Exploiting Renewable Energy and UPS Systems to Reduce Power Consumption in Data Centers

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: 88%

“…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

Advances in Transdisciplinary Engineering

“…For PV output, the solar irradiation in the target area is obtained from the Meso numerical forecasting model (MSM) from Japan meteorological agency and used after converting it to slope solar irradiation. The PV output curve is calculated using equation ( 5) and (6).…”

Section: Pv System Modelmentioning

confidence: 99%

“…With the increase in data traffic volume, there is concern about the increase in power consumption of data centers (DCs) and the resulting increase in CO2 emissions [1][2]. As a countermeasure, efforts to utilize renewable energy sources such as photovoltaic systems (PV) to supply power to DCs are being considered [3][4][5][6][7]. However, since PV systems are dependent on the weather and do not generate electricity at night, large storage battery (BAT) capacity is set to required in order to stably supply electricity for DC calculation speed at night.…”

Section: Introductionmentioning

confidence: 99%

CPU Speed Control for Optimal Design of PV and Storage Batteries Toward RE100%DC

Ono,

Shigenobu,

Takahashi

et al. 2024

There is a growing concern about increasing data center (DC) power consumption, and addressing it requires increased use of renewable energy sources (RE). However, a major challenge is that renewable energy output fluctuates. Therefore, proposed CPU speed control to optimally design photovoltaic systems and storage batteries for RE100% DC, and proposed ways to deal with output fluctuations. This paper describes an optimization simulation with upper and lower CPU temperature constraints and verifies the effectiveness of this approach. As a result, be able to confirm an approximately 10% reduction in both CO2 emissions and costs compared to conventional methods.

“…This paper will aim to address this issue by explaining a new technique called HOG based logo detection scheme that offers brand owners the protection they need while providing website operators with assistance in detecting fraudulent or malicious content. [2] II. LITERATURE REVIEW The growing and massive production of visual data by businesses and institutions, as well as the growing popularity of social systems Graphics logos are a type of visual object that is extremely important for determining the identity of something or someone.…”

Section: Evolution Of a Logomentioning

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