Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
Browse
38 results
Search Results
Item Semantic web service selection based on service provider's business offerings(2009) D’Mello, D.A.; Ananthanarayana, V.S.Semantic Web service discovery finds a match between the service requirement and service advertisements based on the semantic descriptions. The matchmaking mechanism might find semantically similar Web services having same matching score. In this paper, the authors propose the semantic Web service selection mechanism which distinguishes semantically similar Web services based on the Quality of Service (QoS) and Business Offerings (BO). To realize the semantic Web service discovery and selection (ranking), we propose the semantic broker based Web service architecture which recommends the best match for the requester based on the requested functionality, quality and business offerings. The authors design the semantic broker which facilitates the provider to advertise the service by creating OWL-S service profile consisting information related to functionality, quality and business offerings. After the service advertisement, the broker computes and records matchmaking information to improve the performance (service query time) of discovery and selection process. The broker also reads requirements from the requester and finds the best (profitable) Web service by matching and ranking the advertised services based on the functionality, capability, quality and business offering.Item Verification of protocol design using UML - SMV(2009) Prashanth, C.M.; Chandrashekar Shet, K.In recent past, the Unified Modeling Language (UML) has become the de facto industry standard for object-oriented modeling of the software systems. The syntax and semantics rich UML has encouraged industry to develop several supporting tools including those capable of generating deployable product (code) from the UML models. As a consequence, ensuring the correctness of the model/design has become challenging and extremely important task. In this paper, we present an approach for automatic verification of protocol model/design. As a case study, Session Initiation Protocol (SIP) design is verified for the property, "the CALLER will not converse with the CALLEE before the connection is established between them ". The SIP is modeled using UML statechart diagrams and the desired properties are expressed in temporal logic. Our prototype verifier "UML-SMV" is used to carry out the verification. We subjected an erroneous SIP model to the UML-SMV, the verifier could successfully detect the error (in 76.26ms) and generate the error trace.Item Dynamic web service composition based on operation flow semantics(2010) D’Mello, D.A.; Ananthanarayana, V.S.Dynamic Web service composition is a process of building a new value added service using available services to satisfy the requester's complex functional need. In this paper we propose the broker based architecture for dynamic Web service composition. The broker plays a major role in effective discovery of Web services for the individual tasks of the complex need. The broker maintains flow knowledge for the composition, which stores the dependency among the Web service operations and their input, output parameters. For the given complex requirements, the broker first generates the abstract composition plan and discovers the possible candidate Web services to each task of the abstract composition plan. The abstract composition plan is further refined based on the Message Exchange Patterns (MEP), Input/Output parameters, QoS of the candidate Web services to produce refined composition plan involving Web service operations with execution flow. The refined composition plan is then transferred to generic service provider to generate executable composition plan based on the requester's input or output requirements and preferences. The proposed effective Web service discovery and composition mechanism is defined based on the concept of functional semantics and flow semantics of Web service operations. © 2010 Springer-Verlag Berlin Heidelberg.Item A bio-inspired, incremental clustering algorithm for semantics-based web service discovery(Inderscience Enterprises Ltd., 2015) Kamath S?, S.; Ananthanarayana, V.S.Web service discovery is a challenging task due to the widespread availability of published services on the web. In this paper, a service crawler-based web service discovery framework is proposed, that employs information retrieval techniques to effectively retrieve available, published service descriptions. Their functional semantics is extracted for similarity computation and tag generation using natural language processing techniques. The framework is inherently dynamic in nature as new service descriptions may be continually added during periodic crawler runs or existing ones may be removed if service is unavailable. To deal with these issues, a dynamic, incremental clustering approach based on bird flocking behaviour is proposed. Experimental results show that semantic analysis and automatic tagging captured the services' functional semantics in a meaningful way. The algorithm effectively handled the dynamic requirements of the proposed framework by eliminating cluster recomputation overhead and achieved a speed-up factor of 61.8% when compared to hierarchical clustering. © 2015 Inderscience Enterprises Ltd.Item Semantic similarity based context-aware web service discovery using NLP techniques(Rinton Press Inc. sales@rintonpress.com, 2016) Kamath S?, S.S.; Ananthanarayana, V.S.Due to the high availability and also the distributed nature of published web services on the Web, efficient discovery and retrieval of relevant services that meet user requirements can be a challenging task. In this paper, we present a semantics based web service retrieval framework that uses natural language processing techniques to extract a service’s functional information. The extracted information is used to compute the similarity between any given service pair, for generating additional metadata for each service and for classifying the services based on their functional similarity. The framework also adds natural language querying capabilities for supporting exact and approximate matching of relevant services to a given user query. We present experimental results that show that the semantic analysis & automatic tagging effectively captured the inherent functional details of a service and also the similarity between different services. Also, a significant improvement in precision and recall was observed during Web service retrieval when compared to simple keyword matching search, using the natural language querying interface provided by the proposed framework. © Rinton Press.Item Semantics-based Web service classification using morphological analysis and ensemble learning techniques(Springer Science and Business Media Deutschland GmbH, 2016) Kamath S?, S.S.; Ananthanarayana, V.S.With the emergence of the Programmable Web paradigm, the World Wide Web is evolving into a Web of Services, where data and services can be effectively reused across applications. Given the wide diversity and scale of published Web services, the problem of service discovery is a big challenge for service-based application development. This is further compounded by the limited availability of intelligent categorization and service management frameworks. In this paper, an approach that extends service similarity analysis by using morphological analysis and machine learning techniques for capturing the functional semantics of real-world Web services for facilitating effective categorization is presented. To capture the functional diversity of the services, different feature vector selection techniques are used to represent a service in vector space, with the aim of finding the optimal set of features. Using these feature vector models, services are classified as per their domain, using ensemble machine learning methods. Experiments were performed to validate the classification accuracy with respect to the various service feature vector models designed, and the results emphasize the effectiveness of the proposed approach. © 2016, Springer International Publishing Switzerland.Item Leveraging virtual machine introspection with memory forensics to detect and characterize unknown malware using machine learning techniques at hypervisor(Elsevier Ltd, 2017) M.a, M.A.; Jaidhar, C.D.The Virtual Machine Introspection (VMI) has emerged as a fine-grained, out-of-VM security solution that detects malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS). Specifically, it functions by the Virtual Machine Monitor (VMM), or hypervisor. The reconstructed semantic details obtained by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, the existing out-of-VM security solutions require extensive manual analysis. In this paper, we propose an advanced VMM-based, guest-assisted Automated Internal-and-External (A-IntExt) introspection system by leveraging VMI, Memory Forensics Analysis (MFA), and machine learning techniques at the hypervisor. Further, we use the VMI-based technique to introspect digital artifacts of the live guest OS to obtain a semantic view of the processes details. We implemented an Intelligent Cross View Analyzer (ICVA) and implanted it into our proposed A-IntExt system, which examines the data supplied by the VMI to detect hidden, dead, and dubious processes, while also predicting early symptoms of malware execution on the introspected guest OS in a timely manner. Machine learning techniques are used to analyze the executables that are mined and extracted using MFA-based techniques and ascertain the malicious executables. The practicality of the A-IntExt system is evaluated by executing large real-world malware and benign executables onto the live guest OSs. The evaluation results achieved 99.55% accuracy and 0.004 False Positive Rate (FPR) on the 10-fold cross-validation to detect unknown malware on the generated dataset. Additionally, the proposed system was validated against other benchmarked malware datasets and the A-IntExt system outperforms the detection of real-world malware at the VMM with performance exceeding 6.3%. © 2017 Elsevier LtdItem Energy-efficient and reliable data collection in wireless sensor networks(Turkiye Klinikleri Journal of Medical Sciences Talapapa Bulvary no. 102 Hamammonu 1 06230, 2018) Puneeth, D.; Joshi, N.; Atrey, P.K.; Kulkarni, M.Ensuring energy efficiency, data reliability, and security is important in wireless sensor networks (WSNs). A combination of variants from the cryptographic secret sharing technique and the disjoint multipath routing scheme is an effective strategy to address these requirements. Although Shamir's secret sharing (SSS) provides the desired reliability and information-theoretic security, it is not energy efficient. Alternatively, Shamir's ramp secret sharing (SRSS) provides energy efficiency and data reliability, but is only computationally secure. We argue that both these approaches may suffer from a compromised node (CN) attack when a minimum number of nodes is compromised. Hence, we propose a new scheme that is energy efficient, provides data reliability, and is secure against CN attacks. The core idea of our scheme is to combine SRSS and a round-reduced AES cipher, which we call "split hop AES (SHAES)". Both the simulation results and the theoretical analysis are employed to validate the near-sink CN attack, and a secure reliable scheme using SHAES is proposed. © 2018 TÜBITAK.Item Automated multi-level malware detection system based on reconstructed semantic view of executables using machine learning techniques at VMM(Elsevier B.V., 2018) M.a, A.K.; Jaidhar, C.D.In order to fulfill the requirements like stringent timing restraints and demand on resources, Cyber–Physical System (CPS) must deploy on the virtualized environment such as cloud computing. To protect Virtual Machines (VMs) in which CPSs are functioning against malware-based attacks, malware detection and mitigation technique is emerging as a highly crucial concern. The traditional VM-based anti-malware software themselves a potential target for malware-based attack since they are easily subverted by sophisticated malware. Thus, a reliable and robust malware monitoring and detection systems are needed to detect and mitigate rapidly the malware based cyber-attacks in real time particularly for virtualized environment. The Virtual Machine Introspection (VMI) has emerged as a fine-grained out-of-VM security solution to detect malware by introspecting and reconstructing the volatile memory state of the live guest Operating System (OS) by functioning at the Virtual Machine Monitor (VMM) or hypervisor. However, the reconstructed semantic details by the VMI are available in a combination of benign and malicious states at the hypervisor. In order to distinguish between these two states, extensive manual analysis is required by the existing out-of-VM security solutions. To address the foremost issue, in this paper, we propose an advanced VMM-based guest-assisted Automated Multilevel Malware Detection System (AMMDS) that leverages both VMI and Memory Forensic Analysis (MFA) techniques to predict early symptoms of malware execution by detecting stealthy hidden processes on a live guest OS. More specifically, the AMMDS system detects and classifies the actual running malicious executables from the semantically reconstructed process view of the guest OS. The two sub-components of the AMMDS are: Online Malware Detector (OMD) and Offline Malware Classifier (OFMC). The OMD recognizes whether the running processes are benign or malicious using its Local Malware Signature Database (LMSD) and online malware scanner and the OFMC classify unknown malware by adopting machine learning techniques at the hypervisor. The AMMDS has been evaluated by executing large real-world malware and benign executables on to the live guest OSs. The evaluation results achieved 100% of accuracy and zero False Positive Rate (FPR) on the 10-fold cross-validation in classifying unknown malware with maximum performance overhead of 5.8%. © 2017 Elsevier B.V.Item Dense refinement residual network for road extraction from aerial imagery data(Institute of Electrical and Electronics Engineers Inc., 2019) Eerapu, K.K.; Ashwath, B.; Lal, S.; Dell’Acqua, F.; Narasimha Dhan, A.V.Extraction of roads from high-resolution aerial images with a high degree of accuracy is a prerequisite in various applications. In aerial images, road pixels and background pixels are generally in the ratio of ones-to-tens, which implies a class imbalance problem. Existing semantic segmentation architectures generally do well in road-dominated cases but fail in background-dominated scenarios. This paper proposes a dense refinement residual network (DRR Net) for semantic segmentation of aerial imagery data. The proposed semantic segmentation architecture is composed of multiple DRR modules for the extraction of diversified roads alleviating the class imbalance problem. Each module of the proposed architecture utilizes dense convolutions at various scales only in the encoder for feature learning. Residual connections in each module of the proposed architecture provide the guided learning path by propagating the combined features to subsequent DRR modules. Segmentation maps undergo various levels of refinement based on the number of DRR modules utilized in the architecture. To emphasize more on small object instances, the proposed architecture has been trained with a composite loss function. The qualitative and quantitative results are reported by utilizing the Massachusetts roads dataset. The experimental results report that the proposed architecture provides better results as compared to other recent architectures. © 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.