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Entity matching aims at mapping records to different entities where records share the common entity name. The entity matching problem is challenging because several times records do not contain complete information (many of the attributes are missing), unequal distribution of records for different entities, big overlaps between records of different entities. In this paper, we have proposed an unsupervised framework to solve this problem. The aforementioned problem is posed as a partitioning problem. Three unknown artifacts for solving this partitioning problem: optimal partitioning including the optimal number of partitions, suitable distance measure which can be utilized to measure the distance between records and a set of attributes/features which can take part in the distance calculation, are determined automatically using the search capability of a multiobjective optimization technique. Several objective functions which help in measuring the goodness of partitioning are optimized simultaneously by some popular multiobjective optimization techniques, NSGA-II/NSGA-III (Non-dominated Sorting Genetic Algorithm-II/III) to solve the partitioning problem. Total 247 combinations of eight different objective functions are used in the experiments for partitioning fourteen bibliographic datasets. A detailed comparative study of the proposed approach using NSGA-II and NSGA-III.

In this work, we propose a scheduling algorithm, named mixed-MinPeak, to schedule deferrable appliances that can be of both preemptive and non-preemptive types, together in the presence of non-deferrable appliances. The performance of the proposed algorithm has been evaluated on practical power consumption data, along with a comparative analysis with existing algorithms, the results of which prove the capability of the proposed algorithm in producing better solutions.

In this work, we propose a multi-objective optimal scheduling strategy for air-conditioning devices to optimize both energy consumption and thermal comfort for the users. We propose a graph-based modeling for the problem and utilize Johnson's all elementary circuit finding algorithm to obtain the desired solutions. The proposed methodology has been experimented on test cases that mimic real-world scenario, and further, the applicability of Karp's minimum mean cycle algorithm is also studied in this problem set-up.

In this paper, we obtain the tight upper bound on the number of non-dominated fronts in a population. In general, the maximum number of non-dominated fronts in a population of size $\boldsymbol{N}$ can be $\boldsymbol{N}$ and the minimum number of non-dominated fronts can be 1. However, without sorting the entire population, it is very difficult to obtain an upper bound on the number of fronts. Here, we present an algorithm to obtain the tight upper bound on the number of non-dominated fronts and also prove that the time complexity of obtaining the upper bound is $\boldsymbol{\mathcal{O}}(\boldsymbol{M}\boldsymbol{N}\ \mathbf{log}\ \boldsymbol{N})$ where $\boldsymbol{M}$ is the number of objectives associate with each solution in the population. At the end we propose the parallel version of our approach and also prove the space and time complexities of the parallel version.

In entity matching techniques from bibliographic databases like DBLP, Arnetminer, Scopus, Google Scholar, etc., records of the same entity need to be clustered together. Checking the correctness of such techniques is very challenging because actual or correct results are often not known or very difficult to know. Generally, F-measure is used for correctness evaluation, which requires gold standard data. However, obtaining the gold standard data is very difficult, time-consuming and requires the help from some human-annotators. To address this problem, in the current work, we have proposed the use of some internal cluster validity measures to evaluate the goodness of the entity matching techniques. In order to handle the bibliographic databases, several different distance measures are used and these are incorporated into the definitions of various internal cluster validity measures to make them applicable for entity matching problem. Comparative analysis is done on a large collection of data sets considering various internal validity measures.

Honeyword (or fake password) based authentication technique has become a well-established security mechanism for protecting the original password from the server-side attack. However, there are few underlying issues associated with this technique which are still a matter of concern for the security community. Achieving flatness or producing honeywords that are equally likely to the original password is one such major concern that requires further attention. Though recent studies have made significant efforts to meet this flatness criterion, our analysis shows that they still fall short to address this issue. In this paper, at first, we have made a detailed investigation to define the basic properties for achieving flatness. Followed by this, we have proposed a questionnaire-based authentication technique which can generate significantly flatter list of honeywords compared to existing state-of-the-art. Further study reveals that the proposed technique passes the other essential evaluation criteria of honeyword based authentication technique with flying colors.

The current paper aims to improve an efficient algorithm for solving the problem of non-dominated sorting which is one of the dominant steps of any Pareto based multi/many objective optimization algorithms. Recent years witnessed a large number of attempts in developing some solution frameworks for the problem of non-dominated sorting. One such recent approach is ‘Best Order Sort’ which is efficient with respect to the number of dominance comparisons. However, this approach does not perform well in the presence of duplicate solutions. In this paper attempts have been made to modify the ‘Best Order Sort’ and we call this modified version as ‘Modified Best Order Sort’ to remove the above mentioned limitation. The modified best order sort algorithm has been thoroughly analyzed in different scenarios. Current work shows that ‘Best Order Sort’ can be generalized without affecting its best and the worst case time complexities.

Author name disambiguation is one of the very important problems in scientometrics. Plenty of approaches have been proposed for solving this problem. However, none of the proposed approaches is complete so there is still scope for improvement. In this regard, we have proposed an unsupervised heuristic-based approach for author name disambiguation. The technique also uses the information collected from the web as auxiliary information. Experimental results show that our method performs better than the existing methods.

Minimizing peak load consumption has been one of the primary objectives behind most of the research work conducted on demand side management in smart grid domain. Demand side management uses different techniques to curtail consumers' load consumption during peak-hour periods and tries to schedule various deferrable appliances to operate during off-peak hours. In this paper, we propose MinPeak algorithm to schedule deferrable appliances with the objective of reducing peak load demand. The problem is solved as a strip-packing problem, where all the appliances, characterized by their power consumption and execution time units, are packed or scheduled within a certain scheduling time horizon or width of a strip. We have conducted a comprehensive simulation study using existing benchmark data sets and compared the proposed algorithm with some of the well known heuristics for strip packing problem. The results are very encouraging in terms of reducing peak load demand and overall efficiency of the system.

In the modern era, energy generation and distribution systems are evolving towards the new concept of smart grid. One of the major components of the smart grid is advanced metering infrastructure which facilitates bidirectional communication between smart meters and utilities, allowing information about consumption, outages, and electricity rates to be shared reliably and efficiently. However, this new infrastructure is prone to a wider array of attacks, with energy thefts being a critical vulnerability leading to losses worth billions of dollars annually to the Utility Companies. The goal of this paper is to propose a clustering based energy theft detection technique which can be deployed in real time energy systems. The proposed technique is based on the idea of peer-to-peer computing technique for energy theft detection, a technique that has been reported to result in 100% detection capability. Extensive simulations and testing have been performed and the results show that the proposed techniques can be successfully deployed in real time systems to identify the dishonest customers within deterministic time delay.

Multiobjective optimization refers to optimization of multiple conflicting objective functions simultaneously. Clustering problem is often formulated as a multiobjective optimization problem where multiple cluster quality measures are simultaneously optimized and Pareto based approaches are popular in solving that Pareto based approaches yield a set of solutions known as Pareto front where all the solutions are non-dominated with respect to each other. A single solution is selected by the decision maker according to his/her preference. But when the number of non-dominated solutions is large in number, then it is difficult for the decision maker to choose the one solution. The selection of a solution from the given Pareto front is known as Post-Pareto optimality analysis. In the past many approaches were proposed for solving the aforementioned problem, but most of these involve the decision maker. In this paper, we have proposed an approach to obtain a single solution from a set of non-dominated solutions by combining these solutions without the intervention of the decision maker. We have evaluated our approach on the set of solutions obtained after application of a newly developed multiobjective based clustering technique on bibliographic databases like DBLR.

The phenomenal growth in the healthcare data has inspired us in investigating robust and scalable models for data mining. For classification problems Information Gain(IG) based Decision Tree is one of the popular choices. However, depending upon the nature of the dataset, IG based Decision Tree may not always perform well as it prefers the attribute with more number of distinct values as the splitting attribute. Healthcare datasets generally have many attributes and each attribute generally has many distinct values. In this paper, we have tried to focus on this characteristics of the datasets while analysing the performance of our proposed approach which is a variant of Decision Tree model and uses the concept of Correlation Ratio(CR). Unlike IG based approach, this CR based approach has no biasness towards the attribute with more number of distinct values. We have applied our model on some benchmark healthcare datasets to show the effectiveness of the proposed technique.

To prevent shoulder surfing attack, a secured channel between the device and user must be established so that it cannot be eavesdropped by an adversary. In this paper we have explored vibration signals to design authentication services namely - (a) VDLS and (b) M-VDLS to resist shoulder surfing attack. The use of vibration provides a key strength to bypass the threat of shoulder surfing without using any auxiliary device. Thus, proposed schemes in this paper avail aforementioned advantage to address the attack. While VDLS attains very high security standard compared to existing approaches, M-VDLS ensures high usability standard to avoid the attack in mobile environment. Both these approaches are also capable of providing solution against classical shoulder surfing attack without any information leakage. Finally, we show that required overheads of proposed approaches are reasonable in practice and outperform the existing approaches in terms of security and usability.

Many of the real-life problems involve simultaneous optimization of multiple objectives. In recent years there is an enormous increase in the number of multi-objective optimization problems related to different real-life domains. Evolutionary algorithms are the most popular in solving these types of problems. The non-dominating sorting is one of the steps of any multiobjective evolutionary algorithms. This is used mostly to select the non-dominated set of solutions from a given population. In the past various efficient approaches are proposed in the literature to reduce the complexity of this step. As the evolutionary algorithms inhibit parallelism in it. But not all the existing non-dominating sorting approaches have the parallelism property. So in this paper, we have proposed a new approach named as DCNS (Divide and Conquer based Non-dominating Sorting) which inhibits parallelism in it. It has been shown theoretically and empirically that the proposed approach is computationally efficient than existing state-of-the-art methods.

In steady-state evolutionary algorithms, the parent population is updated each time once a new offspring solution is generated. Due to the updation of the parent population, the non-dominated sorting needs to be applied again and again. The repetition of non-dominated sorting makes steady-state algorithms computationally expensive. But the recent study has identified that the insertion of an offspring solution in the known non-domination level structure of solutions does not change the entire structure. So there is no need to apply the complete non-dominated sorting algorithm again and again. In this regard an efficient non-domination level update approach known as ENLU approach was proposed. In steady-state evolutionary algorithm, the same pair of solutions can be compared multiple times in different generations of the algorithm. In this paper, we have performed the same ENLU approach in a different way so that the same pair of solutions is compared only once if they are in the current population. The worst case time complexity of ENLU approach is O(MN2). So for G generations of the steady-state evolutionary algorithm, the worst case time complexity is GO(MN2). In this paper, we have utilized the same ENLU approach which performs the same number of comparisons all the times. The worst case time complexity of our approach is G(O(MN log N) + O(N2)). However, in terms of space complexity the proposed approach requires O(N2) space as compared to O(N) of the ENLU approach. So we have achieved the speedup at the cost of extra space. At the end, we have explored the possibility of parallelism to make the ENLU approach faster.

Entity matching is to map the records to the corresponding entity. It is a well known problem studied by many researchers over the last few years. In bibliographic database, the data evolve over time. For example, the email id of an author in DBLP and ArnetMiner which are two bibliographic databases changes with time. Authors also keep on changing their affiliations. The set of authors with whom they work also changes with time. These types of variations make the entity matching task more difficult. In this paper, we have addressed this problem and proposed the nondominated sorting genetic algorithm-II (NSGA-II) based solution framework. The dissimilarities between different records can be measured using various distance measures. One distance measure can be suitable for one data set while some other distance measure can be suitable for some other data sets. So selecting the appropriate distance measure is also difficult. To address this issue, this paper presents an automatic framework which selects the suitable distance measure along with the appropriate partitioning. To encode the partitions, medoid based encoding is used. Several new mutation operations are used to explore the search space efficiently. Silhouette Index along with Xie-Beni Index are optimized simultaneously during the experiments for three bibliographic data sets. The results of our approach are compared with two existing well known techniques, DBLP and ArnetMiner. From the results it is clear that our proposed approach performs well.

In entity name disambiguation, performance evaluation of any approach is difficult. This is due to the fact that correct or actual results are often not known. Generally for evaluation purpose, three measures namely precision, recall and f-measure are used. They all are external validity indices because they need golden standard data. But in Bibliographic databases like DBLP, Arnetminer, Scopus, Web of Science, Google Scholar, etc., gold standard data is not easily available and it is very difficult to obtain this due to the overlapping nature of data. So, there is a need to use some other matrices for evaluation purpose. In this paper, some internal cluster validity index based schemes are proposed for evaluating entity name disambiguation algorithms when applied on bibliographic data without using any gold standard datasets. Two new internal validity indices are also proposed in the current paper for this purpose. Experimental results shown on seven bibliographic datasets reveal that proposed internal cluster validity indices are able to compare the results obtained by different methods without prior/gold standard. Thus the present paper demonstrates a novel way of evaluating any entity matching algorithm for bibliographic datasets without using any prior/gold standard information.

Classical PIN entry mechanism is widely used for authenticating a user. It is a popular scheme because it nicely balances the usability and security aspects of a system. However, if this scheme is to be used in a public system then the scheme may suffer from shoulder surfing attack. In this attack, an unauthorized user can fully or partially observe the login session. Even the activities of the login session can be recorded which the attacker can use it later to get the actual PIN. In this paper, we propose an intelligent user interface, known as Color Pass to resist the shoulder surfing attack so that any genuine user can enter the session PIN without disclosing the actual PIN. The Color Pass is based on a partially observable attacker model. The experimental analysis shows that the Color Pass interface is safe and easy to use even for novice users.

In entity name disambiguation technique, records of same entity are clustered together. One of the major challenges in such technique is to validate the result as the actual or correct results are often not known or difficult to know. In this context, three commonly known evaluation measures are precision, recall and f-measure. All these indices are external validity indices as they all need gold standard data. But in Bibliographic databases like DBLP, Arnetminer, Scopus, Web of Science etc., obtaining golden standard is very difficult for each entity. So, there is a need to use some other metrics to evaluate the performance on Bibliographic data. In this paper, a novel scheme based on internal validity index is used to evaluate the performance of entity name disambiguation algorithm. Several distance measures are used here to compute the similarity between two records. These functions are then incorporated in the definitions of internal validity indices.

Role Based Access control (RBAC) is a popular security technique to impose restriction on different resources. To capture various emerging security requirements, RBAC has been evolved from time to time. Due to advancement in wireless devices new security requirements have come up and it is quite challenging for the existing access control models to tackle such requirements. In this paper, a location sensitive access control (LSAC) model is proposed to incorporate security policies related to mobile users. It has been observed that different characteristics of moving objects such as velocity, acceleration, etc., are often ignored while granting access to a particular resource. In this work, an attempt is made to consider those parameters while allowing or disallowing access to location sensitive objects.

In an organization, one of the important job of an administrator is to define different access control policies based on the various requirements. Access-control policy misconfigurations that cause requests to be erroneously denied can result in wasted time, user frustration. Depending upon the context of particular applications (e.g., health care, national security) the effect may be quite severe. Identification of such possible inconsistencies in the access control system at an early stage even before the user tries to access them, can help in rectifying such mistakes. Access Control List (ACL) purely reflects the various policies that a system must follow to carry out the desirable tasks. For most of the access control systems the ACL remains the same. In recent times, such models are not sufficient enough to meet the requirements, leading to models like TRBAC, GTRBAC. Identification of policy misconfigurations in such systems helps in minimizing the vulnerabilities, reducing the security risks and insider attacks. And in certain scenarios the policies may not be simple and may involve priorities among objects, where there is a fair chance of having erroneous policies unknowingly. Thus, identification of misconfigurations in such cases is of prior importance.

Role based access control (RBAC) has emerged as an effective solution for several access control problems of relevance today. To cope with the growing requirements, core RBAC has been extended over temporal, spatial and spatiotemporal dimensions. The various models developed so far predominantly deal with monotonic policies which allow access only if there is a corresponding positive authorization. However, in many practical situations, there is a need for specifying policies that deny access through negative authorization. The authorization may again depend on different spatiotemporal conditions. In this paper, the notion of user-role-permission (URP) relation is used to incorporate negative authorization in ESTARBAC, one of the existing spatiotemporal RBAC models. The proposed method has been compared with two other existing approaches. We also analyze how negative authorization can facilitate expressing some of the important access control policies relevant for an organization.

Role Based Access Control (RBAC) is arguably the most common access control mechanism today due to its applicability at various levels of authorization in a system. Time varying nature of access control in RBAC administered systems is often implemented through Temporal-RBAC - an extension of RBAC in the temporal domain. In this paper, we propose an initial approach towards verification of security properties of a Temporal-RBAC system. Each role is mapped to a timed automaton. A controller automaton is used to activate and deactivate various roles. Security properties are specified using Computation Tree Logic (CTL) and are verified with the help of a model checking tool named Uppaal. We have specifically considered reachability, safety and liveness properties to show the usefulness of our approach.

In this paper, we created a generalized framework for the automated placement and routing of analog test structures. We exploited the concept of terminal properties when placing and routing the test structures and generated a library of place and routing strategies for different architectures. This new approach significantly reduces layout time, maximizes the reuse of place and route routines, and facilitates the introduction of a holistic parametric test design flow.