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Machine Learning and AI - Effective Email Protection Against Sophisticated Threats

Latest Update: 22/02/2024

Machine Learning and AI - Effective Email Protection Against Sophisticated Threats

Machine Learning and AI are becoming indispensable technologies in safeguarding corporate email. This article will provide information on how Machine Learning and AI technologies prevent and respond to increasingly sophisticated and rampant email attacks in recent times.

Overview of Email Attack Trends in 2023

According to the latest report from Allianz Global Corporate & Specialty (AGCS), 2023 witnessed a significant increase in damages from email attacks, estimated at up to 30 billion USD, a 10% rise compared to 2022, marking the highest figure to date. Concurrently, as reported by Proofpoint in 2023, the number of email attacks surged to approximately 3.1 billion, a 15% increase from 2022. Meanwhile, the success rate of these attacks, according to Verizon's report, reached 23%, a 5% increase from the previous year. These figures raise a serious alarm about the risks businesses are facing from email attacks.

According to the report from VNETWORK Corporation - the provider of the comprehensive email security solution EG-Platform, there is an emphasis on the increasingly complex and unpredictable trends in email attacks, especially for businesses that have not paid sufficient attention to enhancing email security both in receiving and sending directions.

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VNETWORK's Recorded Email Attack Landscape Report (As of June 2023)

In the Vietnamese market, according to VNETWORK's report, as of June 2023, there were over 50,000 emails containing viruses or fraudulent content out of 184,245 emails recorded by the system, equivalent to a rate of 27.13%. Compared to the global average, the email attack rate in Vietnam is 87 times higher, highlighting the particularly severe level of information security threats that businesses in this market are facing, and there are no signs of reduction. This underscores the urgency of implementing email security measures for businesses against increasingly sophisticated and subtle threats.

Common Email Attack Patterns in 2023

Sophisticated Phishing Attacks

Phishing attacks via email are becoming increasingly sophisticated with the constant evolution of tactics by cybercriminals. Previously, attacks often used generic themes and content that were easily recognizable. However, today, more sophisticated phishing techniques have emerged, maximizing personalization for each victim.

A sophisticated strategy involves using personally gathered information from public sources such as social media. This information is then employed to craft deceptive emails impersonating colleagues or superiors, enhancing persuasiveness and making victims more likely to trust.

Furthermore, cybercriminals utilize Artificial Intelligence (AI) to generate phishing emails that closely resemble authentic messages. This tool can mimic the emotions and writing style of a genuine sender while evading conventional email security systems.

Zero-Day Exploit Attacks

Attacks leveraging Zero-day vulnerabilities represent a high level of threat in the field of information security. These are security vulnerabilities that software manufacturers have no prior knowledge of, providing favorable conditions for cybercriminals to execute undetected attacks. These attacks typically employ phishing emails to deploy malicious software, exploiting the trust of users without requiring their interaction.

In the face of this threat, maintaining up-to-date security for both software and operating systems becomes more crucial than ever. Organizations need to regularly assess and update their security systems to address the potential risks arising from undisclosed security vulnerabilities.

Ransomware Attacks

Ransomware attacks are among the most dangerous threats in today's cybersecurity landscape. Attackers often use phishing emails to deploy ransomware on the victim's computer. The ransomware then encrypts the victim's data and demands a ransom payment for decryption.

To prevent such attacks, users need to maintain robust cybersecurity measures, including the use of antivirus software, regular updating of operating systems and applications, and performing regular backups of essential data. Additionally, enhancing awareness and education on preventive measures play a crucial role in safeguarding the cybersecurity of individuals and organizations.

Emotion and Psychology Exploitation Attacks

Attackers exploit the emotions and psychology of victims to obtain sensitive information through phishing emails. They induce anxiety or fear in the victim to coerce them into disclosing crucial personal information.

Attackers carefully choose the subject and content of the email to manipulate the victim's psychology. The message may mimic a security incident or convey urgent requests related to personal accounts. When the victim is anxious or fearful, they are more likely to lose control and be deceived.

To prevent social engineering attacks, users need to be vigilant and aware of phishing techniques. Additionally, training employees on how to recognize and respond to suspicious situations is crucial for enhancing organizational cybersecurity.

Anticipating Email Attack Trends in 2024

As technology undergoes significant advancements, experts predict that 2024 will witness an increase in the complexity and sophistication of email attack threats. Cybercriminals continually enhance their techniques, leveraging new trends and tactics to challenge modern email security measures. In this context, proactively improving detection and prevention capabilities becomes more crucial than ever.

Attacks Utilizing Artificial Intelligence (AI)

Artificial Intelligence (AI) is increasingly becoming a powerful tool in email attacks. According to the latest report from CISA, 2023 witnessed a significant increase in the use of AI in 67% of email attacks. The integration of AI helps create deceptive messages that are challenging to distinguish from legitimate emails, and it is also employed to deceive email security systems by exploiting security vulnerabilities.

Supply Chain Targeted Attacks

Attackers may impersonate official notifications, raise payment issues, or request critical information. Additionally, Business Email Compromise (BEC) attacks, social media manipulation, and mobile device fraud using phishing techniques to steal personal or financial information from mobile users will evolve unpredictably by leveraging stolen information to craft credible emails. Messages may be related to personal events, advertising campaigns, or forged SMS messages. The trend of enhancing targeted individual attacks will utilize information from social media, creating multidimensional scenarios, increasing the difficulty in prevention and response.

Machine Learning and AI Approaches to Preventing and Responding to New Email Attack Patterns

Pattern Detection using Machine Learning and Artificial Intelligence

Utilizing Machine Learning (ML) and Artificial Intelligence (AI) in pattern detection is an effective strategy to combat email fraud. The system is trained based on known fraudulent pattern data, automating the analysis of characteristics and models of fraudulent emails.

When encountering a new email, the system uses the learned model to evaluate and trigger alerts if it detects similarities with known fraudulent patterns. This approach enhances accuracy and performance, enabling the system to adapt quickly to new and sophisticated attack environments. Integrating Machine Learning and AI is an effective solution to address advanced threats in cybersecurity.

Behavioral Detection using Machine Learning and AI

Analyzing behavior through Machine Learning and AI is an advanced method to counter threats arising from user activities. Machine Learning models can identify abnormal changes in behavior, such as examining and analyzing emails from unfamiliar or new addresses. This helps prevent early-stage attack risks, providing quick notifications to administrators and users for preventive measures.

The flexibility and self-learning capabilities of Machine Learning and AI systems are crucial factors in enhancing predictive abilities and prevention in the cybersecurity environment. This approach not only helps prevent threats as soon as they emerge but also enhances the performance of security strategies based on user behavior analysis.

Operational Mechanism of Machine Learning and AI during Attacks

When a new attack occurs, the Machine Learning and AI system follows the process outlined below:

Step 1: The system is trained on a dataset containing known fraudulent email samples, including common subjects, content, and sender email addresses.

Step 2: When a new email appears, the system uses the pre-trained machine learning models to analyze and determine whether the email exhibits characteristics similar to known fraudulent patterns.

Step 3: If the new email is identified with similarities to known fraudulent patterns, the system will take preventive measures and alert the user. Alerts can be displayed as notifications in the web browser or sent through an email to the user's inbox.

For example, if the new email has the subject "Your Account Notification," the Machine Learning and AI system will detect and warn the user not to open that email, effectively preventing phishing attacks.

Elevating Email Security with EG-Platform - A Comprehensive Defense Against Emerging Threats through Integrated Machine Learning and AI Technology

Integrating Machine Learning and AI technology into the EG-Platform brings numerous significant benefits for businesses, such as:

Enhancing detection of new attacks: The Machine Learning and AI technologies within the EG-Platform not only help businesses recognize but also learn and adapt to new network attack patterns. This enhances the ability to cope with the latest threats, ensuring a secure and safe environment.

Automating network attack detection: Machine Learning and AI technology not only serves as a detection tool but also helps automate this process. Automation reduces the burden on human resources, allowing them to focus on more complex tasks in dealing with network threats.

Adapting to changes: Machine Learning and AI technology on the EG-Platform are flexible and can be updated to reflect shifts and changes in network attack strategies. This flexibility helps businesses maintain security against increasingly complex and diverse threats.

Furthermore, the power of the EG-Platform integrates a trio of advanced email attack filters:

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EG-Platform's trio of email filters

Spamguard: Utilizing Machine Learning to identify and block spam emails by analyzing the subject, content, and sender's email address. This model helps users avoid unwanted messages and reduce the burden of spam emails in their inbox.

Receive Guard: Built on Machine Learning technology, Receive Guard focuses on detecting network attacks through emails. This system can recognize signs of phishing, zero-day vulnerabilities, ransomware, thereby safeguarding businesses from potential threats.

Send Guard: Applies Machine Learning to identify emails containing malware or unlawful content. By analyzing the hash of attached files and the email content, Send Guard ensures that any message with the potential to harm the business is blocked and alerts the users.

In summary, integrating Machine Learning and AI into the EG-Platform is a significant step forward in email security, providing flexibility and high efficiency in addressing modern email threats.

The EG-Platform, a product of VNETWORK, is a comprehensive email security solution ensuring timely and effective protection against sophisticated email attacks for businesses. Customers can contact directly through the hotline (028) 7306 8789 or email: contact@vnetwork for detailed consultation.

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