A Novel Private Cloud Document Archival System Architecture Based on ICmetrics

This paper proposes a novel architecture that offers features for a truly interactive yet private document archival cloud. The proposed secure document archival system has been specifically designed for the elimination of paper based systems in organization. The architecture attempts to shift paper based systems to an electronic archival system where the documents are secure and the system is supportive to user demands and features. Therefore besides document archival the proposed architecture also offers the authentication and encryption for secure keeping of data on the cloud. We propose a novel combination of SSL coupled with ICMetrics for the document archival system, which will prove to be a very valuable tool for encrypting and authenticating in the cloud world

SortAlgo-Metrics: Identification of Cloud-Based Server Via a Simple Algorithmic Analysis

This paper introduces a novel technique to detect spoof or fake software systems via the generation of a unique digital signature based on a direct analysis of the construction of the system. Specifically, we model a novel mechanism referred to as SortAlgo-Metrics analysis to identify cloud-based servers. Experimentally, we deployed four cloud-based servers to run four sorting algorithms in order to extract features that are employed to perform statistical analysis upon with the aim to obtain their metrics which has further underpin the investigation of their behaviours. The model has been validated by comparing training data and unknown data, and the result has shown server 2-4 have a strong identification with 96% probability, while server 1 with 55%, it is surmised that is could be as the result of insufficient sample data. However, if such a simple model can produce a result with this high probability, this shows that with more complex features and sufficient data pulled from cloud-based servers, SortAlgo-Metrics model could generate a higher degree of basis numbers for ICMetrics technology entropy key generation and other complex systems

Secure device identification using multidimensional mapping

In this paper we investigate several potential hardware features from multiple devices for suitability during the employment of a device identification. The generation of stable and unique digital identity from features is challenging in device identification because of the unstable operation environments that implies the features employed are likely to vary under normal operating conditions. To address this, we introduce a novel multi-dimensional key generation technology which maps from multi-dimensional feature space directly to a key space. Furthermore, normalized distributions of features give the necessary data to model the characteristics, from which we derive intra-sample device feature distributions, and correlate the distinct features to generate a secure key to identify the device. Furthermore, to evaluate our experiment, we considerably carried out measurement using the mathematical & statistical modelling

Robust Device Authentication Using Non-Standard Classification Features

This paper investigates the use of novel hardware features derived from the physical and behavioral characteristics of electronic devices to identify such devices uniquely. Importantly, the features examined exhibit non-standard and multimodal distributions which present a significant challenge to model and characterize. Specifically, the potency of four data classification methods is compared whilst employing such characteristics, proposed model Multivariate Gaussian Distribution (MVGD -address multimodality), Logistic Regression (LogR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM). Performance is measured based on its accuracy, precision, recall and f measure. The experimental results reveal that by addressing multimodal features with proposed model Multivariate Gaussian Distribution classifier, the overall performance is better than the other classifiers

A Scheme for the Generation of Strong Cryptographic Key Pairs based on ICMetrics

This paper presents a scheme for the generation of strong high entropy keys based on ICMetrics. ICMetrics generates the security attributes of the sensor node based on measurable hardware and software characteristics of the integrated circuit. This work is based on key derivation functions to derive cryptographic key pairs from ICMetrics values. The proposed ICMetrics based key derivation function makes use of ICMetrics basis numbers and authentication tokens from the trusted third party to generate high entropy public/private key pairs. The proposed approach makes use of key stretching using SHA-2 and performs multiple iterations of the proposed key derivation function to generate strong high entropy keys of sufficient length, so as to prevent exhaustive search attacks. The novelty of this work lies in the fact that the entire key generation scheme has been designed keeping in mind the construction principles of ICMetrics, which does not store keys but computes these for every session based on ICMetrics value, therefore use of a random value anywhere in the protocol will compromise the purpose of ICMetrics. The proposed scheme generates high entropy key pairs while concealing the original ICMetrics data, such that it is impossible to recover the ICMetrics basis data in the system