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Revolutionizing ransomware detection and criticality assessment: Multiclass hybrid machine learning and semantic similarity-based end2end solution

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期83卷 39135页

作者： Chaithanya, B. N. Brahmananda, S. H. Gitam Sch Technol Dept Comp Sci & Engn Bangalore India

In the digital environment, a ransomware detection and protection solution is crucial. Because it makes it possible for companies to combat the rising threat of ransomware attacks, prevent financial losses, preserve crucial systems, and satisfy legal requirements. The primary issue is that current ransomware detection and mitigation methods could be more effective due to its dynamic nature and an insufficient up-to-date understanding of its variants. To create more effective defenses and narrow the cybersecurity knowledge gap, interdisciplinary research that examines ransomware's coding, behavior, and goals is required. This paper seeks to improve methods of ransomware defense by analyzing the code, behavior, and goals of numerous ransomware variants. The study proposes using semantic similarity algorithms to estimate the severity of attacks and identify connections between existing attacks to enhance detection and mitigation measures. The objectives of this paper include developing an improved, all-inclusive multiclass ransomware detection and response generation model with automatic or semi-automatic response capabilities. The method combines artificial intelligence with semantic similarity detection techniques to automatically identify and classify ransomware attacks using predefined classifiers. Single machine learning techniques were initially trained on a dataset of ransomware samples;however, the accuracy was poor, ranging from 11% with the LGBM classifier to a maximum of 51% with the decision tree classifier. A hybridization of ML algorithms increases accuracy, leading to a notable improvement in classifier accuracy, with a Hybrid 3 obtaining 91% accuracy. semantic algorithms periodically retrain the model to keep it current with new ransomware variations. These algorithms also provide the severity of the attack. If the criticality is low, the model notifies the administrator for additional analysis;if it is medium, sensitive data-related operations are stop

关键词： Ransomware Malicious files Machine learning Hybridization Data processing Infection identification system Criticality identification semantic algorithms

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A Survey of Machine Learning Assistance in Seismic Interpretation 1

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2nd International Conference on Forthcoming Networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Al Anbagi, Mohammed Kamoona, Zaid Kufa Univ Najaf Iraq Minist Oil Baghdad Iraq

ISBN: (数字)9783031628719

ISBN: (纸本)9783031628702;9783031628719

The exploration and drilling of oil and gas are so expensive and takes a high responsibility for the geoscientists and interpreters to make their decision about the accurate place for drilling and this decision depends on a lot of underground factors. As researchers, we decided to conduct a survey about how to assist the geoscientists and seismic interpreters in making the right decision, with some of the underground characteristics that oil detection depends on. Our survey explains the use of machine learning algorithms to find geological characteristics such as Horizon, Fault, and Stratigraphy. The results show that interpretation by using machine learning algorithms has high accuracy and so close to the real seismic interpretations. Finally, it is noticed that there are a few experiments in this field due to the lack of available data set, although the result is acceptable, it needs time and a lot of experiments to apply on this important part.

关键词： Seismic Horizon Facies Fault Deep Learning semantic algorithms

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Advancing Data Science Using AI-Driven Processes

Advancing Data Science Using AI-Driven Processes

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Jahir Pasha, M. Gaurav, Kumar Bhanja, Amit Kumar Shamout, Mohamed Dawood Mupparaju, Chinna Babu Manohar Bhanushali, Mahesh G.Pullaiah College of Engineering and Technology Computer Science and Engineering Kurnool India Lovely Professional University Phagwara India School of Management Sciences Department of Management Varanasi India College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates University of Central Missouri Masters in Computer Science warrensburg United States Management Studies University of Mumbai VPM's Dr.V.N.Bedekar Institute of Management Studies Thane India

ISBN: (纸本)9798350357769

Advances in AI-pushed facts technological know-how can be seen in an expansion of different regions. Examples of those consist of herbal Language Processing (NLP), machine learning (ML), Deep gaining knowledge of (DL), pc vision (CV), computerized data evaluation, and plenty *** technology is used to technique and extract significant records from textual content-based totally datasets. This era uses semantic algorithms, natural language processing tools, and rule-based totally era to apprehend and interpret textual content information. machine studying (ML) is now being used to automate positive obligations such as predicting monetary trends, figuring out consumer styles, and greater. ML technology can be used to identify relationships among facts points that have been previously no longer seen. Deep gaining knowledge of (DL) is a sub field of the device getting to know that applies layers of neural networks to identify patterns inside facts units. This generation gives an exceptionally powerful manner of analyzing records, allowing machines to stumble on complicated relationships that may have previously been too hard to come *** imaginative and prescient (CV) is another region in which advances in AI-pushed information science are being visible. CV generation lets in for computerized evaluation of snap shots and movies, making it viable to become aware of gadgets, come across tendencies, and discover anomalies. Computerized facts analysis is one of the maximum powerful approaches. © 2023 IEEE.

关键词： Analyzing Records Computerized Data Evaluation semantic algorithms

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Business Process Execution From the Alignment Between Business Processes and Web Services: A semantic and Model-Driven Modernization Process

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IEEE ACCESS 2020年 8卷 93346-93368页

作者： Sosa Sanchez, Encarna Clemente, Pedro J. Conejero, Jose M. Prieto, Alvaro E. Univ Extremadura Dept Comp Sci Quercus Software Engn Grp Caceres 10003 Spain

Many companies have implemented their business processes in Web applications which must be frequently adapted so as to stay aligned with new business process requirements. Service-oriented architectures (SOAs) constitute an appropriate option to manage the continuous changes in those processes by facilitating their alignment with the changing underlying system services. In this context, firms are trying to migrate their Web applications to new software architectures such as SOAs. However, this migration is usually carried out ad-hoc by means of non-reusable and error-prone manual processes. Similarly, the alignment between the business processes and the underlying services identified is usually done by hand. This work presents a model-driven semiautomatic approach to modernize legacy Web applications to SOAs. The approach is focused on an automatic semantic process aimed at discovering the services that can be used to implement the business processes (defined by the companies), then aligning these processes with the underlying services. A semantic algorithm is provided to aid the migration architect during the alignment process. The case study carried out shows that the alignment process results obtained by the semantic algorithm presented in this paper are similar to those obtained by the experts manually. Finally, SOA orchestration artifacts are generated from the semantic algorithm results.

关键词： semantics Task analysis Ontologies Service-oriented architecture Companies LegacyWeb applications service-oriented architecture semantic algorithms model-driven techniques

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Aligning Business Processes With the Services Layer Using a semantic Approach

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IEEE ACCESS 2019年 7卷 2904-2927页

作者： Sosa-Sanchez, Encarna Clemente, Pedro J. Prieto, Alvaro E. Ortiz-Caraballo, Carmen Univ Extremadura Dept Comp Sci Caceres 10003 Spain Univ Lleida Math Dept Campus Igualada Igualada 08700 Spain

Organizations require their business processes goals and the underlying information technology (IT) to be in synchronization with each other, but the continual changes in business processes makes this difficult. To accomplish this synchronization, there needs to be an alignment between the business processes and the IT. Business processes are currently defined using such well-known notations as BPMN, and the IT is made available by different services. Hence, the alignment process can be defined as one between the organization's BPMNs and the services provided by its IT. In practice, however, this process is a complex task which is carried out by hand and hence is error prone. The present communication analyzes the conditions, relations, and incompatibilities between BPMNs and the service descriptions. The incompatibilities are formalized mathematically in order to facilitate their identification and resolution. Then, an alignment process is defined taking into account these incompatibilities and their solutions. The wrapper code needed to resolve each incompatibility identified during the alignment process is generated automatically. Finally, a case study is presented to validate and illustrate the use of the proposed alignment process. The results provided by the semiautomatic alignment process were similar to those obtained manually by a group of experts.

关键词： Alignment process support business process alignment service-oriented architecture semantic algorithms service incompatibility resolution

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Measuring semantic components in training and motivation: A methodological introduction to the semantic theory of survey response

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HUMAN RESOURCE DEVELOPMENT QUARTERLY 2019年第1期30卷 17-38页

作者： Arnulf, Jan K. Dysvik, Anders Larsen, Kai R. BI Norwegian Business Sch Dept Leadership & Org Behav N-0442 Nydalen Norway Univ Colorado Leeds Sch Business Org Leadership & Informat Analyt Boulder CO 80309 USA

This is a methodological presentation of the relationship between semantics and survey statistics in human resource development (HRD) research. This study starts with an introduction to the semantic theory of survey response (STSR) and proceeds by offering a guided approach to conducting such analyses. The reader is presented with two types of semantic algorithms and a brief overview of how they are calculated and how they can be accessed by interested researchers. Subsequently, we use semantic data to reanalyze a previously published study on the relationships between perceptions of a trainee program, intrinsic motivation, and work outcomes. The semantic algorithms can explain between 31 and 55% of the variation in the observed correlations. This article shows how the statistical models originally used to explore the survey data can be replicated using semantics either alone or as an identifiable source of variation in the data. All the steps are presented in detail, and the datasets as well as the statistical syntax necessary to perform the analyses are made available to the readers. Implications for methodology and the improvement of predictive validity in HRD research are discussed.

关键词： motivation semantic algorithms survey data training

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Service discovery using a semantic algorithm in a SOA modernization process from legacy web applications 10

Service discovery using a semantic algorithm in a SOA modern...

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IEEE World Congress on Services (SERVICES)

作者： Sosa, Encarna Clemente, Pedro J. Sanchez-Cabrera, Miguel Conejero, Jose M. Rodriguez-Echeverria, Roberto Sanchez-Figueroa, Fernando Univ Extremadura Quercus Software Engn Grp E-06071 Badajoz Spain

ISBN: (纸本)9781479950690

Service-oriented architectures (SOA) offer a suitable solution to manage the continuous changes in companies business processes. SOA facilitates the alignment of business process with underlying system services. There is a high percentage of Legacy Web Applications (LWA) developed by software factories that were implemented by using Model-View Controller (MVC) frameworks and without considering SOA in their development process. In this context, many companies are performing a modernization of their systems to be adapted to SOA. However, this migration is usually carried out ad-hoc by means of not reusable and error-prone manual processes. Additionally, these migration processes are often performed at a low abstraction level, close to code, hindering reusability and maintainability of the obtained systems. In this paper a Model-Driven systematic and semi-automatic approach to modernize legacy web applications to SOA is presented. The approach eases the reutilization of the process at different domains, since the underlying services of the LWA are identified and generated from model-driven techniques, but also of the new generated system since these new services may be offered as an interoperable service layer. Although the paper completely introduces the approach, it mainly focuses on the semantic process defined to discover services of the LWA that may be later associated (aligned) to the company business processes.

关键词： Reverse engineering web applications service-oriented architecture semantic algorithms service discovery model-driven engineering

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Adaptive semantic Search: Re-Ranking of search results based on Webpage feature extraction and implicitly learned knowledge of User Interests 10

Adaptive Semantic Search: Re-Ranking of search results based...

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10th International Conference on semantics, Knowledge and Grids (SKG)

作者： Venkataraman, Ganesh Ravichandran, Arunkumar Anna Univ Sri Venkateswara Coll Engn Sriperumbudur Tk 602117 Tamil Nadu India

ISBN: (纸本)9781479967155

The quest for information in the contemporary world ends at search engines that crawl millions of web pages on the World Wide Web and it is clearly essential that the results should be ranked in an order that would best fit the user interests. This paper proposes a method of re-ranking the search results that have been primarily ranked using either conventional algorithms that use link structure and user clicks or semantic algorithms, using a combination of general webpage features and user interests. The features of web pages like images, videos etc., are extracted by crawling them and the user's general interest in those features are learnt from past queries made and clicks on particular results. Using the degree to which each feature is present and the corresponding interest of the user, the user's interest in a particular search result is predicted and consequently the results are re-ranked in such a way that it augments the efficiency and effectiveness of conventional intent / meaning driven semantic search concept.

关键词： Internet information retrieval learning (artificial intelligence) Web page feature extraction adaptive semantic search implicit learning link structure semantic algorithms semantic search concept user clicks user interests Feature extraction Search engines semantics Vectors Videos Web pages Web search Search engine ranking re-ranking of search results semantic search webpage features Web pages semantics Search engines Feature extraction Implicit Learning Videotapes Web search information retrieval Internets Interests creeping

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