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Publication Date: 2011
pp. 422-427
Combinatorial auctions are auctions in which bidders bid on combinations of items, bundles, instead of on individual items. In these auctions, bidders always tend to construct and bid on the most beneficial bundles of items, while facing a substantial number of items. Since there are a huge number of items available in a combinatorial auction, deciding on which items to put in bundles is a challenge for bidders. In combinatorial auctions, bundling of items and bidding on the best possible bundles are of great importance and developing an efficient bidding strategy can increase quality of the auctions considerably. In this paper, we have proposed an efficient bidding strategy. Performance of the proposed strategy in various markets has been simulated and compared with the bidding strategies already available in the literature. The obtained results show that in comparison with the previously available bidding strategies, the proposed strategy is more beneficial to both bidders and auctioneer, especially in markets where there is a considerable difference between values of items. © 2011 IEEE.
Publication Date: 2013
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 pp. 252-257
Collaborative filtering has been considerably successful in improving recommender systems both in the literature and commercial applications. Most of the algorithms designed up to now consider users' ratings equally and do not pay attention to the fact that users' interests or requirements might change over the time. In this paper a collaborative filtering based recommender system is designed which tries to find each user's interests to each group of items, thus resulting to a better prediction of ratings a user will give to an item in the near future. This goal is achieved through using the ratings' timestamp, predefined groups of items, and defining a new similarity measure among users. Unlike standard collaborative filtering methods and many new ones in which similarity between users is defined as a single number, in this research we define similarity between users as "group similarity" which is an array of similarity values between items of each group rated by two users. Predefined groups for items e.g. genres for movies, are used as groups for items. Also for calculating similarity, different weights will be dedicated to ratings of each user based on the ratings' timestamp, i.e. a rating with higher timestamp will receive a higher weight. Empirical tests show that our proposed algorithm works better than standard User-based and Item-based collaborative filtering methods in the case of predicting users' interests in the near future with higher precision. Also it is empirically shown that our algorithm works considerably well for cold-start users. © 2013 IEEE.
Rahimi, S.K. ,
Lano, K. ,
Sharbaf, M. ,
Karimi, M. ,
Alfraihi, H. Publication Date: 2020
Journal of Systems and Software (01641212) 169
The quality of model transformations (MT) has high impact on model-driven engineering (MDE) software development approaches, because of the central role played by transformations in MDE for refining, migrating, refactoring and other operations on models. For programming languages, a popular paradigm for code quality is the concept of technical debt (TD), which uses the analogy that quality flaws in code are a debt burden carried by the software, which must either be ‘redeemed’ by expending specific effort to remove its flaws, or be tolerated, with ongoing additional costs to maintenance due to the flaws. Whilst the analysis and management of quality flaws and TD in programming languages has been investigated in depth over several years, less research on the topic has been carried out for model transformations. In this paper we investigate the characteristics of quality flaws and technical debt in model transformation languages, based upon systematic analysis of over 100 transformation cases in four leading MT languages. Based on quality flaw indicators for TD, we identify significant differences in the level and kinds of technical debt in different MT languages, and we propose ways in which TD in MT can be reduced and managed. © 2020 Elsevier Inc.
Publication Date: 2024
Automated Software Engineering (09288910) 31(2)
The Inter-Component Communication (ICC) model in Android enables the sharing of data and services among app components. However, it has been associated with several problems, including complexity, support for unconstrained communication, and difficulties for developers to understand. These issues have led to numerous security vulnerabilities in Android ICC. While existing research has focused on specific subsets of these vulnerabilities, it lacks comprehensive and scalable modeling of app specifications and interactions, which limits the precision of analysis. To tackle these problems, we introduce VAnDroid3, a Model-Driven Reverse Engineering (MDRE) framework. VAnDroid3 utilizes purposeful model-based representations to enhance the comprehension of apps and their interactions. We have made significant extensions to our previous work, which include the identification of six prominent ICC vulnerabilities and the consideration of both Intent and Data sharing mechanisms that facilitate ICCs. By employing MDRE techniques to create more efficient and accurate domain-specific models from apps, VAnDroid3 enables the analysis of ICC vulnerabilities on intra- and inter-app communication levels. We have implemented VAnDroid3 as an Eclipse-based tool and conducted extensive experiments to evaluate its correctness, scalability, and run-time performance. Additionally, we compared VAnDroid3 with state-of-the-art tools. The results substantiate VAnDroid3 as a promising framework for revealing Android inter-app ICC security issues. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Publication Date: 2009
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 pp. 131-138
Anonymity is an important issue in information security, which its main goal is to protect entities privacy in the systems. Different methods and protocols (with different types of anonymity services) have been developed so far to provide special anonymity requirements of applications. Each of these systems has been developed with different ad hoc approaches. In this paper we present a conceptual framework that makes specification, analysis and design of anonymity applications more systematic. To do this, first we go toward presenting a conceptual model of anonymity which can be used in clear description of different aspects of anonymity. Then we extract a list of anonymity primitives from the existing anonymity providing methods. These primitives are base functions which can be composed to form anonymity services to provide specified anonymity requirements of the system. Copyright 2009 ACM.
Publication Date: 2026
Frontiers in Computer Science (26249898) 7
Introduction: In the software development life cycle, collaborative modeling through multiple projective views of a single, shared model is a critical activity that enables effective collaboration among experts and stakeholders. Real-time optimistic collaboration in multi-view modeling allows concurrent modifications but often introduces inconsistencies that must be resolved to achieve an integrated and valid model. Existing inconsistency management methods frequently focus on isolated repairs or offer limited alternatives, lacking support for collaborative dynamics and configurable resolution strategies. This study aims to develop a configurable framework for managing intra-model inconsistencies in real-time multi-view collaborative modeling environments. Methods: We propose a novel framework for inconsistency management tailored to multi-view collaborative modeling, based on Model-Driven Engineering (MDE) principles. The framework supports real-time modeling scenarios and enables change propagation according to the online collaboration mode. Key components include a consistency oracle and incremental consistency checking, which together manage the integration of model changes and overlaps. We introduce the COMIM approach, which assists collaborators in handling inconsistencies by considering team interactions, individual ownership, and configurable repair strategies. Results: The framework was evaluated through a case study involving multi-view collaborative modeling sessions. Empirical results demonstrate the feasibility and effectiveness of the COMIM approach in maintaining consistency during concurrent modeling activities. The system performed efficiently for teams of up to seven concurrent users, successfully managing change propagation, detecting inconsistencies incrementally, and supporting configurable resolution aligned with collaborative priorities. Discussion: The proposed framework effectively addresses the complexities of repairing inconsistencies across diverse software models in a collaborative setting. By emphasizing collaborative dynamics, our approach advances traditional inconsistency management methods, which often lack personalization and configurability. Future work may explore scalability to larger teams and adaptation to additional modeling paradigms. Copyright © 2026 Alsharuee, Sharbaf and Tork Ladani.
Publication Date: 2011
pp. 57-60
Keyword based search scheme imposes the problem of representing a lot of web pages in the search engines. Query expansion with relevant words increases the performance of search engines, but finding and using the relevant words is an open problem. In this research we describe a new model for query expansion which employs user context and semantic concepts to discover new words for obtaining accurate results. Experimental results show an enhancement on information retrieval performance comparing to the traditional approaches. © 2011 IEEE.
Publication Date: 2011
ADVANCES IN ARTIFICIAL INTELLIGENCE (03029743) 6657pp. 301-312
In this paper we have proposed a context-aware reputation-based trust model for multi-agent environments. Due to the lack of a general method for recognition and representation of context notion, we proposed a functional ontology of context for evaluating trust (FOCET) as the building block of our model. In addition, a computational reputation-based trust model based on this ontology is developed. Our model benefits from powerful reasoning facilities and the capability of adjusting the effect of context on trust assessment. Simulation results shows that an appropriate context weight results in the enhancement of the total profit in open systems.
Publication Date: 2007
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (03029743) 4749pp. 404-409
In this paper we have proposed an approach to extend the existing service-oriented architecture reference model by taking into consideration the hierarchical human needs model, which can help us in determining the user's goals and enhancing the service discovery process. This is achieved by enriching the user's context model and representing the needs model as a specific ontology. The main benefits of this approach are improved service matching, and ensuring better privacy as required by users in utilizing specific services like profile-matching. © Springer-Verlag Berlin Heidelberg 2007.
Publication Date: 2009
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 pp. 29-35
In this paper, we introduce a continuous double auction method for grid resource allocation in which resources are considered as provider agents and users as consumer agents. In each time step, each provider agent determines its requested value based on its workload and each consumer agent determines its bid value based on two constraints: the remaining time for bidding, and the remaining resources for bidding. We study this method in terms of economic efficiency and system performance. Experimental results show that the proposed method is better than Earliest Deadline First (EDF) method, which is a default strategy in many schedulers.
Publication Date: 2024
Pervasive and Mobile Computing (15741192) 105
In recent years, one type of complex network called the Social Internet of Things (SIoT) has attracted the attention of researchers. Controllability is one of the important problems in complex networks and it has essential applications in social, biological, and technical networks. Applying this problem can also play an important role in the control of social smart cities, but it has not yet been defined as a specific problem on SIoT, and no solution has been provided for it. This paper addresses the controllability problem of the temporal SIoT network. In this regard, first, a definition for the temporal SIoT network is provided. Then, the unique relationships of this network are defined and modeled formally. In the following, the Controllability problem is applied to the temporal SIoT network (CSIoT) to identify the Minimum Driver nodes Set (MDS). Then proposed CSIoT is compared with the state-of-the-art methods for performance analysis. In the obtained results, the heterogeneity (different types, brands, and models) has been investigated. Also, 69.80 % of the SIoT sub-graphs nodes have been identified as critical driver nodes in 152 different sets. The proposed controllability deals with network control in a distributed manner. © 2024 Elsevier B.V.
Publication Date: 2024
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 pp. 253-259
This study presents a method for the automatic identification of micro-cracks in photovoltaic solar modules using deep learning techniques. The main challenge in this research is the lack of labeled data and class imbalance for the detection of micro-cracks. The proposed method employs a multi-stage approach. Initially, 10% of the dataset is manually labeled to train a simple convolutional neural network model. This model is then used to generate pseudo-labels for the unlabeled data using a semi-supervised approach. The pseudo-labels are manually reviewed to increase the number of micro-crack samples in the training set. Data augmentation techniques are also applied to increase the size and diversity of the training dataset. Finally, the pre-trained ResNet-50 model is fine-tuned on the expanded labeled dataset for accurate detection of microcracks. Advanced preprocessing steps, including solar cell segmentation, cropping, and data augmentation, have been performed. The class imbalance problem is addressed through undersampling and weighted loss functions. The experimental results demonstrate the effectiveness of the proposed method, achieving an accuracy of 0.978 and an F1-score of 0.797 in the detection of micro-cracks in electroluminescence images of solar panels. This study provides insights into the use of limited labeled data for training robust deep learning models for the identification of defects in solar modules. © 2024 IEEE.
Publication Date: 2018
Reliability Engineering and System Safety (9518320) pp. 13-24
This paper presents an approach to optimize the reliability and makespan of hardware task graphs, running on FPGA-based reconfigurable computers, in space-mission computing applications with dynamic soft error rates (SERs). Thus, with rises and falls of the SER, the presented approach dynamically generates a set of solutions that apply redundancy-based fault tolerance (FT) techniques to the running tasks. The set of solutions is generated by decomposing the task graph into multiple subgraphs, applying a multi-objective optimization algorithm to the subgraphs separately, and finally combining and filtering out the obtained solutions of the subgraphs. In this regard, a heuristic has been proposed to decompose task graphs in such a way that a high coverage of the true Pareto set is attained. The experiments show that the presented approach covers 97.37% of the true Pareto set and improves the average computation time of generating the Pareto set from 6.29 h to 81.86 ms. In addition, it outperforms the NSGA-II algorithm in terms of the Pareto set coverage and computation time. Additional experiments demonstrate the advantages of the presented approach over the state-of-the-art adaptive FT techniques in dynamic environments. © 2018 Elsevier Ltd
Question-answering systems, characterized by their fundamental functions of question classification, information retrieval, and answer selection, demand refinement to enhance precision in retrieving exact answers. Question classification, a cornerstone task, anticipates the probable answer to a posed query. However, the performance of question classification algorithms is hampered, particularly in agglutinative languages with complex morphology like Persian, where linguistic resources are limited. In this study, we propose a novel multi-layer Long-short-term memory (LSTM) Attention Convolutional Neural Network (CNN) (LACNN) classifier, tailored to extract pertinent information from Persian language contexts. Notably, this model operates autonomously, obviating the need for prior knowledge or external features. Moreover, we introduce UIMQC, the first medical question dataset in Persian, derived from the English GARD dataset. The inquiries within UIMQC are inherently intricate, often pertaining to rare diseases necessitating specialized diagnosis. Our experimental findings demonstrate a notable enhancement over baseline methods, with a 9% performance increase on the UTQC dataset, and achieving 67.08% accuracy on the UIMQC dataset. Consequently, we advocate for the adoption of the LACNN model in various morphological analysis tasks across low-resource languages, as in Question Answering systems it improves the performance for retrieving accurate answers to the users’ queries. ©2024 IEEE.
Publication Date: 2010
International Journal of Innovative Computing, Information and Control (13494198) 6(9)pp. 4219-4234
Scheduling is one of the core steps to efficiently exploit the capabilities of emergent computational systems such as grid. Grid environment is a dynamic, heterogeneous and unpredictable one sharing different services among many different users. Because of heterogeneous and dynamic nature of grid, the methods used in traditional systems could not be applied to grid scheduling and therefore new methods should be looked for. This paper represents a discrete Particle Swarm Optimization (DPSO) approach for grid job scheduling. PSO is a population based search algorithm based on the simulation of the social behavior of bird flocking and fish schooling. Particles fly in problem search space to find optimal or nearoptimal solutions. In this paper, the scheduler aims at minimizing makespan and flowtime simultaneously in grid environment. Experimental studies illustrate that the proposed method is more efficient and surpasses those of reported metaheuristic algorithms for this problem. © 2010 ICIC INTERNATIONAL.
Publication Date: 2007
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 pp. 203-212
Web services are self-contained, modular units of application logic which provide business functionality to other applications via Internet connections. Several models have been used to compose Web services which are mainly served at specification level and provide static data dependent coordination processes. Hence they can not support reconfigurable dynamic coordination processes in which participant Web services and the coordination process itself will not be known explicitly prior to execution and would be determined dynamically at run time. In this paper we present a framework to coordinate Web services using Reo coordination language. Reo is a channel-based exogenous coordination language which has a formal basis and supports loose coupling, distribution, dynamic reconfiguration and mobility. Given that Web services are inherently loosely coupled and primarily built independently, the channel-based structure of Reo and its reconfigurability will provide a reconfigurable coordination mechanism for Web service composition. The proposed approach is a distributed dynamic orchestration framework which uses Reo channels as a communication means between Web services and benefits from Reo reconfiguration property to provide a dynamic coordination process. Due to data independence property of Reo, the proposed model is a data neutral framework which is mainly focused on coordination. In this paper we also present a number of case studies by using the proposed framework and investigate its pros and cons through these case studies. © 2007 IEEE.
Publication Date: 2011
pp. 53-56
Web services are increasingly used to integrate and build business application on the internet. Failure of web services is not acceptable in many situations such as online banking, so fault tolerance is a key challenge of web services. Web service architecture still lacks facilities to support fault tolerance. This paper proposes a fault tolerant architecture for web services by increasing the reliability and availability, the architecture is based on application-level and transport-level logging of requests and replies, N-Version and active replication techniques. The proposed architecture is client transparent and provides fault tolerance even for requests being processed at the time of server failure. © 2011 IEEE.
Publication Date: 2006
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 2pp. 3028-3033
Multi agent systems are applied as a solution for distributed IT systems. Organizational concepts are usually applied to analyze and design such systems. Thus, a multi agent system can be seen as an organization which coordinates agent interactions. In this paper we propose a formal model to specify the coordination behavior of a multi agent system organization. This formal model enables the developers to have a cross checking between the agent interactions, the organizational structure and the coordination behavior of the organization. We can also apply this formal model to evaluate the system properties such as security. © 2006 IEEE.
Publication Date: 2017
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025 pp. 13-18
Trust and reputation systems (TRSs) are used as decision making criterion in many modern systems. In these systems normally a set of agents cooperate with each other to achieve system/own goals based on ethical norms of the system. The security of a TRS against malicious agents who try to bypass the ethical norms of the system can be evaluated using simulation or verification techniques in which both of them need to have formal models of TRSs. There are a few works who tried to present formalisms for modeling TRSs; however most of them are either unsophisticated or improper for checking security properties. In this paper we introduce a formal model of an agent interaction system along with the underlying TRS which is capable of formalizing the attacker behaviors. The presented formalism is suitable to be used in simulation or verification schemes for analyzing the security and robustness of TRSs. To demonstrate the sufficiency and capabilities of our model, eBay reputation system as a famous TRS is formalized with the presented model. © 2017 IEEE.
Publication Date: 0
pp. 169-174
Task allocation, as an important issue in multi-agent systems (MAS), is defined as allocating the tasks to the agents such that maximum tasks are performed in minimum time. The vast range of application domains, such as scheduling, cooperation in crisis management, and project management, deal with the task allocation problem. Despite the plethora of algorithms that are proposed to solve this problem in different application domains, research on proposing a formalism for this problem is scarce. Such a formalism can be used as a way for better understanding and analyzing the behavior of real-world systems. In this paper, we propose a new formalism for specifying capability-based task allocation in MAS. The formalism can be used in different application domains to help domain experts better analyze and test their algorithms with more precision. To show the applicability of the formalism, we consider two algorithms as the case studies and formalize the inputs and outputs of these algorithms using the proposed formalism. The results indicate that our formalism is promising for specifying the capability-based task allocation in MAS at a proper level of abstraction. © 2021 IEEE.
Publication Date: 2020
pp. 1-11
Verifying the consistency of model merging is an important step towards the support for team collaboration in software modeling and evolution. Since merging conflicts are inevitable, this has triggered intensive research on conflict management in different domains. Despite these efforts, techniques for high-level conflict representation have hardly been investigated yet. In this paper, we propose an approach to specify model merging conflicts. This approach includes the Conflict Pattern Language (CPL), a formalism for specifying conflicts in different modeling languages. CPL is based on the OCL grammar and is tooled by an editor and a parser. CPL facilitates the slow and error-prone task of specifying model merging conflicts and can be used to specify conflicts in any EMF-based model. We evaluated our approach with a case study, including five different conflict cases. The results are promising about how CPL can be used for specifying syntactic and semantic conflicts. © 2020 Association for Computing Machinery.
Publication Date: 2012
ETRI Journal (12256463) 34(5)pp. 743-752
Semantic interpretation of the relationship between noun compound (NC) elements has been a challenging issue due to the lack of contextual information, the unbounded number of combinations, and the absence of a universally accepted system for the categorization. The current models require a huge corpus of data to extract contextual information, which limits their usage in many situations. In this paper, a new semantic relations interpreter for NCs based on novel lightweight binary features is proposed. Some of the binary features used are novel. In addition, the interpreter uses a new feature selection method. By developing these new features and techniques, the proposed method removes the need for any huge corpuses. Implementing this method using a modular and plugin-based framework, and by training it using the largest and the most current fine-grained data set, shows that the accuracy is better than that of previously reported upon methods that utilize large corpuses. This improvement in accuracy and the provision of superior efficiency is achieved not only by improving the old features with such techniques as semantic scattering and sense collocation, but also by using various novel features and classifier max entropy. That the accuracy of the max entropy classifier is higher compared to that of other classifiers, such as a support vector machine, a Naïve Bayes, and a decision tree, is also shown. © 2012 ETRI.
Publication Date: 2009
International Journal of Human Computer Studies (10959300) 67(1)pp. 1-35
Our everyday lives and specially our commercial transactions involve complex negotiations that incorporate decision-making in a multi-issue setting under utility constraints. Negotiation as a key stage in all commercial transactions has been proliferated by applying decision support facilities that AI techniques provide. Recently, Distributed Artificial Intelligence techniques have been evolved towards multi-agent systems (MASs) where each agent is an intelligent system that solves a specific problem. Incorporating MAS into e-commerce negotiation and bargaining has brought even more potential improvement in efficiency and effectiveness of business systems by automating several of the most time consuming and repetitive stages of the buying process. In bargaining, participants with opposing interests communicate and try to find mutually beneficial agreements by exchanging compromising proposals. However, recent studies on commercial bargaining and negotiation in MASs lack a personality model. Indeed, adding personality to intelligent agents makes them more human-like and increases their flexibility. We investigate the role of personality behaviors of participants in multi-criteria bilateral bargaining in a single-good e-marketplace, where both parties are OCEAN agents based on the five-factor (Openness, Conscientiousness, Extraversion, Agreeableness, and Negative emotions) model of personality. We do not aim to determine strategies that humans should use in negotiation, but to present a more human-like model to enhance the realism of rational bargaining behavior in MASs. First, this study presents a computational approach based on a heuristic bargaining protocol and a personality model, and second, considers the issue of what personality traits and behaviors should be investigated in relation to automated negotiations. We show the results obtained via the simulation on artificial stereotypes. The results suggest and model compound personality style behaviors appropriate to gain the best overall utility in the role of buyer and seller agents and with regard to social welfare and market activeness. This personality-based approach can be used as a predictive or descriptive model of human behavior to adopt in appropriate situations in many areas involving negotiation and bargaining (e.g., commerce, business, politics, military, etc.) for conflict prevention and resolution. This model can be applied as a testbed for comparing personality models against each other based on human data in different negotiation domains. © 2008 Elsevier Ltd. All rights reserved.
Publication Date: 2021
Expert Systems with Applications (9574174)
In the Authorship Attribution (AA) task, the most likely author of textual documents, such as books, papers, news, and text messages and posts are identified using statistical and computational methods. In this paper, a new computational approach is presented for identifying the most likely author of text documents. The proposed solution emphasizes lazy profile-based classification and, by using the Term Frequency-Inverse Document Frequency (TF_IDF) scheme, introduces a new measure for identifying important terms of documents. The importance of the terms is then used to calculate the similarity between an anonymous document and known documents. The proposed solution works with raw text documents and does not require any NLP tools for preprocessing, which makes it language-independent. The efficiency of the proposed solution has been evaluated by conducting several experiments on two English and Persian datasets, each of which contains six corpora with different number of authors. The obtained results demonstrate that the proposed solution outperforms state-of-the-art stylometric features, employed by seven well-known classifiers, by obtaining 0.902 accuracy for the English dataset and 0.931 accuracy for the Persian dataset. In addition, supplementary experiments have been conducted to evaluate the effects of documents’ length on the accuracy of the proposed solution, to examine the computation time of the proposed solution and competitive classifiers, and to identify the most effective stylometric features and classifiers. © 2021 Elsevier Ltd
Publication Date: 2021
pp. 28-34
Evasion techniques are used by some Android malware to hide their malicious behavior and to hinder their execution during the dynamic analysis process. Many tools tackle such evasions by using a manually created list of API functions (as sources of evasions) to detect these evasions. As an important consequence, no matter how good the tool is, it can only guarantee to defeat these evasions and extract the real behavior of the malware if its list of evasion sources is complete. This way, if some evasion sources are missing from the list or when similar API functions are used, the dynamic analysis can be hindered. In this paper, we propose a machine learning approach to detect and categorize various evasion sources in Android malware. The proposed approach uses a manually collected training dataset to train two classifiers. The first classifier is used to detect the evasion nature of the Android API methods, while the second classifier is used to categorize the detected evasion sources into predefined categories. We applied the proposed approach to a large number of methods extracted from Android API 27. The proposed approach could detect hundreds of evasions with accuracy of 92.8% for the first classifier and 90.5% for the second classifier. The evaluation for 500 real-world samples showed that many of the evasions are detected by our approach, are not considered by the state-of-the-art dynamic analysis frameworks that are indeed used by malware samples. © 2021 IEEE.
Publication Date: 2023
ISeCure (20083076) 15(1)pp. 59-71
Sensitive methods are those that are commonly used by Android malware to perform malicious behavior. These methods may be either evasion or malicious payload methods. Although there are several approaches to handle these methods for performing effective dynamic malware analysis, generally most of them are based on a manually created list. However, the performance shown by the selected approaches is based on the completeness of the manually created list that is not almost a complete and up-to-date one. Missing some sensitive methods causes to degrade the overall performance and affects the effectiveness of analyzing Android malware. In this paper, we propose a machine learning approach to predict new sensitive methods that might be used in Android malware. We use a manually collected training dataset to train two classifiers: the first one is used to detect the sensitivity nature of the Android methods, and the second one is used to categorize the detected sensitive methods into predefined categories. We applied the proposed approach to a large number of methods extracted from Android API 27. The proposed approach is able to predict hundreds of sensitive methods with the accuracy of 94.4% for the first classifier and 92.8% for the second classifier. To evaluate the proposed approach, we built a new list of the detected sensitive methods and used it in a number of tools to perform dynamic malware analysis. The proposed model found various sensitive methods that were not considered before by any other tools. Hence, the effectiveness of these tools in performing dynamic analysis is increased.(c) 2020 ISC. All rights reserved.
Publication Date: 2015
2025 29th International Computer Conference, Computer Society of Iran, CSICC 2025
MapReduce as a programming model for parallel data processing has been used in many open systems such as cloud computing and service-oriented computing. Collusive behavior of worker entities in MapReduce model can violate integrity concern of open systems. In this paper, a MapReduce-based algorithm for parallel collusion detection of malicious workers has been proposed. This algorithm uses a voting matrix that is represented as a list of voting values of different workers. Three phases of majority selection, correlation counting and correlation computing are designed and implemented in this paper. Preliminary results show that speedup of 1.8 and efficiency of about 70% is achieved using data set containing 2000 worker's votes. © 2015 IEEE.
Publication Date: 2022
IEEE Access (21693536) 10pp. 37457-37476
Wireless sensor networks (WSN) are important communication components of an internet of things (IoT). With the development of IoT and the increasing number of connected devices, network structure management and maintenance face the serious challenge of energy consumption. By balancing the network load, the energy consumption can be improved effectively. In the conventional WSN architecture, the two prerequisites of the load-balancing mechanism, flexibility and adaptability, are difficult to achieve. software-defined networking (SDN) is a novel network architecture that can promote flexibility and adaptability using a centralized controller. In this paper, a novel SDN architecture aimed at reducing load distribution and prolonging lifetime is proposed, which consists of different components such as topology, BS and controller discovery, link, and virtual routing. Accordingly, a new mechanism is proposed for load-balancing routing through SDN and virtualization. Through direct monitoring of the link load information and the network running status, the employed OpenFlow protocol can determine load-balancing routing for every flow in different IoT applications. The flows in different resource applications can be directed to a base station (BS) via various routes. This implementation reduces the exchange of network status and other relevant information. Virtual routing aims to weigh forward nodes and select the best node for each IoT application. The simulation results show the distribution of load over the network in the proposed algorithm and are characterized by the balanced network energy consumption, but also it prolongs network lifetime in comparison to the LEACH, improved LEACH, and LEACH-C algorithms. © 2013 IEEE.
Publication Date: 2011
pp. 402-408
In the MAC protocols based on the S-MAC scheme, usually the combination of periodic sleep/listen scheduling and four-way handshake mechanism is employed to reduce idle listening and avoid interference. However, this combination greatly degrades network capacity and results in high end-to-end latency. In this paper, we propose Adaptive IAMAC to increase channel utilization and improve communication efficiency, specifically in large-scale sensor networks with low duty cycle. Adaptive IAMAC allows multiple nodes to transmit to their common parent during a frame. Moreover, it includes the adaptive parent selection mechanism, which enables the nodes to change their parent according to the currently overheard control packets at the MAC layer. Through these techniques, Adaptive IAMAC enhances network throughput, reduces end-to-end latency, and moderates the overhead of four-way handshake mechanism. Simulation results confirm that Adaptive IAMAC provides significant improvements over S-MAC in terms of throughput, latency, and energy efficiency. © 2011 IEEE.
Publication Date: 0
pp. 72-76
Question Answering is a hot topic in artificial intelligence and has many real-world applications. This field aims at generating an answer to the user's question by analyzing a massive volume of text documents. Answer Selection is a significant part of a question answering system and attempts to extract the most relevant answers to the user's question from the candidate answers pool. Recently, researchers have attempted to resolve the answer selection task by using deep neural networks. They first employed the recurrent neural networks and then gradually migrated to convolutional neural networks. Nevertheless, the use of language models, which is implemented by deep neural networks, has recently been considered. In this research, the DistilBERT language model was employed as the language model. The outputs of the Question Analysis part and Expected Answer Extraction component are also applied with [CLS] token output as the final feature vector. This operation leads to improving the method performance. Several experiments are performed to evaluate the effectiveness of the proposed method, and the results are reported based on the MAP and MRR metrics. The results show that the MAP values of the proposed method improved by 0.6%, and the MRR metric is improved by 0.2%. The results of our research show that using a heavy language model does not guarantee a more reliable method for answer selection problem. It also shows that the use of particular words, such as Question Word and Expected Answer word, can improve the performance of the method. © 2020 IEEE.
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