Naima Iltaf

A mechanism for detecting dishonest recommendation in indirect trust computation

Indirect trust computation based on recommendations form an important component in trust-based access control models for pervasive environment. It can provide the service provider the confidence to interact with unknown service requesters. However, recommendation-based indirect trust computation is vulnerable to various types of attacks. This paper proposes a defense mechanism for filtering out dishonest recommendations based on a measure of dissimilarity function between the two subsets. A subset of recommendations with the highest measure of dissimilarity is considered as a set of dishonest recommendations. To analyze the effectiveness of the proposed approach, we have simulated three inherent attack scenarios for recommendation models (bad mouthing, ballot stuffing, and random opinion attack). The simulation results show that the proposed approach can effectively filter out the dishonest recommendations based on the majority rule. A comparison between the exiting schemes and our proposed approach is also given.

A Mathematical Approach towards Trust Based Security in Pervasive Computing Environment

In ubiquitous computing traditional security mechanism is difficult to realize, as users do not have prior knowledge of each other and the availability of a trusted third party cannot be relied upon. Since trust is used as an elementary channel of interaction in a human society, a security model based on the real world characteristics of trust can provide the internal security before taking action, despite uncertainty and inadequate information in ubiquitous computing environment. We propose the trust based security architecture based on the human notion of trust as a mechanism to secure computing in ubiquitous environment.

Security & trust enforcement in pervasive computing environment (STEP)

The propagation of ubiquitous computing is bringing in an environment, where multitudes of diverse entities interact with each other. It is widely acknowledged that traditional security measures do not scale well in environments which are epitomized by unforeseen circumstances, unexpected interactions, and unknown entities. We propose the trust based security architecture based on the human notion of trust as a mechanism to secure computing in ubiquitous environments.

Modeling interaction using trust and recommendation in ubiquitous computing environment

To secure computing in pervasive environment, an adaptive trust and recommendation based access control model (based on human notion of trust) is proposed. The proposed model provides support to calculate direct as well as indirect trust based on recommendations. It handles situations (by itself) both in which the requesting entity has a past experience with the service and a stranger entity requesting to access the service without any past interaction with the service. It encompasses the ability to reason human cognitive behavior and has the capability to adjust in accordance with behavioral pattern changes. X-bar control chart is used to handle malicious recommendation. The defense mechanisms incorporated by proposed model against attacks such as bad mouthing attack, oscillating behavior attack and conflicting behavior attack are also demonstrated.

An Effective Model for Indirect Trust Computation in Pervasive Computing Environment

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. An effective model for indirect trust computation is proposed which is capable of identifying dishonest recommendations. Dishonest recommendations are identified by using deviation based detecting technique. The concept of measuring the credibility of recommendation (rather than credibility of recommender) using fuzzy inference engine is also proposed to determine the influence of each honest recommendation. The proposed model has been compared with other existing evolutionary recommendation models in this field, and it is shown that the model is more accurate in measuring the trustworthiness of unknown entity.

Image Contrast Enhancement Using Weighted Transformation Function

This paper presents a contrast enhancement technique using weighted transformation functions. The transformation functions obtained at different levels (using modified histograms) are weighted according to their similarity/dissimilarity from mean value. To increase the dynamic range of enhanced image, bins having very slight contribution in the histogram are filtered out. Simulation results (evaluated visually and quantitatively) on different images show the significance of the proposed technique as compared with the state-of-the-art existing techniques.

An attack resistant method for detecting dishonest recommendations in pervasive computing environment

An attack resistant method for indirect trust computation (based on recommendation) for pervasive computing environment is proposed. The method extends a mechanism to detect outliers in dataset presented in [19] and apply it to filter out malicious recommendations in indirect trust computation. The method is based on a dissimilarity metric based on the statistical distribution of the recommendations grouped into recommendation classes. The proposed model has been evaluated in different attack scenarios (bad mouthing, ballot stuffing and random attack). The model has also been compared with other existing evolutionary recommendation models in this field, and it is shown that the proposed approach can effectively filter out dishonest recommendations provided that the number of dishonest recommendations is less than the number of honest recommendations.

A fuzzy based credibility evaluation of recommended trust in pervasive computing environment

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. A fuzzy logic based technique is proposed for indirect trust computation that measures the credibility of recommendation (rather than credibility of recommender)to determine the influence of each honest recommendation.

STEP-a: An Algorithmic Approach towards Trust Based Security in Pervasive Computing Environment

-- To secure computing in pervasive environment, an adaptive algorithmic approach for access control (based on human notion of trust) is proposed. The proposed model provides support to calculate direct trust in situations when the requesting entity has a past experience with the service. It encompasses the ability to reason human cognitive behavior and has the capability to adjust in accordance with behavioral pattern changes. An adaptive policy based management is proposed to handle strategic malicious behavior. The defense mechanism incorporated by proposed model against strategic attack is also demonstrated. Finally we have compared our model with existing evolutionary models in this field.

A review on variant clustering designs in heterogeneous WSNs

In last decade researchers have a growing attention in domain of Wireless Sensor Networks (WSNs) due to numerous increasing automated industry and battle field applications. WSNs comprises of hundreds or even thousands of unsystematically deployed energy lacking sensor units. These sensors are aimed to transfer data from the field to distant Base Station (BS). This operation necessitate excessive energy. Minimizing energy expenditure and spreading WSN lifetime are prodigious tasks. Development of energy saving routing mechanism is one of the main issues in WSNs. In practical scenario almost all the WSNs are heterogenous or become heterogenous after small life span. Practically efficiency of every protocol changes with diverse heterogeneity environment. There is a dire need for testing of routing protocols underling varying heterogeneous parameters to cope with the practical scenario problems. In this paper performance of DEEC (Distributed Energy-Efficient Clustering), DDEEC (Developed DEEC), TDEEC (Threshold DEEC) and BEENISH (Balanced Energy Efficient Network Integrated Super Heterogenous) are evaluated with respect to varying heterogeneity metrics. Testing shows the variable behaviour of protocols with changing environment. However BEENISH performs the best in all cases and performance is sustained in varying heterogeneous scenarios.