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.

Abstract The Internet of Things (IoT) are standard inter connected devices aimed at join everyday object to the internet. This ecosystem include manufacturing, agriculture, smart cities, industry, as well as healthcare. The capacity of controlling and monitoring the objects of the physical world using IoT generate numerous opportunities. However some extra cost is also added to make the device globally accessible. The aggressive growth of the IoT devices, the multifariousness of IoT network technology, and the diversity of IoT use cases generate a question mark regarding the sustainability of the IoT. The aim of the proposed work to contribute in this regard, for that a dynamic integration of IoT objects that is pre determined for (i) creating an IoT platform dynamically (ii) monitoring the current status of IoT environment (iii) measuring the quality of the overall system (iv) helping to utilize all the interconnected efficiently by adding M2M communication, is introduced. Some property set which is suitable for decentralized IoT platform is also explained. Such types of dynamic IoT platform helps in every IoT application domain including industrial IoT. With the propound research, the aim is to create a dynamic IoT platform to simplify the production of next generation. The primary contribution of the proposed paper is a concept which can help to design IoT device in a faster way, which can be called rapid hardware development approach. Efficiently used human resources, that means any one having common technical knowledge can design the device, and reduce the hardware heterogeneous architecture.

Abstract Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum Software Project Success Prediction Model (AQSSPM). Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Utilizing GA with NBC, project success probability improved from 53.17 to 99.68%, with cost reductions from 0.463 to 0.403%. Similarly, GA with LR increased success rates from 55.52 to 98.99%, and costs decreased from 0.496 to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs = 0.955) in causes ranking, with no significant difference between them ( t = 1.195, p = 0.240 > 0.05). The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project.