A wireless network gives flexibility to the user in terms of mobility that attracts the user to use wireless communication more. The video communication in the wireless network experiences Quality of Services (QoS) and Quality of Experience (QoE) issues due to network dynamics. The parameters, such as node mobility, routing protocols, and distance between the nodes, play a major role in the quality of video communication. Scalable Video Coding (SVC) is an extension to H.264 Advanced Video Coding (AVC), allows partial removal of layers, and generates a valid adapted bit-stream. This adaptation feature enables the streaming of video data over a wireless network to meet the availability of the resources. The video adaptation is a dynamic process and requires prior knowledge to decide the adaptation parameter for extraction of the video levels. This research work aims at building the adaptation parameters that are required by the adaptation engines, such as Media Aware Network Elements (MANE), to perform adaptation on-the-fly. The prior knowledge improves the performances of the adaptation engines and gives the improved quality of the video communication. The unique feature of this work is that, here, we used an experimental evaluation method to identify the video levels that are suitable for a given network condition. In this paper, we estimated the adaptation parameters for streaming scalable video over the wireless network using the experimental method. The adaptation parameters are derived using node mobility, link bandwidth, and motion level of video sequences as deciding parameters. The experimentation is carried on the OMNeT++ tool, and Joint Scalable Video Module (JSVM) is used to encode and decode the scalable video data.

As adversaries refine their techniques to avoid detection, Advanced Persistent Threats (APTs) are increasingly exploring unconventional mechanisms to maintain stealth. This paper introduces a novel approach that leverages Digital Rights Management (DRM) as an anti-analysis shield to prevent reverse engineering and forensic investigation. By binding malware execution to a specific machine through DRM fingerprinting, sandbox, and virtual-machine analysis are effectively thwarted. To harden this mechanism, anti-debugging via Thread Local Storage (TLS) callbacks is implemented to detect debuggers before the main execution begins, rendering any debugging attempt futile by nullifying the malware’s main function. The paper presents a proof-of-concept in the form of a DLL injector, showcasing a stealthy persistence mechanism and anti-analysis measures. To further complicate analysis, Import Address Table (IAT) camouflage introduces whitelisted API noise, misleading defenders and evading static detection. While the current implementation is a minimal loader and persistence mechanism, the techniques outlined here provide a blueprint for more sophisticated operations by advanced threat actors. This paper aims to highlight a unique attack chain that could be utilized to essentially nullify modern defensive mechanisms.

Sarcoidosis, one of the rarest diseases, is challenging to diagnose as it mimics the symptoms of other diseases. Machine learning algorithms identify hidden patterns among symptoms, making them suitable for early diagnosis of Sarcoidosis. In this study, four ensemble models are developed using baseline classifiers and applied to a symptom-based secondary dataset to explore the hidden information. The dataset comprises 189 patient records with 14 attributes: 2 serum markers, 10 symptoms, the patient’s gender, and 1 target variable. An exploratory data analysis is carried out using necessary preprocessing techniques, including missing value imputation and data scaling. The features are selected using PCA, and the relevance of the features is analyzed using the Chi-Square Test, Mutual Information, Sequential Feature Selection, and Tree-Based Selection methods. CTGAN, a GenAI technique, is used to augment the dataset, as it contains only 189 records. CTGAN preserves the clinical fidelity of all the features pertaining to the diagnosis of Sarcoidosis, ensuring synthetic data retains meaningful diagnostic patterns. The performance of the models developed is evaluated by applying them to both the original and synthetic data. Results demonstrate that proposed ensemble methods, Model Combinations 1, 3, and 4, showed 99.47% accuracy on the original dataset, whereas Model Combination 1 and Random Forest classifier showed 85.19% and 60.78% accuracies on a combination of the original with 81 synthetic and 1000 synthetic data, respectively. This highlights the combined advantage of CTGAN-based augmentation and ensemble learning in enhancing diagnostic modeling for rare diseases like Sarcoidosis where the datasets are available with limited data points.