CC BY
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tools integrate with platforms like AWS, Microsoft Azure, and Google Workspace to monitor and protect data flows.
4. AI and Machine Learning
Emerging DLP solutions leverage AI and machine learning to identify patterns of anomalous behavior, enabling real-time threat detection and response to insider threats or advanced persistent threats (APTs). Challenges in Implementing DLP
1. Balancing Security and Usability
Overly restrictive DLP policies can hinder productivity, while lenient policies may expose the organization to risk. Striking the right balance is crucial.
2. Insider Threats
Employees with legitimate access to sensitive data can inadvertently or intentionally cause data loss. Combating insider threats requires a combination of technical controls and user awareness training.
3. Hybrid Work Environments
The shift to hybrid and remote work models has expanded the attack surface, complicating DLP enforcement across unmanaged devices and home networks.
Data Loss Prevention is a cornerstone of modern cybersecurity, offering a multifaceted approach to safeguarding sensitive information across diverse environments. By leveraging advanced technologies and aligning with industry best practices, organizations can protect themselves from the financial and reputational damages associated with data breaches. As the cybersecurity landscape evolves, embracing innovative trends like AI-powered DLP and zero trust architectures will be critical in staying ahead of emerging threats. Список использованной литературы:
1. Cavoukian, A. (2010). Privacy by Design: The 7 Foundational Principles. Information and Privacy Commissioner of Ontario.
2. Guttman, B., & Roback, E. (1995). An Introduction to Computer Security: The NIST Handbook. National Institute of Standards and Technology.
3. Anderson, R. (2020). Security Engineering: A Guide to Building Dependable Distributed Systems (3rd ed.). Wiley.
© Annamammedov J., Populova E., 2024
УДК 62
Atamyradov B.,
student. Tagangylyjov I.,
teacher.
Oguz han Engineering and Technology university of Turkmenistan.
Ashgabat, Turkmenistan.
A COMPREHENSIVE NEWS AGGREGATOR FOR LOCAL AND GLOBAL NEWS
Annotation
This paper explores the concept, design, and utility of a comprehensive news aggregator capable of seamlessly integrating local and global news. By analyzing the current landscape of digital news dissemination, the study highlights the challenges posed by information silos, regional biases, and fragmented user experiences.
The proposed aggregator leverages advanced machine learning algorithms, natural language processing, and adaptive filtering techniques to curate a balanced and personalized news feed. Emphasis is placed on the role of such platforms in fostering informed citizenship and combating misinformation. The findings underscore the necessity of transparency, inclusivity, and adaptability in the architecture of news aggregation tools to ensure credibility and user engagement.
Keywords:
news aggregator, local news, global news, misinformation, machine learning, natural language processing, digital media.
The proliferation of digital news sources has fundamentally transformed how people access and consume information. While the internet enables rapid dissemination of news, the sheer volume and variety of content present significant challenges for readers aiming to stay informed. Comprehensive news aggregators are emerging as critical tools for organizing, filtering, and presenting news from diverse sources. This paper examines the potential of such aggregators to bridge the gap between local and global news, ensuring a holistic and unbiased perspective for users.
The Importance of Comprehensive News Aggregation
Comprehensive news aggregation involves collecting and organizing news from various sources, ensuring accessibility and relevance for diverse audiences. A balanced aggregator system must account for:
1. Local Relevance: Providing users with news pertinent to their immediate environment.
2. Global Context: Offering insights into global events and their implications.
3. Credibility and Trust: Filtering out misinformation and ensuring the integrity of sources.
These factors highlight the need for a system that is both technologically advanced and user-centric.
Technological Framework
1. Machine Learning and Personalization
Modern news aggregators employ machine learning algorithms to analyze user preferences and behavior. Personalization engines suggest content based on individual interests while avoiding over-personalization, which can create echo chambers.
2. Natural Language Processing (NLP)
NLP techniques enable the system to extract key topics, sentiment, and relevance from a vast array of articles. This ensures that content is appropriately categorized and prioritized.
3. Adaptive Filtering and Moderation
Adaptive filtering algorithms dynamically update news feeds to reflect current trends, ensuring a mix of real-time updates and in-depth analysis. Moderation systems, both automated and manual, are essential for maintaining content quality.
Design Principles for a Balanced Aggregator
1. Transparency: Clear labeling of sources and explanation of curation criteria.
2. Inclusivity: Incorporation of diverse perspectives to counteract bias.
3. Accessibility: A user-friendly interface with customizable features for different demographics.
4. Data Privacy: Ensuring the security of user data and compliance with regulations. Impact on Society
Comprehensive news aggregators can significantly enhance public awareness and civic engagement. By providing a balanced mix of local and global news, they empower users to make informed decisions. Moreover, they play a pivotal role in combating misinformation by prioritizing verified and credible sources. Challenges and Future Directions
Despite their potential, news aggregators face challenges such as algorithmic bias, ethical dilemmas in content prioritization, and evolving user expectations. Future research should focus on integrating advanced AI
capabilities, fostering collaboration with credible news organizations, and developing robust frameworks for ethical decision-making.
Список использованной литературы:
1. McChesney, R. W., & Nichols, J. (2010). The Death and Life of American Journalism: The Media Revolution that Will Begin the World Again.
2. Diakopoulos, N. (2019). Automating the News: How Algorithms are Rewriting the Media.
3. Newman, N., et al. (2022). Reuters Institute Digital News Report 2022.
4. Pariser, E. (2011). The Filter Bubble: What the Internet is Hiding from You.
5. Sunstein, C. R. (2018). #Republic: Divided Democracy in the Age of Social Media.
© Atamyradov B., Tagangylyjov I., 2024
УДК 62
Atanazarov A.,
student.
Gylyjov A.,
teacher.
Oguz han Engineering and Technology university of Turkmenistan.
Ashgabat, Turkmenistan.
IOT-BASED SMART CITY NETWORK Annotation
The concept of smart cities is rapidly becoming a cornerstone of urban development, aiming to address the growing challenges of urbanization, sustainability, and quality of life. The Internet of Things (IoT) plays a pivotal role in this transformation by creating interconnected networks that enable real-time data collection, analysis, and decision-making. This paper explores the architecture, applications, and challenges of IoT-based smart city networks, focusing on areas such as transportation, energy, healthcare, waste management, and public safety. By integrating IoT devices, cloud computing, and artificial intelligence, smart cities can optimize resources, reduce environmental impact, and enhance the well-being of citizens. The paper also discusses security and privacy concerns, scalability issues, and future research directions.
Keywords:
IoT, smart city, urban development, sustainability, connected technologies, urban infrastructure, smart transportation, public safety, IoT security.
Rapid urbanization presents significant challenges, including resource management, traffic congestion, waste disposal, and public safety. Traditional urban infrastructure struggles to keep pace with these demands. Smart cities aim to overcome these challenges by leveraging technology to enhance urban living.
The Internet of Things (IoT) is a fundamental enabler of smart cities. It connects physical devices to the internet, enabling real-time monitoring and control of urban infrastructure. This paper examines the architecture and applications of IoT in smart cities, emphasizing its potential to improve efficiency, sustainability, and quality of life.
IoT Architecture in Smart Cities
1. Sensing Layer
