Application of Machine Learning and AI in Email Filtering

What is Machine Learning?

Machine Learning (ML) is a branch of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed. ML algorithms learn by analyzing data and identifying patterns. Once an ML algorithm has learned a pattern, it can use that pattern to make predictions or decisions.

In the context of cybersecurity, ML plays a critical role in enhancing threat detection and analysis capabilities. ML algorithms can be trained on large datasets to identify normal and anomalous patterns in network traffic, user behavior, or system activity. This ability to learn adaptively allows ML systems to improve and refine their accuracy over time, providing an effective way to identify new and sophisticated cyber threats.

What is Artificial Intelligence?

Artificial Intelligence (AI) is a broad term that encompasses the development of systems that can simulate human intelligence, including learning, reasoning, and detecting anomalies to timely detect new attacks.

AI is being used to create intelligent systems that can automate decision-making, quickly analyze large datasets, and respond to security incidents in real-time. This helps to increase the ability to proactively and flexibly identify and mitigate potential risks.

The Role of Machine Learning and AI in Information Security

In the field of cybersecurity, machine learning (ML) and artificial intelligence (AI) are pioneering technologies that play a critical role in strengthening digital defense against ever-evolving threats.

The integration of AI and ML in cybersecurity provides a proactive and intelligent defense system, increasing the ability to defend against the ever-changing nature of cyber risks. These technologies allow organizations to go beyond traditional rule-based methods, identify anomalies, predict potential threats, and automate routine security tasks.

One of the important applications of ML is in the field of intrusion detection (IDS/IPS), where machine learning models are used to identify suspicious activities in the network and detect intrusions. This helps organizations to respond quickly to security incidents.

In addition, AI technology also plays an important role in preventing and responding quickly to threats. Intrusion response systems (NIDS) use AI to automatically respond to security incidents, granting or disabling network connections to prevent threats before they cause major consequences.

Furthermore, AI and ML play an important role in risk analysis. Risk prediction and assessment models can automatically identify the level of risk and prioritize risks so that organizations can implement preventive measures effectively.

In terms of network security, AI and ML are used to prevent phishing and cyberattacks. Detecting common phishing techniques through the analysis of email content and user behavior is an important application. At the same time, network behavior monitoring is also performed by AI to detect suspicious and abnormal activities.

Finally, AI and ML also contribute to the implementation of automated security. The system is capable of deploying automated security measures based on data and machine learning, reducing response time and increasing the ability to cope with increasingly complex threats.

In the context of the continued evolution of cybersecurity threats, AI and ML have become essential tools for driving innovation and improving the effectiveness of protecting corporate information and data. The combination of AI and ML brings about significant improvements in the field of cybersecurity, helping organizations maintain a safe and secure environment.

Threats email to businesses

Spam

Spam is becoming a common and dangerous threat to email users, with content such as advertising, fraud, and junk information. According to a report by Statista, in 2023, 58% of the total 347.3 billion emails were spam, posing a major challenge to email communication.

A report by Verizon found that 60% of businesses experience spam attacks, impacting productivity and increasing the risk of data loss. This problem is becoming increasingly serious, requiring the implementation of strict email security measures. Organizations need to filter and block spam, while also raising awareness of the risks posed by spam, to ensure the protection of information and network performance.

Malware attacks

Malware attacks via email are becoming a major threat, causing serious consequences for users and businesses. BSA reported that businesses lose an average of $2.6 million per year due to malware attacks. The number of malware detected by Malwarebytes increased by 20% in 2023, raising concerns about the increasing number and complexity of the threat. Facing this challenge, it is necessary to strengthen security measures and deploy anti-malware technology to prevent attacks before they cause damage.

Email fraud

Email fraud is a dangerous attack that aims to deceive users into revealing sensitive information, often through a fake email from a trusted source. According to the World Bank, businesses lose an average of $3.6 million per year to fraud. The number of detected phishing emails increased by 30% in 2023, according to Phishing Hunter, requiring security measures and user education to prevent increasingly sophisticated fraud schemes.

Business Email Compromise (BEC)

Business Email Compromise (BEC) is a fraudulent strategy that involves impersonating a trustworthy organization to deceive users into taking harmful actions, such as transferring money or providing sensitive information. Businesses suffer an average loss of $2.9 million per year due to BEC attacks, according to the FTC. The number of BEC cases has increased by 40% in 2023, emphasizing the need to update security measures and enhance employee education on new fraudulent tactics.

DDoS attacks and malicious bots

DDoS attacks and the propagation of malicious bots through email are becoming increasingly serious threats to businesses, causing service disruptions, data loss, and even financial damages.

According to Check Point's report, the number of malicious bots has also increased by 20% in 2023, elevating the risks of intrusion, and infection, and enhancing the ability to launch attacks, resulting in severe consequences for businesses. Faced with the complexity of the network environment, deploying robust security measures and continuously updating techniques and attack strategies are crucial to effectively confront these diverse threats.

Authentication attacks

Authentication attacks on email servers are increasingly becoming a significant threat, posing a substantial risk of stealing critical information such as usernames and passwords. According to Verizon's report, 30% of data breaches start with authentication attacks, underscoring the importance of preventing their origin.

Proofpoint reports a 25% increase in the number of authentication attacks on email servers in 2023, highlighting the growing sophistication and complexity of these attack techniques. This raises a high demand for organizations to focus on improving and reinforcing email authentication security measures to effectively safeguard accounts and vital information.

Application of AI Algorithms in Email Filtering In email filtering systems, Artificial Intelligence (AI) algorithms primarily utilize two main types of algorithms: Natural Language Processing (NLP) and Computer Vision (CV).

Natural Language Processing (NLP) Analysis: Using algorithms to analyze natural language in emails to detect threats. For instance, these algorithms can be employed to identify words or phrases in emails that may indicate spam or fraud.

Computer Vision (CV) Artificial Intelligence: Employing algorithms to analyze images in emails to identify threats. For example, these algorithms can be used to detect images of money, credit cards, or other personal information in emails, as these images are often used in phishing emails.

The combination of both types of algorithms enhances the effectiveness of email filtering systems in detecting and preventing threats from spam and various forms of online fraud.

The EG-Platform, an advanced email security solution by VNETWORK, integrates cutting-edge technologies, notably Artificial Intelligence (AI), and Machine Learning (ML), to robustly and effectively safeguard enterprise emails.

One of the leading technologies employed by EG-Platform is Behavioral Analysis, which evaluates user and system behaviors to detect any anomalies that may signify a potential threat. Leveraging comprehensive assessment capabilities, from email opening processes to interactions with content, this technology swiftly identifies and addresses threatening actions.

Semantic Analysis, focuses on scrutinizing email content to detect malicious code, phishing, and other latent threats. This technology aids in identifying hazardous elements within the text, including harmful links and deceptive language, thereby enhancing preventive measures.

Machine Learning technology utilizes machine learning algorithms to continually analyze data and detect new threats. This ensures that the EG-Platform maintains a high level of blocking effectiveness, with the ability to automatically update models, thus ensuring the system consistently faces the diverse and increasingly complex landscape of enterprise email security challenges.

In addition, EG-Platform is equipped with a trio of filters as follows:

SpamGuard: An advanced spam filtering system utilizing Machine Learning and Bayesian technology. This filter is designed to block illegal intermediary servers, minimize bulk spam, and effectively counter Phishing Mail, Viruses, and Ransomware. To assist users, SpamGuard also provides access management features based on criteria set by the business.

Receive Guard: A robust inbound email protection solution that effectively prevents phishing emails, APT attacks, and BEC. This system conducts URL checks and directly analyzes user behavior, with the ability to convert URLs into images for added safety. Using Machine Learning, Receive Guard identifies spoofed domains and checks emails in virtual environments, enhancing threat detection capabilities. Additionally, Receive Guard blocks illegal intermediary servers, checks for spoofed emails, and monitors changes in mail routes.

Send Guard: A powerful outbound email protection solution that helps manage and approve emails before sending to ensure information safety. This system examines content to prevent information leaks and supports the sending and recalling of secure emails. Send Guard is particularly effective in blocking connections from IP-based or country-based systems, as well as blocking connections from Outlook.

The EG-Platform's Trio of Filters

Moreover, this filter provides the capability to establish an approval process before sending, enhancing control over outgoing emails. Through detailed reports via server access logs, Send Guard assists in monitoring and evaluating activities related to the email system, preventing feedback from malicious emails, and controlling email forwarding.

The flexible integration of these unique technologies not only ensures performance and strong defense capabilities but also positions EG-Platform as a top choice for businesses seeking safety and stability in email communication.

VNETWORK's EG-Platform is a comprehensive email security solution, ensuring timely and effective protection for businesses against sophisticated email attacks. Please contact us directly via the hotline (028) 7306 8789 or contact@vnetwork.vn for a detailed consultation.