and computation complexity. Cross-validation further supports the reliability of our model.38 0
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