There has been an enormous increase in information flow and communication data due to the rapid rise in the number of Internet of Everything (IoE) devices and the development of cutting-edge technologies such as the rollout of the Sixth Generation (6G) network. The rising and inevitable off-loading requirements of IoE devices have resulted in an unprecedented increase in the reliance on edge and cloud paradigms. However, such a reliance on far-end technologies to access already scarce resources can often result in increased latency and unstable connection issues due to limited bandwidth. In this paper, we investigate the solution for such a stringent network design by presenting a conceptual cloud architecture based on key components such as resource allocation, scheduling and task off-loading for IoE devices. The IoE devices utilise a scheduler to access resources from nearby higher resourced IoE devices for their task computation, where the scheduler allocates incoming requests according to the availability of resources within a cluster of devices or to other devices in nearby clusters. Motivated by these design characteristics, we propose a design of a novel Main Task Off-loading Scheduling Algorithm (MTOSA) for efficient task allocation and dissemination. We present a theoretical analysis of five different scheduling policies namely Round Robin (RR), Strongest Channel (SC), Max Rate (MR), Proportional Fair (PF) and Priority Base (PB) scheduling to find an optimal technique for task off-loading in futuristic networks. Furthermore, we compare the performance of these five scheduling policies with the two existing scheduling policies from the literature. It is shown through various experiments that the proposed MTOSA algorithm performs better when compared with the existing schemes for different performance parameters.

Network forensics is commonly used to identify and analyse evidence of any illegal or unauthorised activity in a given network. The collected information can be used for preventive measures against potential cyber attacks and serve as evidence acceptable in legal proceedings. Several conventional tools and techniques are available to identify and collect such pieces of evidence; however, most of them require expensive commercial resources, longer investigation times, and costly human expertise. Due to modern networks’ diverse and heterogeneous nature, forensic operations through conventional means become a cumbersome and challenging process. This calls for a new look at how network forensics is approached, considering contemporary approaches to network analysis. In this work, we explore artificial intelligence (AI) techniques based on contemporary machine learning (ML) algorithms such as deep learning (DL) to conduct network forensics. We also propose an investigation model based on AI/ML techniques that can analyse network traffic and behavioural patterns to identify any prior or potential cyber attacks. The proposed AI-based network forensics model speeds up the investigation process, boosting network monitoring without human intervention. This also aims to provide timely and accurate information to network administrators for quick and effective decisions, enabling them to avoid and circumvent future cyber attacks.