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Maintenance: Fingerprint scanners require regular maintenance to ensure accurate
readings. Dust, dirt, or scratches on the scanner can affect performance.
Hygiene: In shared environments, concerns about hygiene may arise, as many
people may have to touch the same fingerprint scanner throughout the day.
Technical Issues: Like any electronic system, fingerprint attendance systems can
experience technical glitches, such as software crashes or hardware failures.
Enrollment Process: Enrolling fingerprints for a large number of users can be
time-consuming, especially if there are errors or difficulties with certain individuals'
prints.
Environmental Factors: Extreme temperatures, humidity, or dirty environments
can affect the performance of fingerprint scanners.
User Resistance: Some individuals may be uncomfortable with the idea of
providing their fingerprints for attendance tracking, leading to resistance or reluctance.
References:
1.
K.Jaikumar1 , M.Santhosh Kumar2 , S.Rajkumar3 , A.Sakthivel4 fingerprint
based student attendance system with sms alert to parents
2.
B. Rasagna, Prof. C. Rajendra “SSCM: A Smart Systemfor College
Maintenance” International Journal of Advanced Research in Computer Engineering
& Technology, May 2012.
3.
S. Gong, S.J. McKenna, and A. Psarrou, Dynamic Vision: from Images to
Face Recognition, Imperial College Press and World Scientific Publishing, 2000.
4.
Kai-Fu Lee, Hsiao-Wuen Hon, and Raj Reddy, An Overview of the SPHINX
Speech Recognition System. IEEE Transactions on Acoustics, Speech and Signal
Processing.
ENHANCING NETWORK SECURITY THROUGH AI-DRIVEN
SOLUTIONS
Akhunbayev Adil Alimovich,
Khusanboyev Mukhammadbobir Alisherjon ugli,
Isroilov Ikhtiyorjon Ikromjon ugli
Fergana Polytechnic Institute, Uzbekistan
Annotation
: The rapid evolution of cyber threats demands innovative
approaches to network security. This article delves into the realm of AI-driven network
security, exploring how artificial intelligence is revolutionizing threat detection,
response, and prevention in modern network infrastructures. We discuss the key
techniques, benefits, and challenges associated with AI in network security.
Keywords
: Network Security, Artificial Intelligence, AI-Driven Security,
Threat Detection, Behavioral Analysis, Machine Learning Models, Deep Learning,
Natural Language Processing (NLP), Automated Response, Anomaly Detection,
Cybersecurity, Privacy-Preserving AI, Adversarial Attacks, Scalability, Real-Time
82
Threat Detection, Data Privacy, Case Studies, Future Prospects, Network Intrusion
Detection, Security Automation.
Introduction
Network security is at the forefront of safeguarding sensitive information and
critical infrastructures in today's digital age. Traditional security measures often fall
short in addressing the ever-evolving landscape of cyber threats. Enter Artificial
Intelligence (AI). This article explores how AI is being harnessed to fortify network
security, offering unparalleled capabilities in threat detection and mitigation.
I. The Role of AI in Network Security
Threat Detection: AI algorithms excel at identifying anomalies and patterns that
may indicate malicious activity within a network.
Behavioral Analysis: AI-powered systems monitor user and device behavior to
detect deviations from the norm.
Automated Response: AI enables rapid, automated responses to security
incidents, reducing human intervention time.
II. Techniques in AI-Driven Network Security
Machine Learning Models: Discuss how machine learning algorithms are
employed for intrusion detection, including supervised, unsupervised, and
reinforcement learning techniques.
Natural Language Processing (NLP): Explore NLP's role in analyzing network
communications, especially in email and chat-based security.
Deep Learning: Explain the use of deep neural networks for complex threat
analysis and the advantages of deep learning in identifying sophisticated attacks.
III. Benefits of AI-Driven Network Security
Real-Time Threat Detection: AI enables the instantaneous identification of
potential threats, reducing response time.
Adaptability: Discuss how AI can continuously adapt to evolving threats without
manual reconfiguration.
Reduced False Positives: AI-driven systems tend to generate fewer false alarms,
saving valuable resources.
IV. Challenges and Considerations
Data Privacy: Address concerns related to privacy when analyzing network data.
Scalability: Discuss the challenges of scaling AI-driven security solutions to
large, complex networks.
Adversarial Attacks: Explain the vulnerability of AI models to adversarial
attacks and ongoing research in robust AI security.
V. Case Studies
Present real-world examples of organizations or industries successfully
implementing AI-driven network security solutions.
VI. Future Prospects
Discuss the potential for AI to continue evolving in the realm of network security
and how it might address emerging threats.
Conclusion
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AI-driven network security represents a paradigm shift in defending against
cyber threats. As AI technologies continue to advance, network security will become
more proactive, adaptive, and effective in safeguarding our digital ecosystems.
References:
1. Todor Tagarev, ed., Digital Transformation, Cyber Security and Resilience,
Information & Security: An International Journal, vol. 43 (2019)
2. Todor Tagarev, Krassimir Atanassov, Vyacheslav Kharchenko, and Janusz
Kasprzyk, eds., Digital Transformation, Cyber Security and Resilience of Modern
Societies, in Studies in Big Data, vol. 84 (Cham, Switzerland: Springer, 2021)
3. Velizar Shalamanov, Nikolai Stoianov, and Yantsislav Yanakiev, eds.,
DIGILIENCE 2020: Governance, Human Factors, Cyber Awareness, Information &
Security: An International Journal, vol. 46 (2020)
4. Todor Tagarev, George Sharkov, and Andon Lazarov., eds., DIGILIENCE
2020: Cyber Protection of Critical Infrastructures, Big Data and Artificial Intelligence,
Information & Security: An International Journal, vol. 47 (2020)
5. An extended version of the article Vyacheslav Kharchenko, Ihor Kliushnikov,
Herman Fesenko, and Oleg Illiashenko, “Multi-UAV Mission Planning for Monitoring
Critical Infrastructures Considering Failures and Cyberattacks,” Information &
Security: An International Journal, vol. 49 (2021).
BASED ON MACHINE LEARNING ALGORITHMS TO RECOGNIZE
UZBEK SIGN LANGUAGE (UZSL)
O.A.Kayumov
Jizzakh Branch of National University of Uzbekistan
N.R.Kayumova
Jizzakh Branch of the National University of Uzbekistan
Abstract
: Sign language recognition has gained significant attention due to its
potential to bridge communication gaps between the deaf and hearing communities.
This article presents a comprehensive review of machine learning methods employed
for the recognition of Uzbek Sign Language (UzSL). The unique visual and spatial
nature of sign languages poses challenges that necessitate specialized techniques for
accurate recognition. This review surveys various approaches, ranging from traditional
techniques to modern deep learning methods, used to recognize UzSL gestures. The
article begins by introducing the significance of UzSL recognition and its impact on
facilitating effective communication for the Uzbek deaf community. It outlines the
complexities involved in sign language recognition, including variations in hand
shapes, movements, and facial expressions. The challenges of limited training data,
real-time recognition, and capturing dynamic features are discussed in depth. A survey
of traditional machine learning methods such as Hidden Markov Models (HMMs),
Support Vector Machines (SVMs), and k-Nearest Neighbors (k-NN) is presented,
