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A review on spreading and Forensics Analysis of Windows-Based ransomware

Annals of Data science 2024年第5期11卷 1503-1524页

作者： Chayal, Narendrakumar Mangilal Saxena, Ankur Khan, Rijwan Department of Computer Science and Technology Faculty of Engineering and Technology Department of Computer Engineering Sankalchand Patel University Visnagar India Department of Computer Science Indus Institute of Information and Communication Technology Dean-ICT & ampDirector-IIICT Indus University Gujarat Ahmedabad India Department of Computer Science and Engineering ABES Institute of Technology Ghaziabad Affiliated to AKTU Lucknow India

Ransomware is one of the most advanced malware which uses high computer resources and services to encrypt system data once it infects a system and causes large financial data losses to the organization and individuals. There are certain automatic ransomware detection and analysis strategies available nowadays. File system analysis reveals some essential patterns and artifacts that can be very useful to understand its behavior spreading mechanism, taxonomy for malware forensics experts. Current trend explores Ransomware as a service (RaaS) and Malware as a service (MaaS) on Darknet. This paper reveals a theory of digital forensic methodology to identify the spreading/infection mechanism and attack path, the cryptographic methodology, windows services, process, APIs, persistence mechanism, and system lockdown strategies and malware analysis methodology. This review could be helpful to learn and understand malware forensic analysis for threat researchers, students, cyber experts, etc. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Malware

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Fake News Detection Using SRTD Algorithm 4th

Fake News Detection Using SRTD Algorithm

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4th International Conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Mahek Tyagi, Sanjay Department of Computer Science and Applications Kurukshetra University Kurukshetra India

ISBN: (纸本)9789819706990

False news detection has become a more important research area as fake news can be harmful to your mental health and makes it harder for people to see the truth. The purpose of this research is to shed light on the issue from the standpoint of foundational linguistic management to provide the groundwork for the detection of misdirection in the media. The fundamental challenge in this field of study is the lack of high-quality data, since it may include both bogus and real reports on a representative sample of the population. In addition to the previously used malleable and dominant truth disclosure structure, a truth acknowledgment approach based on the concept of nearby words has been discussed. The use of almost identical terminology and phrases may help expose controlled fake news. The Jaccard estimate is utilized in the primary computation to determine the rational number of fake news stories with a firm quality score, using the same key phrases. Scores for freedom, mien, and weakness are averaged to arrive at a measure of stability. The SRTD programs are more accurate than other truth identification techniques. The wordnet dictionary can enhance the accuracy when applied with SRTD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data mining

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Emerging Trends and Perspectives on Challenges and Opportunities in Cloud Computing: A Systematic Literature Review 5th

Emerging Trends and Perspectives on Challenges and Opportuni...

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5th International Conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

作者： Tanwar, Jaswinder Tyagi, Sanjay Department of Computer Science and Applications Kurukshetra University Kurukshetra India

ISBN: (纸本)9789819716814

The way organizations run, store and process data and provide services to clients has all been changed by cloud computing. Based on the material that has already been published, this systematic review seeks to identify the main potential and difficulties in cloud computing. The research revealed a number of drawbacks with cloud computing, such as security and privacy concerns, a lack of standards, difficulties with interoperability and performance issues. Security concerns have been identified as the most significant challenge, as cloud computing involves sharing data and applications over the internet, which can increase the risk of cyber-attacks and data compromises. Privacy issues have been also identified as a significant challenge, as when data is kept in cloud storage, people might not have control over it. Lack of standards and interoperability challenges are identified as barriers to cloud adoption, as it can be challenging to integrate cloud services with existing IT infrastructure. Performance issues are also a challenge, as cloud services may not always meet performance expectations, leading to user dissatisfaction. The review also identified several opportunities in cloud computing, including scalability, cost savings, flexibility and innovation. Scalability is a significant opportunity, due to the fact that cloud computing enables enterprises to scale up or down their IT infrastructure and resources based on demand. Cost savings are also a significant opportunity;as cloud services compared to conventional IT infrastructure, it may be more cost-effective. Flexibility and innovation are also significant opportunities, as cloud computing enables companies to swiftly introduce new services and applications, allowing them to innovate and stay competitive. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data privacy

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Liberty: An Economy, Three Fold Collaboration, and Virtualization-aware Resource Management Approach for Multiverse Based Application Execution Over 6G Enhanced Networks

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第11期51卷 1804-1844页

作者： Chowdhury, Mahfuzulhoq Computer Science and Engineering Department Chittagong University of Engineering and Technology Chittagong4349 Bangladesh

Metaverse-based virtual worlds can provide users with an immersive digital experience by utilizing extended reality, IoT, 6G communication, and computing technology. Unlike the multiverse, in which users can access one virtual environment-based activity at a time, metaverse applications allow users to engage in different virtual environment-based activities simultaneously. Previous research did not look at three-fold collaboration, time-criticality, economy, energy, and network function virtualization (NFV)-aware resource management for multiverse, metaverse, and non-metaverse applications concurrently. To conquer these issues, this article proposes a three-fold collaboration (usage of host, neighbor user, and service provider-owned resources), time-criticality, NFV, energy, economy, and service delivery delay-aware resource management scheme for multiverse, metaverse, and non-metaverse application execution over 6G networks, taking into account heterogeneous physical and digital workers, NFV, communication resources, application arrival, and deadline. Experimental results show that the proposed liberty scheme outperforms existing methods by at least 22.67% economic cost, 31.28% energy expenditure cost, 44.11% service provider profit, and 8.83% task completion ratio gain. © (2024), (International Association of Engineers). All rights reserved.

关键词： Network function virtualization

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Emotions: Pyramidal Structured Feature Representation from EEG Signals

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SN computer science 2025年第4期6卷 1-18页

作者： Thejaswini, M.S. Kumar, G. Hemantha Aradhya, V. N. Manjunath Department of Studies in Computer Science University of Mysore Karnataka Mysore 570006 India Department of Computer Applications JSS Science and Technology University Karnataka Mysuru 570006 India

EEG-based emotion recognition offers significant advantages for human-robot interaction, computer gaming, and mental health monitoring. However, current methods face challenges like high-dimensional data, computational complexity, and unrealistic setups. To address these issues, this research proposes a pyramidal structured feature extraction approach utilizing hierarchical forward interpolation and multirate sampling frequencies. A culturally relevant EEG dataset, featuring Kannada musical clips and commercial advertisements, was created for ecological validity. Data were recorded from 46 participants using a BIOPAC 2-channel EEG device, targeting emotional states such as enjoyment, relaxation, sadness, fear, and being scared. The pyramidal approach reduces dimensionality by scaling EEG features from thousands to a compact set while preserving critical information. Iterative forward difference operations are applied across five hierarchical levels, and multirate sampling enhances temporal resolution for better emotion discrimination. The second stage uses a probabilistic neural network, achieving classification rates of up to 98%, outperforming principal and independent component analysis techniques. This framework is computationally efficient, supports real-time applications, and works flexibly with diverse datasets. By integrating culturally relevant stimuli, it sets a new benchmark for EEG-based emotion recognition. Practical applications include emotion-driven product recommendations, entertainment systems, and advancements in human–computer interaction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： EEG Signals Emotional Intelligence Entertainment Numerical Analysis PNN

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Anomalous Network Packet Detection: A Review of Attacks, Datasets and Techniques 2nd

Anomalous Network Packet Detection: A Review of Attacks, Da...

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2nd NIELIT's International Conference on Communication, Electronics and Digital Technologies, NICEDT 2024

作者： Kohli, Mudita Chhabra, Indu Department of Computer Science and Applications Panjab University Chandigarh India

ISBN: (纸本)9789819736003

Anomalous network packet detection is a dire security concern today. ChatGPT era has emerged as a grave concern for computer scientists and programmers. The method of recognizing diverged network activity from the typical behaviour of network flows is known as anomalous network packet detection. As networks have expanded in terms of complexity and size, detecting outlier occurrences in such huge and complex networks has become crucial in retaining integrity. Detecting threats quickly and accurately determines the success of any network flow. To ensure reliable behaviour and capacity to model features, deep learning models have emerged as promising tools in recent years. Developing an efficient anomaly detection system necessitates understanding the imbalanced datasets, recognizing changing patterns and identifying irregularities. The paper outlines attacks, knowledgeable parameters, techniques and datasets for studying crucial network anomalies by reviewing research for different periods of analysis—before 2000, 2000–2010, 2011–2015, 2016–2018 and 2019–2023. The challenges are addressed, and futuristic options for anomaly detection systems are proposed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection

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Physiological and Inertial Features based Dataset for Falls and Activities: PIF v2 2

Physiological and Inertial Features based Dataset for Falls ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kaur, Rajbinder Sharma, Rohini Dhaliwal, Manpreet Kaur Department of Computer Science and Applications Panjab University Chandigarh India

Automatic detection and prediction of falls specifically in elderly population who live alone can prevent untoward incidents by taking action at appropriate time. Validating these automated systems requires a labeled dataset. The available datasets are mostly based on inertial features. A health-related concern may also be a reason behind a fall. Considering physiological features along with inertial features can increase the accuracy of the system. In this paper, a labeled multiclass dataset is presented based on physiological and Inertial features of the body (PIF v2). Two trials are performed by each participant. The participants are young as well as elderly performing activities and falls in real-time using the prototype containing various sensors designed for collecting inertial as well as physiological features. Two trials are performed by each participant. 27 subjects (15 males and 12 females) between the age 10 years to 75 years participated in data collection. A detailed comparison between PIF v1 where the data were collected individually using each sensor and PIF v2 is given along with statistical analysis of PIF v2. The statistical analysis of various features with a t-test shows that the different activities and falls can be differentiated using inertial as well as physiological features. © 2024 Elsevier B.V.. All rights reserved.

关键词： Statistical tests

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An Investigation of Data Requirements for the Detection of Depression from Social Media Posts

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Recent Patents on engineering 2023年第3期17卷 89-101页

作者： Dalal, Sumit Jain, Sarika Dave, Mayank Department of Computer Applications National Institute of Technology Hasyana Kurukshetra136119 India Department of Computer Engineering National Institute of Technology Kurukshetra India

Background: Only a fraction of the produced social media data is usable in mental health assessment. So the problem of sufficient training data for deep learning approaches arises. Data sufficiency can be presented in terms of number of users or the number of posts per user. Objective: We examine the data need of machine learning and deep learning models for a practi-cal system and let researcher choose best fitting models depending on the dataset type available with them. We perform distinct experiments to find the effect of these issues on depression classification by various approaches. Methods: We explored various machine learning and deep learning techniques on various data set versions, taken from Twitter and Reddit, with varying numbers of users and posts per user. Diagnosed and control users are taken in different ratios to assess the impact of an imbalanced dataset. Results: The results reveal that SVM achieved 68% accuracy in depression classification for 70 users each from diagnosed and control group. It decreases for 150 users from each group, but then regains performance for 350 and 550 users from each group. Whereas Naive Bayes got 64% for the same dataset fragment (1). We observed that accuracy decreases for 150 diagnosed users, but then regains performance for 350 and 550 users. However from deep learning algorithms, HAN and BiLSTM perform better, compared to other algorithms, as the imbalance ratio increases. Conclusion: We found, mainly, that classification accuracy increases with the number of users, number of posts per user and imbalance in the number of diagnosed versus control users. We also found that posts from Reddit have better accuracy compared to tweets. © 2023 Bentham science Publishers.

关键词： Deep learning

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An Enhanced Career Prospect Prediction System for Non-computer Stream Students in Software Companies 1st

An Enhanced Career Prospect Prediction System for Non-comput...

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1st International Conference on Computational Intelligence for engineering and Management applications, CIEMA 2022

作者： Abraham, Biku Ambili, P.S. Department of Computer Applications Saintgits College of Engineering Kottayam India School of Computer Science and Applications REVA University Bangalore India

ISBN: (纸本)9789811984921

The stakeholders of the present education system have a key focus on the chances of on-campus employability of their wards. Since software companies play prominent role as campus recruiters, there is a low demand for the traditional engineering branches. The need to enhance skills for software related employment is a challenge to the private sector professional institutions for the existence of non-computer streams in this scenario. In this study, the authors being academic experts in the similar field, attempt to support institutions, students, curriculum designers, and the faculty by providing insight to better employment possibility in campus. Data set for the study is from the same institution for which the placement prediction was completed. The methodology models the problem as a sequential event prediction problem and employs deep learning techniques. The system extracts data from data set with 19 attributes. The results indicate that this predictive approach provides enhancement methods by which a non-computer stream student can ensure full-time employment in software related jobs through in-campus selection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Students

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Flood Risk Assessment and Interpretation Using Explainable AI 2

Flood Risk Assessment and Interpretation Using Explainable A...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Sharma, Swati Tomar, Vivek Arora, Sangeeta Gupta, Nishant KIET Group of Institutions Delhi - NCR Computer Science & Engineering Department Ghaziabad India University Computer Science & Engineering Department Dehradun India KIET Group of Institutions Delhi-NCR Computer Applications Department Ghaziabad India SET Department of Computer Science & Engineering Sharda University Greater Noida India

ISBN: (纸本)9798350368109

Floods are the unforeseen situations to the society which causes high risk to the society. For proactive action, it is important to identify locations based on past results. It is necessary to take measures before the situation gets worst. For the benefit of society, a novel approach to map flood risk usinga balanced dataset and an Extra Tree Classifier. The Extra Tree Classifier performs better with the Shapely Additive explanation values. It shows improvement towards decision making processes to reduce the causality at the time of floods. The proposed method achieves the accuracy of 93.61% for the estimating flood danger effects on a particular location. In this paper quantitative study was done to handle the challenges to predict the flood danger using the artificial intelligence ethically. © 2024 IEEE.

关键词： Risk assessment

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