Performance modeling for I/O‐intensive applications on virtual machines

Bhattacharya

2023

Preprint

Self Cite

Memory forensics is used to implement and investigate malware that is executed or stored in RAM. Whether it is static malware analysis or dynamic malware analysis,each time the malware investigator retrieves the result, it is displayed in plaintext, and the investigator begins examining each result in the plaintext and triaging the malicious request. It's a labor-intensive process, and occasionally an investigator will upload malicious files to his or her computer to be analyzed for malware. These malicious files could contain worms or have the potential to infect the investigator's computer; if that happens, the attacker will keep an eye on all future investigations and the evidence they produce. With the help of this research and algorithm, whenever a malicious DLL or request is made, the algorithm will be able to identify it and flag it. This will save the investigator a lot of time because the investigator can upload files to his or her computer without worrying about whether they will be flagged as malicious behavior. We experimented wih multiple malicious files and our algorithm shows 98% efficacy.

Section: Literature Surveymentioning

confidence: 81%

“…Today world has seen an enormous surge in computing tra c [20] [21 [22] [23] [24]. With this hike in computing power,] more and more cyber-attacks are affecting corporate and government networks and sometimes even the IT systems underlying the critical infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Memory Malware Analysis: Detecting Malicious Signatures In Memory By VolatilityPlugin’s

Bhattacharya

2023

Preprint

Self Cite

“…Improving energy efficiency in data centers can not only save costs, but also help mitigate the environmental impact of data centers. [4] [5] [6] This system was created to detect when a customer logo is being copied by someone else on social media or other websites without the customer's permission so that the customer can then take appropriate action.Our algorithm looks for nearly identical logos in shape and design but differs in one or more details (a colour change, a few lines removed from an icon). Some logos, like the Starbucks logo, have many variations that can go unnoticed Identify applicable funding agency here.…”

Section: Introductionmentioning

confidence: 99%

Online Fake Logo Detection System

Tanniru

Bhattacharya

2023

Preprint

With the increasing prevalence of online fraud and the use of fake logos to deceive consumers, there is a need for effective methods to detect and prevent the use of fake logos on the internet. In this paper, we propose a method for detecting fake logos using machine learning techniques. Our approach involves extracting features from the logos and training a classifier to distinguish between real and fake logos. We evaluate the performance of our method on a dataset of real and fake logos and demonstrate its effectiveness in detecting fake logos with high accuracy. Every day, hundreds of domain names, websites and logos are being cloned by cyber criminals who want to gain your trust so they can steal your data. It is becoming a big issue in the online world and needs to be addressed. This article will discuss the initial project background of our new Online Fake Logo Detection System

Design and implementation of an augmented reality mobile application for navigating ATM counters (AR-ATM)

K.G.

Antony

Babu³

et al. 2022

Purpose This paper aims to bring in augmented reality (AR) into navigation systems to rectify the issues mentioned. This paper proposes an AR enhanced navigation system for location automated teller machine (ATM) counters (AR-ATM) and branches of banks based on user’s choice. Upon selecting the ATM, the navigational path to the destination is drawn from the current location, thereby the user can reach the ATM through the optimal path. Design/methodology/approach Traditional navigation systems require users to map with the real world environment as and when required and also may lead to incorrect path due to minor difference in distance. The traditional navigation systems’ also does not take into consideration the ergonomics and safety of the user. Findings In this system, a camera lens is used, which is directed down the street at eye level and the application displays the location of ATMs and bank branches and also provides information about the locations like distance and time through the AR superimposed object. Originality/value The application also provides indoor navigation, especially in a multi-storeyed building. Experiments are performed on smartphones that support AR, and the results are promising with no lag in time frame of the real object and virtual object. To determine the factors that regulate the suggested AR tracking mechanism, a quantitative evaluation of the experimental data is also performed. The testing of implemented AR-ATM from the standpoint of end-users is undertaken to evaluate real-time usage comfortability, and the results have been determined to be extremely satisfactory.