No abstract

With the pervasive usage of sensing systems and IoT things, the importance of security has increased. Attempts towards breaching IoT security systems by attackers are on upsurge. Many intrusions in embedded systems, sensing equipment and IoT things have occurred in the past. Though there are cyber security tools like Antivirus, Intrusion detection and prevention systems available for securing the digital devices and its networks. However, a forensic methodology to be followed for the analysis and investigation to detect origin cause of network incidents is lacking. This paper derives a comprehensive preventive cyber forensic process model with honeypots for the digital IoT investigation process which is formal, that can assist in the court of law in defining the reliability of the investigative process. One year data of various attacks to the IoT network has been recorded by the honeypots for this study. The newly derived model HIM has been validated using various methods and instead of converging on a particular aspect of investigation, it details the entire lifecycle of IoT forensic investigation. The model is targeted to address the forensic analysts’ requirements and the need of legal fraternity for a forensic model. The process model follows a preventive method which reduce further attacks on network.

No abstract

Virtual Machine (VM) consolidation is a crucial process in improving the utilization of the resource in cloud computing services. As the cloud data centers consume high electrical power, the operational costs and carbon dioxide releases increases. The inefficient usage of the resources is the main reason for these problems and VM consolidation is a viable solution. VM consolidation includes host overload/under-load detection, VM selection and VM placement processes. Most existing host overload/under-load detection approaches of VM consolidation uses CPU utilization only for the determining host load. In this paper, three resources namely CPU utilization, memory utilization and bandwidth utilization are used for host overload detection and an adaptive regression based model called Multiple Regression Multi-Objective Seven-Spot Ladybird Optimization (MR-MOSLO) is proposed. This model is based on combining the benefits of adaptive threshold based and regression based host overload detection algorithms. This approach of combining these features provide more advantages for threshold setting in dynamic environments with accurate prediction of host overloading. For this purpose, initially, Multiple Regression (MR) algorithm is used which relay on CPU utilization, memory utilization and bandwidth utilization for estimation of the host load conditions. Then a Multi-Objective Seven-Spot Ladybird Optimization (MOSLO) algorithm is introduced to select the upper and lower threshold limits for host utilization. Based on these algorithms, the host overload/underload is detected with high accuracy and less power consumption. The simulations are conducted in CloudSim tool and the empirical results shows that the proposed MR-MOSLO algorithm detects the host overload efficiently. The results obtained for 25 hosts, 30 VMs and 500 tasks, are: SLATAH is 20.0434, PDM is 8.7E-4, SLAV is 3.7E-5 and ESV is 10.962 which are lesser than the other methods. Though the energy of 15.4 kWh and SLA of 0.00757 are negligibly higher than some of the existing methods, the proposed approach provided comparatively better performance.