How to Prevent Phishing Attacks Using AI and ML Learning?

The advancement of artificial intelligence (AI) technology and its widespread availability have given cyber attackers more power than ever before, enabling them to craft more convincing phishing messages for their targets. There is some good news, though: AI technology also has useful applications. The combination of AI and machine learning is a powerful tactic in the face of the growing threat posed by phishing attacks. This article explores how these technologies provide strong defenses, preventing the emergence of new cyberthreats. Phishing is one of the largest cybersecurity risks that can happen in both personal and professional settings. Phishing emails are the origin of 91% of all cyberattacks, according to a Deloitte report.

Phishing Attack

They attempt to fool the recipient into clicking on a risky link or giving sensitive information, including credit card or other account credentials, in order to install malware on their computer. Cybercriminals spread their fake phishing mails on a variety of venues. The most widely used approach is email.

How artificial intelligence combats phishing assaults using machine learning techniques

With the use of artificial intelligence (AI), machine learning algorithms are essential in the fight against phishing attempts. These algorithms have the skill to recognize minor patterns and abnormalities that evade human detection because they have been meticulously trained on enormous datasets of valid and malicious emails. These algorithms can spot phishing attempts by carefully examining email headers, content, attachments, and sender behavior. They can also spot red flags like strange URLs or mismatched domains. These algorithms improve their accuracy over time by evolving and adapting to new phishing techniques through continuous learning. Incoming emails can be quickly categorized into several risk categories by using them, which enables security teams to order their responses. By automating replies and providing real-time threat mitigation, AI also improves this process.

According to a SlashNext analysis, zero-hour threats will increase in 2022 and make up 54% of detections, with spear phishing accounts for 76% of those. These cutting-edge attacks outwit conventional anti-phishing defenses. The study’s key findings are:

1. Adapting offensive tactics in light of past failures.
2. Launching targeted attacks with automation and machine learning to increase infection possibilities.
3. Making use of three basic strategies: harmful attachments, link-based, and threats in natural language.

How to recognize phishing emails:

It is crucial to conduct thorough training on both regular and suspicious emails in order to use ML algorithms for phishing detection. Algorithms can detect anomalies and harmful patterns thanks to this training.

Social Graph Analysis:

Businesses create social graphs that show how employees regularly communicate with one another. This makes it easier to spot suspicious-looking exchanges when they occur. For instance, conversations between marketing and public relations departments are common, whereas talks between accounting and the firm CEO may be infrequent and require closer examination.

Employee communication profiling:

Every person has their own unique language, tone, and manner of writing emails. Writing patterns include certain opening or concluding clauses, formatting options, and sentence constructions. 

Email Structural Analysis:

ML examines the content of technical emails to find suspicious elements. For example, the IP addresses connected to the email’s path are examined, showing any anomalous or dishonest paths. It may be falsified or somehow modified if an email claims to come from Microsoft Outlook (Microsoft servers), yet its header shows Gmail.