Understanding Email Attacks and Malicious Code
October 17, 2023
Email: Its Evolution, Mechanics, and Enduring Significance
October 19, 2023Cybersecurity and Artificial Intelligence Technologies
Summary
Artificial Intelligence (AI) plays a pivotal role in the field of cybersecurity, utilizing statistical analysis to identify anomalies in user activities. Machine learning, a subset of AI, identifies behaviors based on historical data, making it particularly valuable in User Behavior Analysis. Deep learning, an advanced facet of AI, takes this a step further by employing neural networks for intricate pattern recognition. In practical applications, AI accelerates threat detection within Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) systems. It also reinforces the capacity to detect email phishing attempts and strengthens modern endpoint security solutions against advanced threats. To safeguard URL endpoints using AI, it’s crucial to implement continuous monitoring and re-verification of URLs to detect and neutralize threats. Additionally, deactivating links can prevent accidental access to malicious sites. More details on these security measures can be found in the specified sections of the ITU-T standard document.
1. Security AI Technologies
1.1. Statistical Analysis
Artificial Intelligence (AI) plays a crucial role in examining user activity patterns within systems or networks. This involves the processing of vast amounts of data, typically employing statistical measures such as averages, standard deviations, and distributions. By comparing real-time data with these statistical indicators, AI can promptly identifyanomalies or irregular patterns.
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
Deep learning, a subset of machine learning, employs multi-layered neural networks to preform complex pattern recognition and classification tasks. Through autonomous learning of various data features and patterns, it can identify behaviors. For instance, a neural network could learn regular traffic data patterns and then classify new data based on these learned patterns.
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
AI processes vast amounts of security event data to detect and counter threats more swiftly. Splunk, for instance, is recognized for its leading AI technology in this domain. While experts universally acknowledge the potential of AI in the realm of SIEM and SOAR, they also emphasize the need for realistic expectations.
2.2. Email Phishing Detection
Companies such as Proofpoint and Mimecast are specialized in email filtering and phishing prevention, managing vast amounts of email data. Some industry leaders emphasize that AI plays a pivotal role in enhancing the capabilities of these products.
2.3. Endpoint Security
Endpoint security firms utilize AI to train their systems by using data from millions of machines. While earlier systems relied on AI for generating pattern-based signatures, contemporary products leverage AI to detect more dynamic and sophisticated attacks.
3. Protecting Against URL Attacks: Establishing Baseline Security
Before Implementing Advanced AI Measures
2.3. Endpoint Security
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)
