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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.

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.

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.

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.

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.

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.

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.