Cybercrime is defined as a crime involving a computer and a network. It includes illegally accessing private information, hacking, phishing, cyberstalking and various other computer-related crimes. Cybercrimes can be against individuals, organizations, or society. Common cybercrimes against individuals are cyberbullying, identity theft, and cyberstalking. Cybercrimes against organizations include hacking, denial of service attacks, and installing viruses. Cybercrimes against society encompass cyber terrorism, cyber espionage, and cyber pornography.
Cyber Crimes Overview with special focus on Cyber crimes in India. Discussion related to some different types of Cyber Crimes. The presentation states the act about the growing concerns of Cyber Crime and also shows Statistical Data.
Cyberspace is a domain characterized by the use of electronics and the electromagnetic spectrum to store, modify, and exchange data via networked systems and associated physical infrastructures.
Ethical hacking and ethical hacker are terms used to describe hacking performed by a company or individual to help identify potential threats on a computer or network.
Introduction of Cybercrime: Types, The Internet spawns crime, Worms versus viruses, Computers' roles in crimes, Introduction to digital forensics, Introduction to Incident - Incident Response Methodology –Steps - Activities in Initial Response, Phase after detection of an incident
Cyber Crimes Overview with special focus on Cyber crimes in India. Discussion related to some different types of Cyber Crimes. The presentation states the act about the growing concerns of Cyber Crime and also shows Statistical Data.
Cyberspace is a domain characterized by the use of electronics and the electromagnetic spectrum to store, modify, and exchange data via networked systems and associated physical infrastructures.
Ethical hacking and ethical hacker are terms used to describe hacking performed by a company or individual to help identify potential threats on a computer or network.
Introduction of Cybercrime: Types, The Internet spawns crime, Worms versus viruses, Computers' roles in crimes, Introduction to digital forensics, Introduction to Incident - Incident Response Methodology –Steps - Activities in Initial Response, Phase after detection of an incident
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. What is Cybercrime?
• Cybercrime is defined as a crime where a computer is the object of
the crime or is used as a tool to commit an offense.
• A cybercriminal may use a device to access a user’s personal
information, confidential business information, government
information, or disable a device.
• It is also a cybercrime to sell or elicit the above information online.
•
3. Categories of Cybercrime
The heads are:
• cyber crimes against individuals,
• cyber crimes against Property, and
• cyber crimes against Government(society at large).
4. • Crimes Against Individual
• These crimes include cyber harassment and stalking, distribution of
child pornography, credit card fraud, human trafficking, spoofing,
identity theft, and online libel or slander.
• 2. Crimes Against Property
• Some online crimes occur against property, such as a computer or
server. These crimes include DDOS attacks, hacking, virus
transmission, phishing attacks,,ramsomeware computer vandalism,
copyright infringement, and IPR violations.
• Crimes Against Government
• When a cybercrime is committed against the government, it is
considered an attack on that nation's sovereignty. Cybercrimes
against the government include hacking, accessing confidential
information, cyber warfare, cyber terrorism, and pirated software.
5. How Criminal Plan the Attacks
• Reconnaissance (information gathering) is the first phase and is
treated as passive attacks.
• Scanning and scrutinizing the gathered information for the validity of
the information as well as to identify the existing vulnerabilities.
• Launching an attack (gaining and maintaining the system access).
6. 1. Reconnaissance
• "Footprinting" - this is the preparation toward preattack phase, and
involves accumulating data about the target's environment and
computer architecture to find ways to intrude into that environment.
• provides a judgment about possible exploitation of those
vulnerabilities
7. 2. Passive Attacks
A passive attack involves gathering information about a target without
his/her (individual's or company's) knowledge.
• Google or Yahoo search: People search to locate information about employees.
• Surfing online community groups like Orkut/Facebook will prove useful to gain the
information about an individual.
• Organization's website may provide a personnel directory or information about key
employees, for example, contact details, E-Mail address, etc. These can be used in a
social engineering attack to reach the target.
• Blogs, newsgroups, press releases, etc. are generally used as the mediums to gain
information about the company or employees.
• Going through the job postings in particular job profiles for technical persons can
provide information about type of technology, that is, servers or infrastructure
devices a company maybe using on its network
8. 3. Active Attacks
• An active attack involves probing the network to discover individual
hosts to confirm the information (IP addresses, operating system type
and version, and services on the network) gathered in the passive
attack, phase.
9. 4. Scanning and Scrutinizing Gathered Information
• Port scanning: Identify open/close ports and services.
• Network scanning: Understand IP Addresses and related information about the computer
network systems.
• Vulnerability scanning: Understand the existing weaknesses in the system.
• The scrutinizing phase is always called "enumeration" in the hacking
world. The objective behind this step is to identify:
• The valid user accounts or groups;
• Network resources and/or shared resources
• OS and different applications that are running on the OS.
10. 5. Attack
The attack is launched using the following steps:
• Crack the password
• Exploit he password
• Execute the malicious command/applications;
• Hide the files (if required);
• Cover the tracks - delete the access logs, so that there is no trail illicit
activity.
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16. Cyber crimes against individuals
Cyberbullying
• Humiliating/embarrassing content posted online about the victim of
online bullying,
• Hacking social media accounts
• Posting vulgar messages on social media
• Threatening the victim to commit any violent activity
• Child pornography or threatening someone with child pornography
17. Cyberstalking
• Browsing anyone’s internet history or online activity, and sending
obscene content online with the help of any social media, software,
application, etc. to know about that particular person is called
cyberstalking.
• In India, in the year 2020, the state of Uttar Pradesh witnessed the
highest number of cyberstalking incidents against women and
children
• Section 67 of the IT Act punishes cyber stalkers who send, cause to
send, or publish obscene posts or content on electronic media with
imprisonment of up to three years and a fine.
18. Cyber defamation
• Cyber defamation means injuring the other person’s reputation via the
internet through social media, Emails etc. There are two types of Cyber
defamation: libel and slander
• Libel: It refers to any defamatory statement which is in written form. For instance,
writing defamatory comments on posts, forwarding defamatory messages on social
media groups, etc. are a part of cyber defamation in the form of libel.
• Slander: It refers to any defamatory statement published in oral form. For instance,
uploading videos defaming someone on YouTube is a part of cyber defamation in the
form of slander.
• Section 67 of the IT Act; whoever publishes or transmits a defamatory
statement about a person shall be punished with 2 years imprisonment
and a fine up to ₹25000.
19. Phishing
• Phishing refers to the impersonation of a legitimate person and
fraudulently stealing someone’s data.
• Phishing refers to the fraudulent practice of sending emails under the
pretext of reputable companies to induce individuals to reveal
personal information, such as passwords, credit card numbers, etc.,
online.
• Section 66C of the IT Act penalises any offender committing phishing-
related activities & is punishable with imprisonment of up to three
years and a fine of up to rupees one lakh.
20. Cyber fraud
• Any person who dishonestly uses the internet to illegal deceive
people and gets personal data, communication, etc. with a motive to
make money is called a cyber fraud.
• Examples of cyber fraud include sending emails containing fake
invoices, sending fake emails from email addresses similar to the
official ones, etc.
• Section 420 of IPC is imprisonment of up to seven years with a fine.
21. Cyber theft
• Cyber theft is a type of cybercrime which involves the unauthorized
access of personal or other information of people by using the
internet.
• c yber theft is to gather confidential data like passwords, images,
phone numbers, etc. and use it as leverage to demand a lumpsum
amount of money.
• Section 66C of the IT Act. The punishment for the same is
imprisonment of up to three years and/or up to Rs 2 lakh fine.
22. Spyware
• Spyware is a type of malware or malicious software, when it is
installed it starts accessing and computing the other person’s device
without the end user’s knowledge. The primary goal of this software
is to steal credit card numbers, passwords, One-Time Passwords
(OTPs), etc.
23. Cyber crimes against organizations
• Attacks by virus
• A computer virus is a kind of malware which connects itself to another
computer program and can replicate and expand when any person attempts.
• For example, the opening of unknown attachments received from malicious
emails may lead to the automatic installation of the virus on the system in
which it is opened.
• These viruses are extremely dangerous, as they can steal or destroy
computer data, crash computer systems, etc.
24. Salami attack
• It is one of the tactics to steal money, which means the hacker steals
the money in small amounts. The damage done is so minor that it is
unnoticed.
• . In Salami slicing, the attacker uses an online database to obtain
customer information, such as bank/credit card details.
25. Web Jacking
• illegal redirection of a user’s browser from a trusted domain’s page to
a fake domain without the user’s consent.
• people visiting any well-known or reliable website can be easily
redirected to bogus websites, which in turn lead to the installation of
malware, leak of personal data, etc.
• Section 383 of IPC is imprisonment of up to three years or with a fine,
or both.
26. Denial of Service Attack
• The attackers generally attack systems in such a manner by trafficking
the targeted system until it ultimately crashes.
• DoS attacks cost millions of dollars to the corporate world,
27. Cyber crimes against society at large
• Cyber pornography
• It states that the following activities are punishable with
imprisonment of up to 3 years and a fine of up to 5 lakhs:
• Uploading pornographic content on any website, social media, etc.
where third parties may access it.
• Transmitting obscene photos to anyone through email, messaging,
social media, etc.
28. Cyber terrorism
• Hacking government-owned systems of the target country and getting
confidential information.
• Destructing and destroying government databases and backups by
incorporating viruses or malware into the systems.
• Disrupting government networks of the target nation.
• Distracting the government authorities and preventing them from
focusing on matters of priority.
29. Cyber Espionage
• espionage is “the practice of spying or using spies to obtain
information about the plans and activities especially of a foreign
government or a competing company.”
• Similarly, cyber espionage refers to the unauthorized accessing of
sensitive data or intellectual property for economic, or political
reasons. It is also called ‘cyber spying’.
• Military data
• Academic research-related data
• Intellectual property
• Politically strategic data, etc.