In 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw decrease. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

High Frequency Indicator: The dataset contains year- and month-wise compiled data from the year 2021 to till date on the number of different types of grievances (complaints) received from the users of Facebook and Instagram by Meta and the action taken by it. The data compiled is based on the monthly transparency reports published by Meta in accordance with Rule 4(1)(d) of the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (IT Rules, 2021).

The types of grievances received by Meta, for Facebook and Instagram, include content showing sexual content, account hacked, lost of access, harassment, request access to personal data, etc. and the action taken include resolution by tools and special review

Between the second quarter of 2022 and the third quarter of 2024, the number of records exposed to account breaches in Thailand fluctuated significantly. Over ******* datasets were reported as having been leaked in the third quarter of 2024, compared to around ******* during the same quarter of the previous year.

The dataset is already in Kaggle. I used it here just because you can check all the possible Results I find in the Data analysis in my project:

Everyone knows about Netflix. Netflix is mostly used for watching TvShow and movie. Can I ask you some questions regarding Netflix? That's the part of my project.

1: How many movies and TV shows video uploaded by Netflix since 2020? 2: ' Friends' is this a TvShow or movie. ? 3: Brad Anderson is movie director or The director? 4: Netflix started from 2008. But in which year Netflix become popular? 5: How many videos upload by Netflix in 2010 or may be in 2017 or in 2015? 6 : How many TvShow were upload by Netflix in 2010 or in 2013? 7: What are the top3 country which mostly used for shooting movie or TvShow? 8: Netflix mostly upload old movies or New Movies? More question are also there regarding this csv file. If you want to grab all the information of Netflix than just visit this project.

Let me explain you how to get all answer.

In this project 8-9 python file are there. Each PYTHON file is used for different analysis regarding Netflix. Also Plotting the results using Matplotlib. Also apply MachineLearning algorithm. Just check each python file because each python file contains 80+ line of code . That's why I make different file so that you can easily hacked the information of Netflix. 8 PYTHON file Every PYTHON file has it's own best description.

WIFI-HANDSHAKE: Analysis of password patterns in Wi-Fi networks Adrian-Carballal, J. Pablo Galego-Carro, Nereida Rodriguez-Fernandez and Carlos Fernandez-Lozano PeerJ Computer Science

This paper seeks to provide a snapshot of the security of Wi-Fi access points in the metropolitan area of A Coruña. First, we discuss the options for obtaining a tool that allows the collection and storage of auditable information from Wi-Fi networks, from location to signal strength, security protocol or the list of connected clients. Subsequently, an analysis is carried out aimed at identifying password patterns in Wi-Fi networks with WEP, WPA and WPA2 security protocols. For this purpose, a password recovery tool called Hashcat was used to execute dictionary or brute force attacks, among others, with various word collections. The coverage of the access points in which passwords were decrypted is displayed on a heat map that represents various levels of signal quality depending on the signal strength. From the handshakes obtained, and by means of brute force, we will try to crack as many passwords as possible in order to create a targeted and contextualized dictionary both by geographical location and by the nature of the owner of the access point. Finally, we will propose a contextualized grammar that minimizes the size of the dictionary with respect to the most used ones and unifies the decryption capacity of the combination of all of them.

CITATION:

Carballal A, Galego-Carro JP, Rodriguez-Fernandez N, Fernandez-Lozano C. 2022. Wi-Fi Handshake: analysis of password patterns in Wi-Fi networks. PeerJ Computer Science 8:e1185 https://doi.org/10.7717/peerj-cs.1185