An Information Systems Security Risk Assessment Model Under Dempster- Schafer Theory of Belief Functions

This is the author's final draft. The publisher's official version is available from:.This study develops an alternative methodology for the risk analysis of information systems security (ISS), an evidential reasoning approach under the Dempster-Shafer theory of belief functions. The approach has the following important dimensions. First, the evidential reasoning approach provides a rigorous, structured manner to incorporate relevant ISS risk factors, related counter measures and their interrelationships when estimating ISS risk. Secondly, the methodology employs the belief function definition of risk, that is, ISS risk is the plausibility of information system security failures. The proposed approach has other appealing features, such as facilitating cost-benefit analyses to help promote efficient ISS risk management. The paper both elaborates the theoretical concepts and provides operational guidance for implementing the method. The method is illustrated using a hypothetical example from the perspective of management and a real-world example from the perspective of external assurance providers. Sensitivity analyses are performed to evaluate the impact of important parameters on the model’s results

Evaluation of IoT-Based Computational Intelligence Tools for DNA Sequence Analysis in Bioinformatics

In contemporary age, Computational Intelligence (CI) performs an essential role in the interpretation of big biological data considering that it could provide all of the molecular biology and DNA sequencing computations. For this purpose, many researchers have attempted to implement different tools in this field and have competed aggressively. Hence, determining the best of them among the enormous number of available tools is not an easy task, selecting the one which accomplishes big data in the concise time and with no error can significantly improve the scientist's contribution in the bioinformatics field. This study uses different analysis and methods such as Fuzzy, Dempster-Shafer, Murphy and Entropy Shannon to provide the most significant and reliable evaluation of IoT-based computational intelligence tools for DNA sequence analysis. The outcomes of this study can be advantageous to the bioinformatics community, researchers and experts in big biological data

An Introduction to Evidential Reasoning for Decision Making under Uncertainty: Bayesian and Belief Functions Perspectives

The main purpose of this article is to introduce the evidential reasoning approach, a research methodology, for decision making under uncertainty. Bayesian framework and Dempster-Shafer theory of belief functions are used to model uncertainties in the decision problem. We first introduce the basics of the DS theory and then discuss the evidential reasoning approach and related concepts. Next, we demonstrate how specific decision models can be developed from the basic evidential diagrams under the two frameworks. It is interesting to note that it is quite efficient to develop Bayesian models of the decision problems using the evidential reasoning approach compared to using the ladder diagram approach as used in the auditing literature. In addition, we compare the decision models developed in this paper with similar models developed in the literature

The improvement of uncertainty measurements accuracy in sensor networks based on fuzzy dempster-shafer theory

Threat Assessment is one of the most important components in combat management systems. However, uncertainty is one of the problems that occur in the input data of these systems that have been provided using several sensors in sensor networks. In literature, there are some theories that state and model uncertainty in the information. One of the new methods is the Fuzzy Dempster-Shafer Theory. In this paper, a model-based uncertainty is presented in the air defense system based on the Fuzzy Dempster-Shafer Theory to measure uncertainty and its accuracy. This model uses the two concepts naming of the Fuzzy Sets Theory, and the Dempster-Shafer Theory. The input parameters to sensors are fuzzy membership functions, and the basic probability assignment values are earned from the Dempster-Shafer Theory. Therefore, in this paper, the combination of two methods has been used to calculate uncertainty in the air defense system. By using these methods and the output of the Dempster-Shafer theory are calculated and presented the uncertainty diagrams. The advantage of the combination of two theories is the better modeling of uncertainties. This makes that the output of the air defense system is more reliable and accurate. In this method, the air defense system’s total uncertainty is measured using the best uncertainty measure based on the Fuzzy Dempster-Shafer Theory. The simulation results show that this new method has increased the accuracy to 97% that is more computational toward other theories. This matter significantly increases the computational accuracy of the air defense system in targets threat assessment

Minimization of DDoS false alarm rate in Network Security; Refining fusion through correlation

Intrusion Detection Systems are designed to monitor a network environment and generate alerts whenever abnormal activities are detected. However, the number of these alerts can be very large making their evaluation a difficult task for a security analyst. Alert management techniques reduce alert volume significantly and potentially improve detection performance of an Intrusion Detection System. This thesis work presents a framework to improve the effectiveness and efficiency of an Intrusion Detection System by significantly reducing the false positive alerts and increasing the ability to spot an actual intrusion for Distributed Denial of Service attacks. Proposed sensor fusion technique addresses the issues relating the optimality of decision-making through correlation in multiple sensors framework. The fusion process is based on combining belief through Dempster Shafer rule of combination along with associating belief with each type of alert and combining them by using Subjective Logic based on Jøsang theory. Moreover, the reliability factor for any Intrusion Detection System is also addressed accordingly in order to minimize the chance of false diagnose of the final network state. A considerable number of simulations are conducted in order to determine the optimal performance of the proposed prototype

Applying Extensions of Evidence Theory to Detect Frauds in Financial Infrastructures

The Dempster-Shafer (DS) theory of evidence has significant weaknesses when dealing with conflicting information sources, as demonstrated by preeminent mathematicians. This problem may invalidate its effectiveness when it is used to implement decision-making tools that monitor a great number of parameters and metrics. Indeed, in this case, very different estimations are likely to happen and can produce unfair and biased results. In order to solve these flaws, a number of amendments and extensions of the initial DS model have been proposed in literature. In this work, we present a Fraud Detection System that classifies transactions in a Mobile Money Transfer infrastructure by using the data fusion algorithms derived from these new models. We tested it in a simulated environment that closely mimics a real Mobile Money Transfer infrastructure and its actors. Results show substantial improvements of the performance in terms of true positive and false positive rates with respect to the classical DS theory