MD MAHBUBUR RAHMAN

Unlike earlier forms of disinformation that were primarily text-based, modern rumors often combine both text and images to construct compelling but deceptive stories. Detecting such multimodal rumors presents new challenges, as it requires understanding not just each piece of content in isolation, but how they work together to mislead. Traditional rumor detection models aim to identify such content by relying on textual cues or employing shallow multimodal fusion techniques to align modalities of information. However, these approaches often struggle to capture the complex semantic interplay between modalities, especially when faced with semantic discrepancies or emotionally enforced visual cues. This is due to their inability to effectively model the subtle semantic variation, hierarchical, and non-linear relationships that exist between different modalities. Despite recent advances, existing rumor detection models face two critical challenges: (1) Existing methods often treat semantic discrepancies or ambiguities as noise during the fusion of visual and textual modalities. This neglect causes the models to overlook essential cross-modal interdependencies, ultimately leading to poor performance. (2) Most approaches assume a one-to-one correspondence between visual and linguistic features, ignoring the richer hierarchical relationships that bridge different semantic levels across modalities. As a result, models often fail to capture the layered structure of meaning and overlook important hierarchical cues. To address these challenges, this study presents the following key contributions: (1) The study introduces RADAR, a framework that treats semantic discrepancies not as noise to be reduced or ignored, but as valuable signals that can enhance detection. RADAR focuses on amplifying them by incorporating three modules: First, the Semantic Discrepancy Amplifier (SDA), a multi-head attention mechanism that dynamically detects and amplifies mismatches between modalities. Second, these differences are improved using Progressive Feature Purification (PFP), which adjusts filters to remove unnecessary noise while keeping important signals. Third, the Feature Feedback Loop (FFL) continuously improves the quality of the features. Together, these components form a pipeline that effectively highlights subtle inconsistencies often found in rumor content. RADAR achieves state-of-the-art results, with 96.0\% accuracy on the Twitter dataset and 91.2\% on Weibo, effectively detecting both rumors and non-rumors by amplifying semantic discrepancies. (2) This paper proposes the RumorCone framework that incorporates semantic hierarchies using hyperbolic geometry to preserve hierarchical, non-linear relationships, rather than representing them at a flat semantic level. It decomposes both image and text content into three levels: base, mid, and high-level abstractions. First, it places these different types of information into a hyperbolic space to preserve their natural tree-like shape and show how they interact non-linearly. Second, the model uses cross-modal hyperbolic attention, which focuses on important features from each modality at every level while maintaining consistency across modalities. Additionally, hierarchical semantic processing ensures that text and image features are aligned at each level of abstraction, further enhancing the model's ability to identify misalignments. Third, it uses a flexible fusion method that dynamically adjusts the contribution of each modality based on its alignment. RumorCone achieved improved performance 86.76\% F1-score on MR²-E and 85.48\% on MR²-C by utilizing hierarchical reasoning in hyperbolic space. The study further evaluates through cross-lingual experiments, gathering 100 recent social media posts in five languages and including various scenarios across multiple platforms. It achieved 83\% accuracy on high-resource languages such as English and Chinese; performance declines to 68\% for Bengali and Hindi due to translation-induced semantic shifts, with baselines performing even worse. The research demonstrated that semantic discrepancies serve as significant indicators of rumor content, while hierarchical semantic reasoning enhances the accuracy of content-based detection. Cross-lingual evaluation revealed the necessity for datasets covering low-resource languages.

Traditional rumor detection models attempt to identify such content by relying on textual cues or employing shallow multimodal fusion techniques. However, these methods often assume a simplistic one-to-one alignment between modalities, overlooking the richer hierarchical relationships across modalities, failing to capture the layered structure of meaning. In this paper, we present RumorCone, a novel method that employs hyperbolic geometry in order to preserve hierarchical, non-linear relationships, rather than representing them at a flat semantic level. First, RumorCone decomposes image and text content into three levels: base, mid, and high-level abstractions, and embeds them in hyperbolic space to model their tree-like semantic structure. Second, a dynamic hyperbolic multimodal attention mechanism aligns features across modalities and levels, and a flexible fusion strategy adjusts the contribution of each modality based on alignment quality. Our experiments indicate the importance of hierarchical semantic modeling for robust and interpretable multimodal rumor detection.

Traditional intrusion detection systems (IDS) using classifiers face challenges such as model overfitting, incomplete feature extraction, andhighfalse positive rates, limiting their effectiveness in real-world deployments. Toaddress these challenges, this study proposes a robust multiclass machine learn ing based intrusion detection framework. The methodology integrates advanced feature selection techniques to identify critical attributes, mitigating redundancy and enhancing detection accuracy. Two distinct ML architectures are imple mented: a baseline classifier pipeline and a stacked ensemble model combining noise injection, Principal Component Analysis (PCA), and meta learning to im prove generalization and reduce false positives. Evaluated on the AWID3 dataset, the proposed ensemble architecture achieves superior performance, with an ac curacy of 98%, precision of 98%, recall of 98%, and a false positive rate of just 2%, outperforming existing state-of-the-art methods. This work demonstrates the efficacy of combining preprocessing strategies with ensemble learning to fortify network security against sophisticated Wi-Fi attacks, offering a scalable and reli able solution for IoTenvironments. Futuredirectionsincludereal-timedeployment and adversarial resilience testing to further enhance the model’s adaptability.