Biblio
Android malware family classification is an advanced task in Android malware analysis, detection and forensics. Existing methods and models have achieved a certain success for Android malware detection, but the accuracy and the efficiency are still not up to the expectation, especially in the context of multiple class classification with imbalanced training data. To address those challenges, we propose an Android malware family classification model by analyzing the code's specific semantic information based on sensitive opcode sequence. In this work, we construct a sensitive semantic feature-sensitive opcode sequence using opcodes, sensitive APIs, STRs and actions, and propose to analyze the code's specific semantic information, generate a semantic related vector for Android malware family classification based on this feature. Besides, aiming at the families with minority, we adopt an oversampling technique based on the sensitive opcode sequence. Finally, we evaluate our method on Drebin dataset, and select the top 40 malware families for experiments. The experimental results show that the Total Accuracy and Average AUC (Area Under Curve, AUC) reach 99.50% and 98.86% with 45. 17s per Android malware, and even if the number of malware families increases, these results remain good.
Every day, huge amounts of unstructured text is getting generated. Most of this data is in the form of essays, research papers, patents, scholastic articles, book chapters etc. Many plagiarism softwares are being developed to be used in order to reduce the stealing and plagiarizing of Intellectual Property (IP). Current plagiarism softwares are mainly using string matching algorithms to detect copying of text from another source. The drawback of some of such plagiarism softwares is their inability to detect plagiarism when the structure of the sentence is changed. Replacement of keywords by their synonyms also fails to be detected by these softwares. This paper proposes a new method to detect such plagiarism using semantic knowledge graphs. The method uses Named Entity Recognition as well as semantic similarity between sentences to detect possible cases of plagiarism. The doubtful cases are visualized using semantic Knowledge Graphs for thorough analysis of authenticity. Rules for active and passive voice have also been considered in the proposed methodology.
This paper present an approach to automate the conversion of Natural Language Query to SQL Query effectively. Structured Query Language is a powerful tool for managing data held in a relational database management system. To retrieve or manage data user have to enter the correct SQL Query. But the users who don't have any knowledge about SQL are unable to retrieve the required data. To overcome this we proposed a model in Natural Language Processing for converting the Natural Language Query to SQL query. This helps novice user to get required content without knowing any complex details about SQL. This system can also deal with complex queries. This system is designed for Training and Placement cell officers who work on student database but don't have any knowledge about SQL. In this system, user can also enter the query using speech. System will convert speech into the text format. This query will get transformed to SQL query. System will execute the query and gives output to the user.
Expressing and matching the security policy of each participant accurately is the precondition to construct a secure service composition. Most schemes presently use syntactic approaches to represent and match the security policy for service composition process, which is prone to result in false negative because of lacking semantics. In this paper, a novel approach based on semantics is proposed to express and match the security policies in service composition. Through constructing a general security ontology, the definition method and matching algorithm of the semantic security policy for service composition are presented, and the matching problem of policy is translated into the subsumption reasoning problem of semantic concept. Both the theoretical analysis and experimental evaluation show that, the proposed approach can present the necessary semantic information in the representation of policy and effectively improve the accuracy of matching result, thus overcome the deficiency of the syntactic approaches, and can also simplify the definition and management of the policy at the same time, which thereby provides a more effective solution for building the secure service composition based on security policy.
Internet of Things (IoT) is to connect objects of different application fields, functionality and technology. These objects are entirely addressable and use standard communication protocol. Intelligent agents are used to integrate Internet of Things with heterogeneous low-power embedded resource-constrained networked devices. This paper discusses with the implemented real world scenario of smart autonomous patient management with the assistance of semantic technology in IoT. It uses the Smart Semantic framework using domain ontologies to encapsulate the processed information from sensor networks. This embedded Agent based Semantic Internet of Things in healthcare (ASIOTH) system is having semantic logic and semantic value based Information to make the system as smart and intelligent. This paper aims at explaining in detail the technology drivers behind the IoT and health care with the information on data modeling, data mapping of existing IoT data into different other associated system data, workflow or the process flow behind the technical operations of the remote device coordination, the architecture of network, middleware, databases, application services. The challenges and the associated solution in this field are discussed with the use case.