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Due to the steep rise in global population, urbanization, and industrialization, most of the cities in the world today are witnessing increased carbon footprints and reduced per capita space. In such a scenario, vehicle sharing and carpooling systems, specifically with electric vehicles (EV), can significantly help due to the reduced cost of ownership, maintenance, and parking space. In this work, we study the challenging problem of optimal sizing and efficient routing for an Electric Vehicle-on-Demand (EVoD) system. Users demand EVs at the pooling stations at different time instances with individual deadlines to reach the destinations. The objective is to fulfill all the demands respecting the deadlines with minimum investment, which essentially translates to minimizing the total number of EVs. We define the problem formally using Mixed Integer Linear Programming (MILP) formulation and propose a set of intelligent and efficient heuristic algorithms to solve it efficiently. The proposed algorithms' performances are tested and validated in a simulated environment on a reasonable size city network with many EV demands. The results obtained show that the proposed heuristic algorithms are competent by reducing 200-360 EVs per day on a network of 282 charging ports, indicating their scalability to be implemented in real-world scenarios.

Pollution is one of the major threats for the environment as well as society. It causes severe problems for the living organisms and can gives birth of various unknown issues. Different sources like cars, industrial belts, fossil fuels etc. are the major causes of air pollution. Different researchers are working to develop new methods to combat air pollution. In this work, a new solution is proposed to fight against air pollution. The proposed solution is based on nanotechnology which not only fight against the air pollution but, it can generate electricity using the nanostructured carbon materials. The proposed solution can be deployed in real life scenario to reduce the air pollution and produce electricity in a large scale to provide an alternate energy resource to the society.

In bibliometric analysis, ambiguity in author names may lead to erroneous aggregation of records. The author name disambiguation techniques attempt to address this issue by attributing records to the corresponding author. The name disambiguation has been widely studied as a clustering task. However, maintaining consistent accuracy levels over datasets is still a major challenge. Recent efforts have witnessed the use of representation learning based techniques to map the records to an embedding space that can be used to determine the clusters. However, some of these models that use supervised global embedding fail to generalize across different datasets, while others lag in the accuracy. In this paper, we propose a method that uses two independent relations among the documents-co-authorship and meta-content of document, to generate a latent representation of documents that is capable of generalizing over various datasets (consisting different sets of features). Through rigorous validation, we discover that the proposed approach outperforms several state-of-the-art methods by a significant margin in terms of standard measures like pairwise F1, K metric, and BF1 scores. Moreover, we have also validated the performance of our method with the statistical test.

In the era of the Internet of Things (IoT), people access many applications through smartphones for controlling smart devices. Therefore, such a centralized node must follow a robust access control mechanism so that an intruder cannot control the connected devices. Recent reports suggest that password can be used as an authentication factor for accessing the smart setups. However, this static information can be compromised under the light of different machine learning (ML)-empowered attack mechanisms. Alarmingly, different sensors used in the IoT setup can also expose this static information to the adversaries. Password-based authentication that uses a challenge–response strategy is an effective solution for handling such threat scenarios. In this article, at first, we show that no existing usable challenge–response protocol is safe to be used in the public area network. Following this, we propose a challenge–response protocol that is more secure to use in the public domain. By using eight classifiers, we show that a learning-based threat specific to our protocol has a marginal impact on the method’s security standard. The discussion in this article also suggests that the proposed protocol has usability and security advantages compared to the existing state of the art (e.g., reduces the number of interactions between the user and verifier by a factor of 0.5).

Side channel attacks, based on the human behavior, have not received much attention in the domain of recording attack resilient unaided authentication services (RARUAS) that purely rely on human visual perception but not on hidden auxiliary channels. In this paper, for the first time, we have made an extensive analysis to show - how human behavior during the login can weaken the claimed security standard of RARUAS. We identify this threat as behavioral side channel attack. To make situation more alarming, our investigation revealed that the identified threat model is capable of reducing the claimed session resiliency of any RARUAS by a significant extent. For dealing with this threat model, the latter part of our proposal introduces a novel defense strategy that reduces attackers’ efficiency and improves the session resiliency. The subsequent study indicates that by nature of its design, the proposed defense strategy does not make any significant impact on the usability standard. To validate our claims, we have made a thorough experimental study to show that the proposed defense strategy is truly deployable in practice for improving the situation against the behavioral side channel attack.

In general, evolutionary algorithms are very prevalent in solving multi-objective optimization problems. Pareto-based multi-objective evolutionary algorithms are popularly used in solving different multi-objective optimization problems. These algorithms work using several steps, non-dominated sorting being the most salient one. However, this non-dominated sorting step is associated with a high computational complexity. In the past, different approaches have been proposed for non-dominated sorting. In this paper, to address the problem of non-dominated sorting, a framework called DCNS (Divide-and-conquer based non-dominated sorting) is developed. Based on this DCNS framework, four different approaches are proposed. The best case time complexity of our proposed DCNS framework is proved to be O ( N log N + M N ) for M ≥ 2 where N is the number of solutions and M is the number of objectives. This best case time complexity is better than the best case time complexities of various other approaches. The number of dominance comparisons performed by the proposed framework is lower than those from other state-of-the-art approaches in different scenarios. The proposed framework has the parallelism property and the scope of parallelism is also discussed.

In predictive tasks like classification, Information Gain (IG) based Decision Tree is very popularly used. However, IG method has some inherent problems like its preference towards choosing attributes with higher number of distinct values as the splitting attribute in case of nominal attributes and another problem is associated with imbalanced datasets. Most of the real-world datasets have many nominal attributes, and those nominal attributes may have many number of distinct values. In this paper, we have tried to point out these characteristics of the datasets while discussing the performance of our proposed approach. Our approach is a variant of the traditional Decision Tree model and uses a new technique called Dispersion_Ratio, a modification of existing Correlation Ratio (CR) method. The whole approach is divided into two phases - firstly, the dataset is discretised using a discretization module and secondly, the preprocessed dataset is used to build a Dispersion Ratio based Decision Tree model. The proposed method does not prefer the attributes with many unique values and indifferent about class distribution. It performs better than previously proposed CR based Decision Tree (CRDT) Model since an efficient discretization module has been added with it. We have evaluated the performance of our approach on some benchmark datasets from various domains to demonstrate the effectiveness of the proposed technique and also compared our model with Information Gain, Gain Ratio and Gini Index based models. Result shows that the proposed model outperforms other models in majority of the cases that we have considered in our experiment.

In this article, a basic idea about how a person who is blind can walk freely and do everything without any help from others or any assistance. The name of the proposed device is Intelligent Smart Belt and it is used for the blind people so that they can walk like a normal people. It is a belt in which any people can wear around there waist and does not need to hold anything in their hands. Many sensors are attached with the belt to sense any type of obstacles that appears in front of the blind people. The sensor data are in turn connected by an arduino, 1sheeld to understand the obstacles and what they are and it helps to communicate with the blind person using a mobile application.

In this problem, we have considered a city map network with a Centralised Charging Station (CCS) and a number of Battery Swapping Stations (BSSs). The CCS owns a set of delivery trucks with limited capacity. Each BSS can demand some fully charged batteries from the CCS, which needs to figure out a routing strategy for each of the delivery trucks. The strategy fulfills the demand of each BSS and at the same time optimizes the overall cost of delivery in terms of the distance travelled. A constrained programming based approach has been proposed to obtain an optimal routing of the delivery trucks considering the partial delivery of batteries.

In this paper, we address one of the many challenges of electric vehicles(EVs) that is the optimal placement of charging stations(citing) and allocating a certain number of charging ports(sizing) at these stations. We formulate a real life problem in the form of a city network that accounts for the uncertainty in the arrival of EVs, mathematically represented in the form of an optimization problem. The uncertainty in the flow of EVs can be approximated in a statistical distribution which can be analysed to determine the optimal allocation of the charging ports. Experiments suggest that our approach performs better when compared using certain performance metric with the existing research works.

We consider the problem of efficient scheduling of vehicle-to-grid (V2G), grid-to-vehicle (G2V), vehicle-to-vehicle (V2V) operations in a smart microgrid equipped with renewable energy resources and multiple types of charging ports to cater the requirements of Electric Vehicles (EVs) and essential loads of households. Our objective is to schedule the operations such that the total price paid to the utility-grid for the electrical energy is minimum. We propose a mixed integer linear programming (MILP) based formulation to obtain an optimal schedule of those operations. We present a heuristic approach to find a reasonable solution quickly.

Unaided authentication services provide the flexibility to login without being dependent on any external hardware. n-Session recording attack resilient unaided authentication services (n-SRRUASs) are known for setting high security standards against different client side threats. However, because of their authentication procedure, the authors have identified that these services cope poorly with handling the server side issues. Though modern days' research heavily depends on the honeywords (or fake passwords) as a countermeasure of server side threats, they have shown that the honeywords cannot be directly applied to n-SRRUAS. The authors' analysis shows that the idea of incorporating the honeywords directly into an n-SRRUAS is particularly difficult as it prevents the system from storing passwords after applying password-based key derivation function or in the form of a hashed string. In this study, they have proposed few generic principles for incorporating the honeywords into n-SRRUAS and show that the proposed principles are sufficient for incorporating the honeywords into any n-SRRUAS. Furthermore, with the help of an existing n-SRRUAS, they have shown that the proposed idea is truly implementable in practice to fill the existing gap.

In the domain of password security, research has made significant progress in handling different kinds of threats which require human intelligence factor to fix the vulnerabilities. In spite of having strong theoretical establishments, most of these defense mechanisms cannot be used in practice as humans have limitations in processing complex information. The little bit of good news is that very few research proposals in this field have shown the promises to be deployable in practice. This paper focuses on such one method - proposed by Roth et al. back in 2004, which provides adequate user-friendliness to enter Personal Identification Number (PIN) securely in the presence of human shoulder surfers. Surprisingly, the background algorithm of this method for validating users’ responses runs in linear time on a search space of cardinality 5 and hence, the validation process does not put much load on the authenticating device. Therefore, such human identification protocol can also be integrated into the IoT infrastructure for conducting a more secured login from the client-side. Having such advantages, though remained secure for almost ten years after its release in 2004, recently, few proposals revealed some serious vulnerable aspects of the Roth et al. ’s proposal. In this paper, we have taken an attempt to save this user-friendly form of authentication. Firstly, we have made a critical discussion on the importance of the targeted PIN entry method in the domain of usable security and then given a brief overview of the identified limitations of this protocol. Followed by this, a few initiatives have been taken to fix the identified vulnerabilities of Roth et al. ’s proposal by revising its working principle, while the login procedure and the usability standard of this method stay unaffected.

Non-dominated sorting is one of the prominent steps in developing any Pareto-dominance based multi-objective evolutionary algorithm. The computational complexity of any Pareto-dominance based multi-objective evolutionary algorithm primarily depends on this step. Thus, researchers are working on reducing the complexity of this step. Recently, an efficient approach for non-dominated sorting known as Best Order Sort (BOS) has been proposed. This approach is very efficient in terms of the number of comparisons between the solutions. Another advantage of this approach is that while comparing two solutions, the number of objectives which are compared is reduced from the actual number of objectives associated with each solution. However, in spite of these two advantages, this approach is not suitable in its current form for cases in which we have duplicate solutions. This paper generalizes BOS to handle duplicate solutions while retaining both of its advantages. We call this generalized version Generalized Best Order Sort (GBOS). The present work shows that BOS can be generalized to handle its limitation without compromising its time and space complexity.

Existing electrical grid systems have a limited amount of real-time monitoring and controlling capabilities of energy generation and consumption facilities, which trigger various technical issues including voltage overloading, demand–supply mismatch, peak load consumption, etc. Some of the primary reasons for these key issues have been identified to be the inefficient utilization of energy infrastructure and uncoordinated power consumption pattern among the consumers. In this paper, we propose a coordinated load scheduling and controlling algorithm to schedule controllable appliances with the objective to minimize peak load consumption. For this purpose, we model the problem into the strip packing problem, a well-known NP-hard problem, and discuss the applicability of existing heuristics in our problem setup. We then discuss a new offline heuristic solution, named MinPeak, specifically designed for load scheduling problem. We have conducted comprehensive simulation studies using available benchmark data sets and have performed extensive comparative analyses of the proposed algorithm with some of the well-known heuristics for strip packing problem. Furthermore, experiments have been carried out using practical electricity consumption data to evaluate the performance of the algorithm in real life. The results obtained are very encouraging in terms of reducing peak load consumption and overall efficiency of the system.

Residential, commercial, and industrial buildings have been reported to consume a large portion of the generated energy. With the introduction of smart grid and its energy optimization techniques, it is now possible to efficiently manage and control consumers’ energy usage to fulfil their demands with the existing energy generation infrastructure, which otherwise seems to be a backbreaking challenge. This paper presents an efficient energy management solution for buildings with a large number of thermostatic devices (air conditioners) that maintain the temperature of different thermal zones in a predefined range. The primary objective of this paper is to schedule the thermostatic devices in order to reduce total energy consumption by these devices when they are in operation for a very long duration of time, while maintaining the other constraints. We formulate it as a graph problem where minimum mean cycle will provide the desired solution. The proposed methodology ensures that at no point in time the power consumption goes beyond a certain peak power consumption limit. We also enhance the methodology to reduce peak load consumption. Furthermore, a fast greedy approach has been developed to efficiently scale up the aforementioned scheduling scheme for a large number of devices. Experimental results show that significant improvements can be obtained by the proposed approaches over existing algorithms in reducing average energy consumption.

Inverting hashed passwords by performing brute force computation is one of the latest security threats on password based authentication technique. New technologies are being developed for reducing complexity of brute force computation and these increase the success rate of inversion attack. Honeyword base authentication protocol can successfully mitigate this threat by making password cracking detectable. However, existing honeyword based methods have several limitations likeMultiple System Vulnerability, Weak DoS Resistivity, Storage Overhead, etc. In this paper, we have proposed a new modified-UI based honeyword generation approach, identified as Paired Distance Protocol (PDP), which overcomes most of the drawbacks of previously proposed honeyword generation approaches. The comprehensive analysis shows that PDP not only attains a high detection rate of 97.23%, but also reduces the storage overhead to a great extent.

Entity matching is to map the records in a database to their corresponding entities. It is a well-known problem in the field of database and artificial intelligence. In digital libraries such as DBLP, ArnetMiner, Google Scholar, Scopus, Web of Science, AllMusic, IMDB, etc., some of the attributes may evolve over time, i.e., they change their values at different instants of time. For example, affiliation and email-id of an author in bibliographic databases which maintain publication details of various authors like DBLP, ArnetMiner, etc. may change their values. A taxpayer can change his or her address over time. Sometimes people change their surnames due to marriage. When a database contains records of these natures and the number of records grows beyond a limit, then it becomes really challenging to identify which records belong to which entity due to the lack of a proper key. In the current paper, the problem of automatic partitioning of records is posed as an optimization problem. Thereafter, a genetic algorithm based automatic technique is proposed to solve the entity matching problem. The proposed approach is able to automatically determine the number of partitions available in a bibliographic dataset. A comparative analysis with the two existing systems – DBLP and ArnetMiner, over sixteen bibliographic datasets proves the efficacy of the proposed approach.

Demand response has been one of the efficient and highly effective energy management solutions for the smart grid environment, which uses various load curtailment and scheduling policies to minimize energy consumption and peak load demand. Recent advances in direct load control techniques show a significant reduction in peak load demand, resulting in smoother load profiles. However, a majority of the existing work misses out on the practical aspects where appliances are of mixed categories, viz., preemptive, non-preemptive, deferrable, non-deferrable, etc. In this paper, we consider addressing the problem of scheduling deferrable and non-deferrable appliances that have inter-dependency constraints among them. Considering the complexity of the problem, we propose a greedy algorithm to obtain near-optimal solutions relatively quicker than the optimal solutions. The performance of the proposed algorithm has been evaluated and compared with existing algorithms on real-world power consumption data. The results obtained show that the proposed mechanism is highly efficient and produces schedules with a 14% lesser peak as compared to the existing algorithms. Demand response has been one of the efficient and highly effective energy management solutions for the smart grid environment, which uses various load curtailment and scheduling policies to minimize energy consumption and peak load demand. Recent advances in direct load control techniques show a significant reduction in peak load demand, resulting in smoother load profiles. However, a majority of the existing work misses out on the practical aspects where appliances are of mixed categories, viz., preemptive, non-preemptive, deferrable, non-deferrable, etc. In this paper, we consider addressing the problem of scheduling deferrable and non-deferrable appliances that have inter-dependency constraints among them. Considering the complexity of the problem, we propose a greedy algorithm to obtain near-optimal solutions relatively quicker than the optimal solutions. The performance of the proposed algorithm has been evaluated and compared with existing algorithms on real-world power consumption data. The results obtained show that the proposed mechanism is highly efficient and produces schedules with a (Formula presented.) lesser peak as compared to the existing algorithms.

The worldwide energy consumption has been growing in aggregate at a tremendous rate, and a majority of the same is due to heating ventilation air conditioning (HVAC) loads in urban buildings. With the help of the recent advances in energy management and optimization techniques, the operations and functioning of these devices can now be managed and controlled efficiently for an improved energy consumption scenario and thereby reducing cost. In this article, we propose a multiobjective optimal scheduling framework based on Johnson's elementary circuit finding algorithm for controlling HVAC devices, specifically for buildings that require continuous thermal comfort maintenance. Two primary objectives addressed in this article are: minimizing power fluctuation and maximizing thermal comfortability of the users. We use standard comfortability indices to quantify thermal comfortability. To reduce the computation time, we also propose two intelligent improvement schemes that prune the exponential search space of Johnson's algorithm. Furthermore, a new greedy scheduling algorithm has been proposed to obtain near-optimal solutions efficiently. All the proposed approaches have been studied in a simulated environment depicting a real-world scenario to evaluate their efficiency and effectiveness for practical implementations, including a comparative analysis with Karp's minimum mean cycle algorithm in this problem setup.

We propose a coordinated scheduling approach for controllable electrical appliances along with the optimal usage of heterogeneous energy sources to minimize the energy drawn from the grid and thereby reducing electricity cost for the users. A mixed integer linear programming (MILP) formulation for the problem is presented to produce the optimal solutions, and an efficient heuristic algorithm is proposed to obtain results efficiently.

Many kinds of disease-related data are now available and researchers are constantly attempting to mine useful information out of these. Medical data are not always homogeneous and in structured form, and mostly they are time-stamped data. Thus, special care is required to prevent any kind of information loss during mining such data. Mining medical data is challenging as predicting the non-accurate result is often not acceptable in this domain. In this paper, we have analyzed a partially annotated coronary artery disease (CAD) dataset which was originally in a semi-structured form. We have created a set of some well-defined features from the dataset, and then build predictive models for CAD risk identification using different supervised learning algorithms. We then further enhanced the performances of the models using a feature selection technique. Experiments show that results are quite interesting, and are expected to help medical practitioners for investigating CAD risk in patients.

Due to the rising pollution and greenhouse gas emissions resulting from fossil fuel-based transportation systems, researchers and policymakers are pushing for Electric Vehicle (EV) that is envisaged as an efficient, eco-friendly alternative. However, due to their limited range and battery capacity, EVs need frequent charging, which is time-consuming and available at specific locations. Therefore, proper charge scheduling and route management of EVs is essential and significant. This paper addresses this problem by proposing an intelligent heuristic mechanism that ensures that the EVs are always routed through a path that minimizes the energy consumption and the total time to travel. We formulate it as a multi-objective optimization problem considering real-world specifications and constraints and propose a graph-based multi-objective heuristic algorithm (MoHA) to obtain the desired solutions quickly. Further, multiple variants of the proposed algorithm are proposed, and comparative analysis is performed on practical datasets. The proposed algorithm is evaluated based on some of the well-known performance metrics for multi-objective approaches. The results obtained show that the energy-aware-MoHA variant produced 32.39% better results in minimizing energy consumption, and time-aware-MoHA performed better in optimizing average time requirements by 24.32%. Moreover, the initial ordering of the EVs has significant importance on the proposed algorithm's overall performance.

Multiobjective based solution framework for entity matching.Bibliographic database is used.Automatic determination of number of clusters and partitions.New encoding strategy, new mutation operators are explored.Several distance measures and cluster validity indices are used. With the increasing use of on-line resources, the size of the bibliographic database is growing day by day. The available huge amount of data belong to various entities. It is difficult to automatically identify the records which belong to a particular entity. Mapping the records to the corresponding entity is termed as the entity matching problem. In bibliographic database many attributes change over time. For example - affiliation of an author changes frequently. Many authors generally use different email-ids. The names of co-authors also change with time. All these aspects have made the entity matching problem challenging. Generally an entity matching task is carried out by constructing a feature vector to represent a record, then a classifier is trained to classify each feature vector. But for bibliographic database it is very difficult and time consuming to generate some manually annotated labeled data to train a classifier. Inspired by this observation, we have proposed an unsupervised approach for entity matching problem using non-dominated sorting genetic algorithm-II (NSGA-II). A new encoding strategy is used to encode the clusters in the form of a chromosome. New mutation and crossover operators are proposed which are suitable for bibliographic data clustering. Different distance measures are used to measure the dissimilarities between records. Finally, solutions are evolved using the search capability of NSGA-II. Experimental evaluations are carried out with 247 different combinations of eight objective functions for eight different bibliographic datasets. A comparative analysis with two existing systems - DBLP and ArnetMiner, shows that the proposed technique can produce better results in many cases.

In this paper, we propose efficient load scheduling based demand side management schemes for the objective of peak load reduction. We propose two heuristic algorithms, named G-MinPeak and LevelMatch, which are based on the generalized two-dimensional strip packing problem, where each of the appliances has their specific timing requirements to be fulfilled. Furthermore, we have proposed some improvement schemes that try to modify the resulted schedule from the proposed heuristic algorithms to reduce the peak. All the proposed algorithms and improvement schemes are experimented using benchmark data sets for performance evaluation. Extensive simulation studies have been conducted using practical data to evaluate the performance of the algorithms in real life. The results obtained show that all the proposed methodologies are thoroughly effective in reducing peak load, resulting in smoother load profiles. Specifically, for the benchmark datasets, the deviation from the optimal values has been about 6% and 7% for LevelMatch and G-MinPeak algorithms respectively and by using the improvement schemes the deviations are further reduced up to 3% in many cases. For the practical datasets, the proposed improvement schemes reduce the peak by 5.21− 7.35 % on top of the peaks obtained by the two proposed heuristic algorithms without much computation overhead.

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 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.

We propose an efficient charge scheduling and routing mechanism for a set of electric vehicles with the objective to optimize average time and energy consumption for a tour. We propose a graph-based offline heuristic scheduling algorithm, whose performance evaluation is done in comparison to optimal results on graphs depicting real world scenario. Obtained preliminary results indicate that the proposed algorithm is highly efficient and effective in producing solutions that are significantly closer to optimal solutions.

Shoulder surfing attack is a great threat on password based authentication technique. As today's world has seen a rapid growth of mobile users thus, latest methodologies which are developed to address this attack preferably should support mobile users. In this paper we have proposed a new shoulder surfing resilient technique, MobSecure, which is designed to be used in smartphone and acts in a partially observable environment (where an auxiliary device is required for authentication). Additionally the proposed method is also capable of avoiding recording based attack for infinite number of authentication sessions. Allowing alphanumeric characters as password is one of the key properties of the proposed scheme here. Finally, we show that MobSecure attains highest security standard with moderate usability scores compared to existing partially observable approaches.

Password based authentication shows its vulnerability against inversion attack model in which adversary obtains plaintext password from its corresponding hashed value. To cope up with such attack, honeyword based authentication technique is introduced. In this technique, along with the original password of user, some dummy passwords or honeywords are also stored. Although this technique is good enough to address the aforementioned security breach, but use of additional storage to store the honeywords is still an overhead associated with such approach. In this paper, we have proposed few directions to minimize the storage cost of some of the existing honeyword generation approaches. We have even found that in some cases no additional storage overhead is required. A comparative analysis at the end also shows that the proposed techniques are able to raise some of the security features compared to existing honeyword generation approaches.

Graphical passwords have been taken as a potential alternative to alphanumeric passwords. Graphical password based authentication is widely used in many applications for system security and privacy. It increases ease of password use i.e., memorability of the password. With the rapid development of mobile devices, graphical passwords have already been implemented on smartphones. However, shoulder-surfing attack is a major threat to the security of graphical password systems. To overcome this problem, we proposed a novel graphical password authentication system, SGP. SGP uses a pattern of digits for the input of graphical password images. This pattern changes the position of input images in each authentication session. SGP prevents shoulder-surfing attacker to derive which password images are used by the user, even if the attacker records a complete login process. SGP does not use any secondary channels to resist shoulder-surfing attack.

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.

In general, alphanumeric passwords are used for authentication due to its simplicity and deployability. Strong and distinct alphanumeric passwords are inconvenient to memorize. So, users often pick weak passwords and reuse them. Also, users employ some simple tricks to derive passwords from a basic one. However, such weak and easy to derive passwords could not provide sufficient strength to protect users confidential resources. These passwords reduce the work of attackers to a great extent. Although the strong and distinct passwords reduce brute force attack, they are prone to theft and are often compromised under different vulnerabilities. Thus, by compromising one password, an attacker may gain access to other web-accounts where identical or similar passwords are used by the same user. In this paper, we propose drPass, a dynamic and reusable password generating protocol that generates high entropy passwords and thwarts various password stealing attacks. The proposed drPass scheme does not require any server-side change of existing websites for its implementation. It reduces the memory burden on users and also helps users to generate and maintain highly secure, distinct passwords for each site.

Non-dominated sorting has attracted a lot of attention of the research community due to its use in solving multi- and many-objective optimization problems. In recent years, several approaches for non-dominated sorting have been proposed. In this paper, we have developed a non-dominated sorting framework, namely DCNSRC (Divide-and-Conquer based Non-dominated Sorting with Reduced Comparisons). Based on this framework, two approaches have been proposed by varying the search technique. These approaches perform a lower number of dominance comparisons than various other approaches. The duplicate solutions are also handled efficiently. These approaches save various comparisons while comparing the two solutions. The proposed approaches are validated using some theoretical analyses. The number of dominance comparisons performed by the proposed framework are theoretically analyzed in three different scenarios, both in the worst and the best cases. Experimental results on synthetic datasets and the benchmark problems show the superiority of the proposed approach over state-of-the-art algorithms.

In general, it is difficult to remember a strong password i.e. a long and random password. So, the common tendency of a user is to select a weak alphanumeric password that is easy to remember. But the password which is easy to remember is also easy to predict. In contrast, the password that is very difficult to predict or requires more computation to break is also difficult to remember. To overcome this limitation of creating secure and memorable passwords, researchers have developed graphical password scheme which takes images as passwords rather than alphanumeric characters. But graphical password schemes are vulnerable to shoulder-surfing attack where an attacker can capture a password by direct observation. In this paper a graphical password scheme, namely SG-PASS is proposed which can prevent the shoulder-surfing attack by a human observer and also spyware attack, using a challenge response method.

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.

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.

Security analysis is a formal verification technique to ascertain certain desirable guarantees on the access control policy specification. Given a set of access control policies, a general safety requirement in such a system is to determine whether a desirable property is satisfied in all the reachable states. Such an analysis calls for the use of formal verification techniques. While formal analysis on traditional Role Based Access Control (RBAC) has been done to some extent, recent extensions to RBAC lack such an analysis. In this paper, we consider the temporal RBAC extensions and propose a formal technique using timed automata to perform security analysis by analyzing both safety and liveness properties. Using safety properties one ensures that something bad never happens while liveness properties show that some good state is also achieved. GTRBAC is a well accepted generalized temporal RBAC model which can handle a wide range of temporal constraints while specifying different access control policies. Analysis of such a model involves a process of mapping a GTRBAC based system into a state transition system. Different reduction rules are proposed to simplify the modeling process depending upon the constraints supported by the system. The effect of different constraints on the modeling process is also studied.

Classical PIN based authentication schemes are susceptible to shoulder surfing attacks and hence attacker may obtain secret credentials of legitimate user very easily. Some of the existing schemes that provide resistance against shoulder surfing attacks either require multiple rounds for entering single digit or some have dependency on external hardware or some of the schemes require complex computation to be done mentally in order to enter the PIN. Another possible security threat could be stealing the credentials if password file is compromised. In this paper, we propose a new PIN entry mechanism known as SPOSS which provides resilience against not only human-based shoulder surfing but also against recording attack (for one session) in which attacker may impose a recording device like camera to record the whole login session for future reference. SPOSS also provides security against password file compromise attack. Additionally, user authentication can be ensured by single round only without doing any complex computation and without any dependency of external hardware. Experimental analysis shows that proposed scheme achieves a good balance between usability and security parameters.

Traditionally the passwords are stored in hashed format. However, if the password file is compromised then by using the brute force attack there is a high chance that the original passwords can be leaked. False passwords -- also known as honeywords, are used to protect the original passwords from such leak. A good honeyword system is dependent on effective honeyword generation techniques. In this paper, the risk and limitations of some of the existing honeyword generation techniques have been identified as different notes. Three concepts -- modified tails, close number formation and caps key are introduced to address the existing issues. The experimental analysis shows that the proposed techniques with some preprocessing can protect high percentage of passwords. Finally a comparative analysis is presented to show how the proposed approaches stand with respect to the existing honeyword generation approaches.

With the ever-growing use of the internet for electronic commerce and data mining type applications, there seems to be a need for new network computing paradigms that can overcome the barriers posed by network congestion and unreliability. Mobile agent programming is a paradigm that enables programs to move from one host to another, do the processing locally and return results asynchronously. In this article, mobile computing is implemented by the use of a kernel programming language, Kernel Language for Agent Interaction and Mobility (KLAIM), to coordinate efficiently and streamline the functionality of a supply chain in terms of data transactions. The proposed KLAIM-based approach addresses the issues relating to mobility, integrity, privacy and security, which are a prime concern when operating in an open environment as normally encountered in a supply chain network. The proposed approach is supported by an example case study where an attempt is made to expedite and streamline the information transacti...

Non-domination level update problem is to sort the non-dominated fronts after insertion or deletion of a solution. Generally the solution to this problem requires to perform the complete non-dominated sorting which is too expensive in terms of number of comparisons. Recently an Efficient Non-domination Level Update (ENLU) approach is proposed which does not perform the complete sorting. For this purpose, in this paper a space efficient version of ENLU approach is proposed without compromising the number of comparisons. However this approach does not work satisfactorily in all the cases. So we have also proposed another tree based approach for solving this non-domination level update problem. In case of insertion, the tree based approach always checks for same number of fronts unlike linear approach in which the number of fronts to be checked depends on the inserted solution. The result shows that in case where all the solutions are dominating in nature the maximum number of comparisons using tree based approach is $\mathcal{O}(\log N)$ as opposed to $\mathcal{O}(N)$ in ENLU approach. When all the solutions are equally divided into $K$ fronts such that each solution in a front is dominated by all the solutions in the previous front then the maximum number of comparisons to find a deleted solution in case of tree based approach is $K{-}\log K$ less than that of ENLU approach. Using these approaches an on-line sorting algorithm is also proposed and the competitive analysis of this algorithm is also presented., Comment: 18 pages and 6 figures

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.

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.

In a conventional password based authentication system, an adversary can obtain login credentials by performing shoulder surfing. When such attacks are performed by human users with limited cognitive skills and without any recording device then it is referred as weak shoulder surfing attack. Existing methodologies that avoid such weak shoulder surfing attack, comprise of many rounds which may be the cause of fatigue to the general users. In this paper we have proposed a methodology known as Multi Color (MC) method which reduces the number of rounds in a session to half of previously proposed methodologies. Then using the predictive human performance modeling tool we have shown that proposed MC method is immune against weak shoulder surfing attack and also it improves the existing security level.