CC BY
0
УДК 62
Yazmedov H.,
student.
Scientific supervisor: Garyagdyyeva A.,
teacher.
Oguz han Engineering and Technology university of Turkmenistan.
Ashgabat, Turkmenistan.
SMS SPAM FILTERING Annotation
SMS spam is a growing concern in the digital communication era, posing risks such as phishing, fraud, and user annoyance. This paper explores the problem of SMS spam, examines various filtering techniques, including rule-based, machine learning, and hybrid approaches, and discusses their effectiveness. It also addresses challenges in implementing these systems and outlines future directions for advancing SMS spam filtering technologies.
Key words:
SMS spam, digital communication, problem, filtering techniques, machine learning.
Short Message Service (SMS) has become a ubiquitous form of communication, widely used for both personal and professional purposes. However, its popularity has also made it a target for spammers. Spam messages can lead to significant consequences, such as personal data theft and financial losses, necessitating robust filtering mechanisms. The challenge lies in distinguishing between legitimate and spam messages without compromising user experience or privacy. The Nature of SMS Spam
1. Types of SMS Spam:
o Phishing Messages: Aim to deceive recipients into sharing sensitive information. o Promotional Spam: Unwanted advertisements for products or services. o Malware Links: Contain URLs that download malicious software onto devices.
2. Characteristics of SMS Spam: o Typically short and concise.
o Includes keywords like "free," "win," or "urgent." o Often contains suspicious links or unusual sender details. SMS Spam Filtering Techniques
1. Rule-Based Filtering:
o Keyword Matching: Identifies spam based on predefined keywords.
o Blacklist and Whitelist: Blocks messages from known spam senders while allowing trusted ones. o Limitations: Static rules are less effective against evolving spam tactics.
2. Machine Learning Techniques:
o Naive Bayes Classifier: Uses probabilistic methods to classify messages as spam or legitimate. o Support Vector Machines (SVM): Identifies patterns in text to detect spam.
o Deep Learning Models: Leveraging neural networks like LSTMs and transformers for better context understanding.
o Advantages: Adaptive to new spam patterns with training on updated datasets.
3. Hybrid Approaches:
o Combine rule-based methods with machine learning for enhanced accuracy.
o Example: SpamAssassin, which integrates heuristics and statistical techniques. Challenges in SMS Spam Filtering
1. Dynamic Nature of Spam:
Spammers constantly evolve their tactics, requiring adaptive filtering mechanisms.
2. Privacy Concerns:
Filtering systems must ensure user privacy, avoiding excessive data collection or intrusive analysis.
3. Resource Constraints:
Real-time filtering on mobile devices demands efficient algorithms with minimal computational overhead.
4. Language and Regional Variations:
SMS spam often uses multiple languages and local slang, complicating detection efforts. Evaluation Metrics
Effectiveness of SMS spam filtering systems is typically evaluated using metrics such as:
• Precision: Proportion of correctly identified spam messages.
• Recall: Ability to identify all spam messages.
• F1 Score: Harmonic mean of precision and recall, offering a balanced evaluation. Case Studies
1. Spam Filtering in Commercial Services:
Google Messages and Apple's SMS filters use AI to automatically detect spam.
2. Open Source Solutions:
Projects like SpamAssassin demonstrate the utility of hybrid approaches for spam filtering. Effective SMS spam filtering is crucial for maintaining trust and efficiency in digital communication. While traditional techniques offer a foundational approach, advanced methods such as machine learning and hybrid systems provide the adaptability needed to counter evolving spam tactics. Future innovations, including federated learning and blockchain, promise to enhance both accuracy and user privacy, paving the way for more robust spam mitigation strategies.
Список использованной литературы:
1. Sebastiani, F. (2002). Machine Learning in Automated Text Categorization. ACM Computing Surveys.
2. Almeida, T. A., Hidalgo, J. M. G., & Yamakami, A. (2011). SMS Spam Collection. Proceedings of the ACM Symposium on Applied Computing.
3. Sumeetha, T., & Younus, S. S. (2016). Efficient SMS Spam Detection Using Machine Learning Algorithms. IEEE Transactions.
© Yazmedov H., 2024
УДК 62
Yoldashova A., student. Esenova E., teacher.
Oguz han Engineering and Technology university of Turkmenistan.
Ashgabat, Turkmenistan.
DESIGN AND IMPLEMENTATION OF A SUPERMARKET MANAGEMENT SYSTEM (GULZEMIN)
Annotation
This paper explores the design and implementation of a supermarket management system tailored for
