The increasingly advanced forms of cyber-attacks have highlighted the importance of advanced threat hunting as a necessary skillset. The current research examines the effectiveness of using Elasticsearch, Kibana, and Lucene for an intelligence-driven threat hunting to identify attack infrastructure or a Command & Control (C2) server. By aggregating all system traffic logs and security artifacts into a single data lake/warehouse, organizations are able to leverage centralized analysis of information from different sources on a corporate scale. Utilizing Kibana’s ability to perform network and log analysis, using Lucene’s rich syntax to make sophisticated queries will empower individuals to make valuable findings from log and network traffic logs that identify behaviours and patterns typical of C2 activities. A novel intelligence-based threat hunting approach is presented here that utilizes Elasticsearch, with domain-specific language additions to refine search queries and investigate for C2 related activity. A detailed analysis of the research based on real-world datasets is conducted to evaluation the threat hunting framework’s abilities in detecting C2 servers and minimize true/false positives in relation to organizational security concerns.

The convergence of data security and operational efficiency across various sectors, such as manufacturing, industry, logistics, agriculture, healthcare, and internet services, has been significantly enhanced using robotic-driven platforms and protocols. Notably, there has been a notable uptick in sophisticated cyberattacks targeting corporate and industrial robotic systems. These attacks are activated following the integration of the Internet of Things, the Internet, and organizational networks, as industrial units are interconnected. This study has formulated security-oriented criteria-based indicators for cyber-physical systems (CPS), encompassing industrial components and embedded sensors responsible for processing information logs and procedures. In this research, a robust security framework based on attack trees has been introduced, strategically focusing on addressing critical exploitable vulnerabilities rather than attempting to cover all CPS devices comprehensively. The systematic categorization of each physical device and its associated integrated sensors has been accomplished via data from logs and an information repository contained within a sensor index device library.

Artificial intelligence and machine learning have been transforming the health care industry in many areas such as disease diagnosis with medical imaging, surgical robots, and maximizing hospital efficiency. The Healthcare service market utilizing Artificial Intelligence is expected to reach 45.2 billion U. S. Dollars by 2026 from its current valuation, off $4.9 billion. Diabetic Retinopathy (DR) is a disease that results from complications of type one and Type two diabetes and affects patients' eyes. Diabetic retinopathy, if remains unaddressed, is one of the most serious complications of diabetes, resulting in permanent blindness. The disease has been affecting the lives of 347 million people worldwide. The paper aims to propose a residual network-based deep learning framework for the detection of diabetic retinopathy. The accuracy of our approach is 83% whereas the precision value for checking the absence of DR is 95%.

Summary Recently, there has been a massive rise in the popularity of Instagram, which connects individuals globally and allows videos and images to be uploaded and exchanged, and communicated over social media. Instagram is also an online playground of deceit. The use of filters, lighting, and cunning angles transforms the mundane into something spectacular. Automated spam accounts and fake profiles use this to their malicious advantage for executing attacks targeting high‐profile executives. Creating fake Instagram identities is easy to reproduce the idea of being accepted by many fans on social media. Fake accounts are used in the marketing of fake services and products. This research focused on designing and training a unique neural network model and proposed a new algorithm for detecting automated spam and fake Instagram account profiles. The precision and accuracy of the proposed method were achieved at 93% and 91%, respectively.