Biblio

Rapid development of internet and network technologies has led to considerable increase in number of attacks. Intrusion detection system is one of the important ways to achieve high security in computer networks. However, it have curse of dimensionality which tends to increase time complexity and decrease resource utilization. To improve the ability of detecting anomaly intrusions, a combined algorithm is proposed based on Weighted Fuzzy C-Mean Clustering Algorithm (WFCM) and Fuzzy logic. Decision making is performed in two stages. In the first stage, WFCM algorithm is applied to reduce the input data space. The reduced dataset is then fed to Fuzzy Logic scheme to build the fuzzy sets, membership function and the rules that decide whether an instance represents an anomaly or not.

The security level is very important in Bluetooth, because the network or devices using secure communication, are susceptible to many attacks against the transmitted data received through eavesdropping. The cryptosystem designers needs to know the complexity of the designed Bluetooth E0. And what the advantages given by any development performed on any known Bluetooth E0Encryption method. The most important criteria can be used in evaluation method is considered as an important aspect. This paper introduce a proposed fuzzy logic technique to evaluate the complexity of Bluetooth E0Encryption system by choosing two parameters, which are entropy and correlation rate, as inputs to proposed fuzzy logic based Evaluator, which can be applied with MATLAB system.

Internet of Things refers to a paradigm consisting of a variety of uniquely identifiable day to day things communicating with one another to form a large scale dynamic network. Securing access to this network is a current challenging issue. This paper proposes an encryption system suitable to IoT features. In this system we integrated the fuzzy commitment scheme in DCT-based recognition method for fingerprint. To demonstrate the efficiency of our scheme, the obtained results are analyzed and compared with direct matching (without encryption) according to the most used criteria; FAR and FRR.

The aim of this paper is to present a fresh methodology of improved evidence synthesis for assessing software trustworthiness, which can unwind collisions stemming from proofs and these proofs' own uncertainties. To achieve this end, the paper, on the ground of ISO/IEC 9126 and web software attributes, models the indicator framework by factor analysis. Then, the paper conducts an calculation of the weight for each indicator via the technique of structural entropy and makes a fuzzy judgment matrix concerning specialists' comments. This study performs a computation of scoring and grade regarding software trustworthiness by using of the criterion concerning confidence degree discernment and comes up with countermeasures to promote trustworthiness. Relying on online accounting software, this study makes an empirical analysis to further confirm validity and robustness. This paper concludes with pointing out limitations.

The development of a robust strategy for network security is reliant upon a combination of in-house expertise and for completeness attack vectors used by attackers. A honeypot is one of the most popular mechanisms used to gather information about attacks and attackers. However, low-interaction honeypots only emulate an operating system and services, and are more prone to a fingerprinting attack, resulting in severe consequences such as revealing the identity of the honeypot and thus ending the usefulness of the honeypot forever, or worse, enabling it to be converted into a bot used to attack others. A number of tools and techniques are available both to fingerprint low-interaction honeypots and to defend against such fingerprinting; however, there is an absence of fingerprinting techniques to identify the characteristics and behaviours that indicate fingerprinting is occurring. Therefore, this paper proposes a fuzzy technique to correlate the attack actions and predict the probability that an attack is a fingerprinting attack on the honeypot. Initially, an experimental assessment of the fingerprinting attack on the low- interaction honeypot is performed, and a fingerprinting detection mechanism is proposed that includes the underlying principles of popular fingerprinting attack tools. This implementation is based on a popular and commercially available low-interaction honeypot for Windows - KFSensor. However, the proposed fuzzy technique is a general technique and can be used with any low-interaction honeypot to aid in the identification of the fingerprinting attack whilst it is occurring; thus protecting the honeypot from the fingerprinting attack and extending its life.

The growing prevalence of cyber threats in the world are affecting every network user. Numerous security monitoring systems are being employed to protect computer networks and resources from falling victim to cyber-attacks. There is a pressing need to have an efficient security monitoring system to monitor the large network datasets generated in this process. A large network datasets representing Malware attacks have been used in this work to establish an expert system. The characteristics of attacker's IP addresses can be extracted from our integrated datasets to generate statistical data. The cyber security expert provides to the weight of each attribute and forms a scoring system by annotating the log history. We adopted a special semi supervise method to classify cyber security log into attack, unsure and no attack by first breaking the data into 3 cluster using Fuzzy K mean (FKM), then manually label a small data (Analyst Intuition) and finally train the neural network classifier multilayer perceptron (MLP) base on the manually labelled data. By doing so, our results is very encouraging as compare to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection. The method of including Artificial Intelligence (AI) and Analyst Intuition (AI) is also known as AI2. The classification results are encouraging in segregating the types of attacks.

Hidden Markov Models (HMM) are probabilistic models that can be used for forecasting time series data. It has seen success in various domains like finance [1-5], bioinformatics [6-8], healthcare [9-11], agriculture [12-14], artificial intelligence[15-17]. However, the use of HMM in cyber security found to date is numbered. We believe the properties of HMM being predictive, probabilistic, and its ability to model different naturally occurring states form a good basis to model cyber security data. It is hence the motivation of this work to provide the initial results of our attempts to predict security attacks using HMM. A large network datasets representing cyber security attacks have been used in this work to establish an expert system. The characteristics of attacker's IP addresses can be extracted from our integrated datasets to generate statistical data. The cyber security expert provides the weight of each attribute and forms a scoring system by annotating the log history. We applied HMM to distinguish between a cyber security attack, unsure and no attack by first breaking the data into 3 cluster using Fuzzy K mean (FKM), then manually label a small data (Analyst Intuition) and finally use HMM state-based approach. By doing so, our results are very encouraging as compare to finding anomaly in a cyber security log, which generally results in creating huge amount of false detection.

Today almost everyone is using internet for daily activities. Whether it's for social, academic, work or business. But only a few of us are aware that internet generally we access only a small part of the overall of internet access. The Internet or the world wide web is divided into several levels, such as web surfaces, deep web or dark web. Accessing internet into deep or dark web is a dangerous thing. This research will be conducted with research on web content and deep content. For a faster and safer search, in this research will be use crawler framework. From the search process will be obtained various kinds of data to be stored into the database. The database classification process will be implemented to know the level of the website. The classification process is done by using the fuzzy-KNN method. The fuzzy-KNN method classifies the results of the crawling framework that contained in the database. Crawling framework will generate data in the form of url address, page info and other. Crawling data will be compared with predefined sample data. The classification result of fuzzy-KNN will result in the data of the web level based on the value of the word specified in the sample data. From the research conducted on several data tests that found there are as much as 20% of the web surface, 7.5% web bergie, 20% deep web, 22.5% charter and 30% dark web. Research is only done on some test data, it is necessary to add some data in order to get better result. Better crawler frameworks can speed up crawling results, especially at certain web levels because not all crawler frameworks can work at a particular web level, the tor browser's can be used but the crawler framework sometimes can not work.

In the open network environment, the strange entities can establish the mutual trust through Automated Trust Negotiation (ATN) that is based on exchanging digital credentials. In traditional ATN, the attribute certificate required to either satisfied or not, and in the strategy, the importance of the certificate is same, it may cause some unnecessary negotiation failure. And in the actual situation, the properties is not just 0 or 1, it is likely to between 0 and 1, so the satisfaction degree is different, and the negotiation strategy need to be quantified. This paper analyzes the fuzzy negotiation process, in order to improve the trust establishment in high efficiency and accuracy further.

The factors that threaten electric power information network are analyzed. Aiming at the weakness of being unable to provide numerical value of risk, this paper presents the evaluation index system, the evaluation model and method of network security based on multilevel fuzzy comprehensive judgment. The steps and method of security evaluation by the synthesis evaluation model are provided. The results show that this method is effective to evaluate the risk of electric power information network.

In the production process of embedded device, due to the frequent reuse of third-party libraries or development kits, there are large number of same vulnerabilities that appear in more than one firmware. Homology analysis is often used in detecting this kind of vulnerabilities caused by code reuse or third-party reuse and in the homology analysis, the widely used methods are mainly Binary difference analysis, Normalized compression distance, String feature matching and Fuzz hash. But when we use these methods for homology analysis, we found that the detection result is not ideal and there is a high false positive rate. Focusing on this problem, we analyzed the application scenarios of these four methods and their limitations by combining different methods and different types of files and the experiments show that the combination of methods and files have a better performance in homology analysis.

Fuzz testing is a popular program testing technique. However, it is difficult to find hard-to-reach vulnerabilities that are nested with complex branches. In this paper, we propose semi-symbolic fuzz testing to discover hard-to-reach vulnerabilities. Our method groups inputs into high frequency and low frequency ones. Then symbolic execution is utilized to solve only uncovered branches to mitigate the path explosion problem. Especially, in order to play the advantages of fuzz testing, our method locates critical branch for each low frequency input and corrects the generated test cases to comfort the branch condition. We also implemented a prototype\textbackslashtextbarS2F, and the experimental results show that S2F can gain 17.70% coverage performance and discover more hard-to-reach vulnerabilities than other vulnerability detection tools for our benchmark.

In recent years, The vulnerability of agricultural products chain is been exposed because of the endlessly insecure events appeared in every areas and every degrees from the natural disasters on the each node operation of agricultural products supply chain in recently years. As an very important place of HUNAN Province because of its abundant agricultural products, the Eastern Area's security in agricultural products supply chain was related to the safety and stability of economic development in the entire region. In order to make the more objective, scientific, practical of risk management in the empirical analysis, This item is based on the AHP-FCS method to deal with the qualitative to quantitative analysis about risk management of agricultural product supply chain, to identify and evaluate the probability and severity of all the risk possibility.

A variety of methods for images noise reduction has been developed so far. Most of them successfully remove noise but their edge preserving capabilities are weak. Therefore bilateral image filter is helpful to deal with this problem. Nevertheless, their performances depend on spatial and photometric parameters which are chosen by user. Conventionally, the geometric weight is calculated by means of distance of neighboring pixels and the photometric weight is calculated by means of color components of neighboring pixels. The range of weights is between zero and one. In this paper, geometric weights are estimated by fuzzy metrics and photometric weights are estimated by using fuzzy rule based system which does not require any predefined parameter. Experimental results of conventional, fuzzy bilateral filter and proposed approach have been included.

In this paper, an optimization model of automobile supply chain network with risks under fuzzy price is put forward. The supply chain network is composed of component suppliers, plants, and distribution centers. The total costs of automobile supply chain consist of variable costs, fixed costs, and transportation costs. The objective of this study is to minimize the risks of total profits. In order to deal with this model, this paper puts forward an approximation method to transform a continuous fuzzy problem into discrete fuzzy problem. The model is solved using Cplex 12.6. The results show that Cplex 12.6 can perfectly solve this model, the expected value and lower semi-variance of total profits converge with the increasing number of discretization points, the structure of automobile supply chain network keeps unchanged with the increasing number of discretization points.

As a clean energy, wind power is massively utilized in net recent years, which significantly reduced the pollution emission created from unit. This article referred to the concept of energy-saving and emission reducing; built a multiple objective function with represent of the emission of CO2& SO2, the coal-fired from units and the lowest unit fees of commitment; Proposed a algorithm to improving NSGA-D (Non-dominated Sorting Genetic Algorithm-II) for the dynamic characteristics, consider of some constraint conditions such as the shortest operation and fault time and climbing etc.; Optimized and commitment discrete magnitude and Load distribution continuous quantity with the double-optimization strategy; Introduced the fuzzy satisfaction-maximizing method to reaching a decision for Pareto solution and also nested into each dynamic solution; Through simulation for 10 units of wind power, the result show that this method is an effective way to optimize the Multi-objective unit commitment modeling in wind power integrated system with Mixed-integer variable.

The increasing exploitation of the internet leads to new uncertainties, due to interdependencies and links between cyber and physical layers. As an example, the integration between telecommunication and physical processes, that happens when the power grid is managed and controlled, yields to epistemic uncertainty. Managing this uncertainty is possible using specific frameworks, usually coming from fuzzy theory such as Evidence Theory. This approach is attractive due to its flexibility in managing uncertainty by means of simple rule-based systems with data coming from heterogeneous sources. In this paper, Evidence Theory is applied in order to evaluate risk. Therefore, the authors propose a frame of discernment with a specific property among the elements based on a graph representation. This relationship leads to a smaller power set (called Reduced Power Set) that can be used as the classical power set, when the most common combination rules, such as Dempster or Smets, are applied. The paper demonstrates how the use of the Reduced Power Set yields to more efficient algorithms for combining evidences and to application of Evidence Theory for assessing risk.

The modern day approach in boulevard network centers on efficient factor in safe routing. The safe routing must follow up the low risk cities. The troubles in routing are a perennial one confronting people day in and day out. The common goal of everyone using a boulevard seems to be reaching the desired point through the fastest manner which involves the balancing conundrum of multiple expected and unexpected influencing factors such as time, distance, security and cost. It is universal knowledge that travelling is an almost inherent aspect in everyone's daily routine. With the gigantic and complex road network of a modern city or country, finding a low risk community for traversing the distance is not easy to achieve. This paper follows the code based community for detecting the boulevard network and fuzzy technique for identifying low risk community.

The use of multi-terminal HVDC to integrate wind power coming from the North Sea opens de door for a new transmission system model, the DC-Independent System Operator (DC-ISO). DC-ISO will face highly stressed and varying conditions that requires new risk assessment tools to ensure security of supply. This paper proposes a novel risk-based static security assessment methodology named risk-based DC security assessment (RB-DCSA). It combines a probabilistic approach to include uncertainties and a fuzzy inference system to quantify the systemic and individual component risk associated with operational scenarios considering uncertainties. The proposed methodology is illustrated using a multi-terminal HVDC system where the variability of wind speed at the offshore wind is included.

Due to the fact that the cyber security risks exist in industrial control system, risk assessment on Industrial Automation Platform (IAP) is discussed in this paper. The cyber security assessment model for IAP is built based on relevant standards at abroad. Fuzzy analytic hierarchy process and fuzzy comprehensive evaluation method based on entropy theory are utilized to evaluate the communication links' risk of IAP software. As a result, the risk weight of communication links which have impacts on platform and the risk level of this platform are given for further study on protective strategy. The assessment result shows that the methods used can evaluate this platform efficiently and practically.

Cloud Computing has emerged as a paradigm to deliver on demand resources to facilitate the customers with access to their infrastructure and applications as per their requirements on a subscription basis. An exponential increase in the number of cloud services in the past few years provides more options for customers to choose from. To assist customers in selecting a most trustworthy cloud provider, a unified trust evaluation framework is needed. Trust helps in the estimation of competency of a resource provider in completing a task thus enabling users to select the best resources in the heterogeneous cloud infrastructure. Trust estimates obtained using the AHP process exhibit a deviation for parameters that are not in direct proportion to the contributing attributes. Such deviation can be removed using the Fuzzy AHP model. In this paper, a Fuzzy AHP based hierarchical trust model has been proposed to rate the service providers and their various plans for infrastructure as a service.

Fuzzy c-means algorithm is used to identity clusters of similar objects within a data set, while it is not directly applied to incomplete data. In this paper, we proposed a novel fuzzy c-means algorithm based on missing attribute interval size for the clustering of incomplete data. In the new algorithm, incomplete data set was transformed to interval data set according to the nearest neighbor rule. The missing attribute value was replaced by the corresponding interval median and the interval size was set as the additional property for the incomplete data to control the effect of interval size in clustering. Experiments on standard UCI data set show that our approach outperforms other clustering methods for incomplete data.

The significant dependence on cyberspace has indeed brought new risks that often compromise, exploit and damage invaluable data and systems. Thus, the capability to proactively infer malicious activities is of paramount importance. In this context, inferring probing events, which are commonly the first stage of any cyber attack, render a promising tactic to achieve that task. We have been receiving for the past three years 12 GB of daily malicious real darknet data (i.e., Internet traffic destined to half a million routable yet unallocated IP addresses) from more than 12 countries. This paper exploits such data to propose a novel approach that aims at capturing the behavior of the probing sources in an attempt to infer their orchestration (i.e., coordination) pattern. The latter defines a recently discovered characteristic of a new phenomenon of probing events that could be ominously leveraged to cause drastic Internet-wide and enterprise impacts as precursors of various cyber attacks. To accomplish its goals, the proposed approach leverages various signal and statistical techniques, information theoretical metrics, fuzzy approaches with real malware traffic and data mining methods. The approach is validated through one use case that arguably proves that a previously analyzed orchestrated probing event from last year is indeed still active, yet operating in a stealthy, very low rate mode. We envision that the proposed approach that is tailored towards darknet data, which is frequently, abundantly and effectively used to generate cyber threat intelligence, could be used by network security analysts, emergency response teams and/or observers of cyber events to infer large-scale orchestrated probing events for early cyber attack warning and notification.
 

Route selection is a very sensitive activity for mobile ad-hoc network (MANET) and ranking of multiple routes from source node to destination node can result in effective route selection and can provide many other benefits for better performance and security of MANET. This paper proposes an evaluation model based on analytical hierarchy process (AHP), fuzzy sets and technique for order performance by similarity to ideal solution (TOPSIS) to provide a useful solution for ranking of routes. The proposed model utilizes AHP to acquire criteria weights, fuzzy sets to describe vagueness with linguistic values and triangular fuzzy numbers, and TOPSIS to obtain the final ranking of routes. Final ranking of routes facilitates selection of best and most reliable route and provide alternative options for making a robust Mobile Ad-hoc network.

The performance of ad hoc networks depends on the cooperative and trust nature of the distributed nodes. To enhance security in ad hoc networks, it is important to evaluate the trustworthiness of other nodes without central authorities. An information-theoretic framework is presented, to quantitatively measure trust and build a novel trust model (FAPtrust) with multiple trust decision factors. These decision factors are incorporated to reflect trust relationship's complexity and uncertainty in various angles. The weight of these factors is set up using fuzzy analytic hierarchy process theory based on entropy weight method, which makes the model has a better rationality. Moreover, the fuzzy logic rules prediction mechanism is adopted to update a node's trust for future decision-making. As an application of this model, a novel reactive trust-based multicast routing protocol is proposed. This new trusted protocol provides a flexible and feasible approach in routing decision-making, taking into account both the trust constraint and the malicious node detection in multi-agent systems. Comprehensive experiments have been conducted to evaluate the efficiency of trust model and multicast trust enhancement in the improvement of network interaction quality, trust dynamic adaptability, malicious node identification, attack resistance and enhancements of system's security.