MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

The cloud market is growing every day. So are cloud crimes. To investigate crimes that happen in a cloud environment, an investigation is carried out adhering to the court of law. Forensics investigations require evidence from the cloud. Evidence acquisition in the cloud requires formidable efforts because of physical inaccessibility and the lack of cloud forensics tools. Time is very crucial in any forensic investigation. If the evidence is preserved before the cloud forensic investigation, it can give the investigators a head start. To identify and preserve such potential evidence in the cloud, we propose a system with an artificial intelligence (AI)-based agent, equipped for binary classification that monitors and profiles the virtual machine (VM) from hypervisor level activities. The proposed system classifies and preserves evidence data generated in the cloud. The evidence repository module of the system uses a novel blockchain model approach to maintain the data provenance. The proposed system works at the hypervisor level, which makes it robust for anti-forensics techniques in the cloud. The proposed system identifies potential evidence reducing the effective storage space requirement of the evidence repository. Data provenance incorporated in the proposed system reduces trust dependencies on the cloud service provider (CSP). © 2022 Prasad Purnaye and Vrushali Kulkarni, published by De Gruyter.

School of Computer Engineering & Technology

Engineering and Technology

Open Computer Science

BiSHM: Evidence detection and preservation model for cloud forensics

The global coronavirus pandemic and lockdown has had negative impacts on individuals’ mental health and well-being. The crisis has generated symptoms of depression in many, which may last even after the lockdown is over. To provide support to individuals in terms of counseling and psychiatric treatment, it is necessary to identify such depressive symptoms in a timely fashion. To address this problem, an artificial intelligence-based system is proposed to assess the changes, if any, in the mental health of an individual as a function of time, starting from the pre-lockdown period (in India from 20 April 2020). A Mental Health Analyzer has been implemented to automatically detect whether an individual is trending toward a state of depression based on his or her tweets over time. The deep learning models of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM have been implemented and compared for the emotion classification task, specifically to detect the emotions of sadness, fear, anger, and joy present in a person’s tweets. The system identifies the emotion of sadness present in tweets to detect depression. An ensemble maximizing model using CNN, LSTM, and Bidirectional LSTM is proposed to maximize the recall metric to improve the performance for the task of depression detection. The implemented system was tested using the dataset provided for the SemEval-2018 semantic evaluation tasks and achieves better results than previous models for the task of emotion classification and, further, can detect depression when tested on real Twitter data. © 2022 J. Adv. Inf. Technol.

World University root node

School of Electronics & Communication Engineering

Journal of Advances in Information Technology

Mental Health Analyzer for Depression Detection Based on Textual Analysis

The use of cloud computing and cloud-based services has increased sharply in the past decade. Regardless of many advantages, the extensive use of the cloud has also created a large attack platform, frequently exploited by cybercriminals, requiring real-time, automated detection and competent forensics tools for investigations. Evidence identification so far has been limited to performance studies on datasets that were created a long time ago and are not specific to cloud environments. In this paper, we introduce a novel dataset for cloud-specific evidence detection. The dataset has two categories: the monitoring database and the evidence database. The monitoring database has 43 features and 9610 records, whereas the evidence database has 360 memory dump files of around 280 GB which contain memory dumps of benign virtual machines and a hostile virtual machine. The dataset will be an important resource for evidence identification research in the cloud environment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Smart Innovation, Systems and Technologies

Information Retrieval for Cloud Forensics

Four-legged robots, also called quadruped or quadruped, can have very sophisticated locomotion patterns and provide means of navigating on surfaces where it seems impossible for wheeled robots. Part of the earth's landmass may be inaccessible by humans due to their geographical locations, environmental hazards, etc. This project aims to develop a reliable solution that enables the implementation of stable and fast static/dynamic walking on even and uneven terrain. The robot captures/mimics four-legged living creatures' mobility, autonomy, and speed. The robot also consists of a remote controller and balance sensor embedded on it for detecting terrain changes&nbsp; &nbsp;

Quadruped robot - Four Legged Robot

QUADRUPED ROBOT

Cloud Computing has many facets to it which makes it diverse. Investigation of a cyber-crime that involves the cloud as a subject, object, or as an environment, is cloud forensics. Challenges in cloud forensics exist at every phase of digital forensics. To provide a holistic solution to overcome these challenges we must first understand the cloud forensics landscape (the Cloud). The elements which give the Cloud the diversity that it has, and have an impact on the forensics process of the cloud should be taken into consideration while designing the cloud forensics framework. In this paper, the researchers present a comprehensive survey of the research trend in cloud forensics. The paper also proposes a cloud forensics taxonomy based on the paradigms of cloud computing that have an impact on cloud forensics. Although forensics solution taxonomies have been proposed earlier, this paper aims at a taxonomy solution at a more generic level. The proposed taxonomy provides cloud forensics solution strategies for efficient cloud forensics. © 2021, CIMNE, Barcelona, Spain.

Archives of Computational Methods in Engineering

A Comprehensive Study of Cloud Forensics

Emotions play a very important role in our lives. The behavioral expressions help to analyze the state of the individual with respect to their minds. This paper aims at providing a Machine Learning Approach in detecting the likelihood of an individual to commit suicide. Suicide is a very serious issue which is increasing rapidly in the society due to various reasons of which most are mental ones. There is a lot of brain drain that is happening due to individuals opting for suicide instead of facing life with societal challenges. Hence, it needs to be tackled and any attempt of suicide must be prevented. The behavioral pattern of an individual is analyzed using text and audio typed/spoken by the individual. Then classify the data based on emotions present in the input and thereby conclude how likely will the individual commit suicide. Appropriate predictions will help create a healthy society.

International Journal of Computer Engineering and Applications

A REVIEW OF THE APPLICATIONS OF MACHINE LEARNING IN GENOMICS

The recent spurt in Android devices has given rise to the development of millions of Android applications. Hence there is a need for rapid and efficient testing techniques to satiate the global app demand. Hence the paper proposes an automated testing framework by deployment testing practices on the cloud. Android devices could be tested by connecting them through wired or wireless connections. This framework can be addressed as Android Testing as a Service (ATaaS) which assists in carrying out functional testing, performance testing etc. The paper also demonstrates the use of a MAT tool to carryout automated testing, the results of which can be compared to conventional testing practices. It also gives us a gist of virtualization. Hence Cloud Testing Platform (CTP) aims at providing all the conventional testing practices at higher speed, availability and lower cost of testing these applications and the limited resources available in mobile devices.. Developers can conduct compatibility tests in various Android devices only by uploading test programs and scripts to cloud testing platforms.

International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 784 – 787

Performance Enhancement of Android Application Testing using Android Devices as a Service Cloud Model

This paper begins with cloud forensics background, describing steps involved in cloud forensics investigation.When it comes to cloud forensics volatile data plays crucial role. Many of Cloud Service Providers (CSP) do nothave proper mechanism for preserving volatile memory data. At most they could do is storing the VirtualMachine (VM) instance. But what if the client (tenant) performs some malicious activity and then ends hissubscription? In such case all of his data will be lost. So we propose a mechanism to store volatile data in adedicated common persistent storage which will provide help during Cloud forensic investigation.

International Journal of Computer Science Engineering (IJCSE) 2, 41-42

Cloud forensics: Volatile data preservation

The concept of multi-tenancy is a single instance of software that runs on a server and serves multiple client organizations. A software application is designed with a multitenant architecture to virtually divide the data and configuration where each client organization can customize its own virtual application instance. This project aims to demonstrate the conversion of single-tenant applications to multi-tenant applications. The project is applicable for every open-source (java) single-tenant application that runs overcloud. As a part of this project, an eclipse plug-in is developed that will assist users to migrate the code and make necessary changes.