The Future of Email Standardization: The Changes Realized by International Standard
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Email: Its Evolution, Mechanics, and Enduring Significance
11월 15, 2023Cybersecurity and Artificial Intelligence Technologies
Summary
1. Security AI Technologies
1.1. Statistical Analysis
1.2. Machine Learning
Machine learning is a technology that allows the detection of behaviors based on previously learned data. For instance, models are trained using data from login patterns, file access routines, and network traffic trends. Once trained, these models can predict behaviors from new data inputs, thereby identifying any unusual activities. This technology is especially beneficial for User Behavior Analysis (UBA). For example, if an employee from Company A, who typically logs in at 9:00 AM, suddenly logs in at 2:00 AM, it could be flagged as suspicious. Many Chief Information Security Officers (CISOs) consider AI instrumental in addressing internal threats, one of today’s most challenging security issues. AI systems not only send immediate alerts but also provide comprehensive security analysis, guiding where interventions are needed. Some advanced systems even restrict access to specific users to enhance security.
Stuart Laidlaw, the CEO of Cyberlytic, a cybersecurity startup based in the UK, also advocates for the use of machine learning to alleviate the burden on security analytics. He emphasized that, given the sheer volume of data, it’s impractical for security teams to analyze and respond to every single alert. Thus, machine learning can streamline the process, ensuring more efficient and effective analysis.
1.3. Deep Learning
1.4. Summary: Pattern and Signature-Based Detection
Artificial Intelligence (AI) demonstrates exceptional proficiency in pattern and signature-based detection. Although AI may not possess a deep understanding of data flows, it excels at recognizing patterns within vast datasets. Today, AI is capable of sifting through and categorizing enormous amounts of data, showcasing its ability to identifying rare or complex patterns. As cyber threats evolve, databases containing patterns and signatures are consistently updated. Some companies even use AI for statistical inference in network environments, thereby enhancing their threat detection capabilities.
Additionally, AI excels at analyzing historical data to simulate new threat variants and patterns. Nevertheless, it’s crucial to differentiate this capability from the power to predicting the future. While AI can provide insights into potential future attack profiles, it cannot definitively forecast whetherif a specific attack will materialize.
2. Key Areas of AI Application in Cybersecurity
2.1. SIEM/SOAR
2.2. Email Phishing Detection
2.3. Endpoint Security
3. Protecting Against URL Attacks: Establishing Baseline Security Before Implementing Advanced AI Measures
Before implementing advanced AI measures to protect against attacks via URLs, it’s essential to establish the fundamental security requirements. First, “Endpoint URL Monitoring” must be in place, continuously tracking the final destination of all URLs in the body or documents to eliminate potential risk factors, such as web pages that induce users to enter personal information. Second, “URL Post Testing” in real-time re-inspects files and endpoints associated with URLs to prevent time-lag attacks. Finally, “Disabling URLs”, such as converting URLs to images, helps prevent users from accidentally clicking on malicious URLs.
For more detailed information, refer to the ITU-T Standard (Table of Contents: 8.1.3/9.1.3./9.2.4)
