Capping carbon emission from green data centers

Bhattacharya, Tathagata; Rahgouy, Mostafa; Peng, Xiaopu; Takreeti, Taha; Cao, Ting; Mao, Jianzhou; Das, Amit Kumar; Qin, Xiao; Sinha, Apurba

doi:10.1007/s40095-022-00539-9

Cited by 6 publications

(5 citation statements)

References 59 publications

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“…A rootkit was successfully detected using the Volatility Framework on memory images retrieved by AMExtractor [19]. We also provide a remedy to this entire situation and provide a carbon footprint model in this paper that optimizes the total carbon emission from this green data center [20]. The experimental results unveil that our modeling approach offers a practical way of conserving the energy consumption of virtual machines running in data centers [21].…”

Section: Literature Surveymentioning

confidence: 93%

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

Reddy

Bhattacharya

Reddy

2023

Preprint

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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.

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Section: Literature Surveymentioning

confidence: 93%

“…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

Reddy

Bhattacharya

Reddy

2023

Preprint

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“…[13] However, both logos and lexis derive from the same verb légō (), meaning "(I) count, tell, say, speak". [1][13] [14] The Computer Vision Laboratory from New York University has created a tool that lets you scan any logo and get the findings of whether it is real or not. It's basically some AI software that allows you to compare the image of a random file to your company logo.…”

Section: A Origin Of Logo'smentioning

confidence: 99%

“…LogoMotive is operational in the .nl registry, and it is generalizable to detect any other logo in any DNS zone to help identify abuse. [1] B. Fake Logo Examples Fake logos disguised as advertisements are unsolicited emails that claim to be advertisements (i.e., they pretend to be from a particular company), but they contain hidden phishing links that direct you to a website where the user's personal information could then be accessed without their knowledge or permission.…”

Section: A Scammers' Use Of Trademarks To Defraud Consumersmentioning

confidence: 99%

“…From Banking sector to Academic institutions, from defense to corporate sector, people are extremely dependent on IT power which comes with massive energy consumption. [1][2] [3] Energy consumption of the Information and Communication Technology (ICT) sector has grown exponentially in recent years.We require a sizable database of archived photos in order to verify and distinguish between authentic and fake logos in order to establish if a logo is legitimate or not. However, this demands a lot of room in data centers, and maintaining such data centers consumes a lot of energy..To determine if a logo is phony or real, each picture must be recorded in a database, necessitating the use of several servers and data centers.…”

Section: Introductionmentioning

confidence: 99%

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Online Fake Logo Detection System

Tanniru

Bhattacharya

2023

Preprint

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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

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Data Centers Sustainability: Approaches to Green Data Centers

Fatima

Ehsan

2023

2023 International Conference on Communication Technologies (ComTech)

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Capping carbon emission from green data centers

Cited by 6 publications

References 59 publications

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

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

Online Fake Logo Detection System

Data Centers Sustainability: Approaches to Green Data Centers

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