Malicious URLs and its Forms

Many cyber attacks, including spamming and phishing, involve the use of malicious URLs. Identifying and detecting these malicious URLs can stop such attacks. The severity of an attack is determined by the type of threat, guiding the selection of the most effective countermeasure.

Most existing methods detect malicious URLs only for one type of attack. This paper proposes a machine learning-based method to detect malicious URLs from all popular attack types. We also identify the type of attack that a malicious URL is trying to launch. Our method employs a range of discriminative features, including textual properties and link structures, webpage contents, DNS information, as well as network traffic.

Many of these features have been highly innovative and are extremely effective. Our experiments with 40,000 benign URLs as well as 32,000 malicious URLs, gathered from real-life Internet sources, show that our method has superior performance. The accuracy of the detection of malicious URLs was 98% and 93% respectively. We also discuss the effectiveness of each of these discriminative features and their evitability.

How Does it Effect

An attack chain is usually a series of tricks that trick users. This could include convincing them to open a malicious PDF because it looks like a video or open an infected PDF because it contains financial information. Although the dangers of using social engineering-based techniques such as these can be difficult to manage, their simplistic nature can make them a double-edged sword and a boon to detection systems.

Social engineering attacks often target the lowest common denominator: users lacking tech-savvy or those easily exploitable. These attacks are typically carried out in large numbers and lack sophistication. Most users can recognize the deception if they take the time to think about it. Defenders can detect these tricks because they follow predictable patterns. For instance, a URL containing words like “free” and “cash” might tempt users to click, but such words can trigger a detection system to flag them. What if we trained a machine-learning model to encourage users to think twice? This is how we developed a malicious URL detector to address this question.

Setting Up URLs

The image displays the user entering a URL, which our machine-learning model analyzes in real time to determine its safety. It provides immediate feedback on the URL’s maliciousness. Our model can identify certain red flags, as seen in the gif.

Unencrypted pages (i.e., HTTP pages) are vulnerable to cyber-attacks. The model recognizes a common trick used by attackers: falsely presenting a well-known website like Google by using it as a subdomain, such as “google.securesite.com.” Trained on over 100 million benign and malicious URLs, the model learns to recognize these suspicious patterns.

Although the model’s architecture is complex, it utilizes both character-level embeddings and a convolutional neural net. It operates on a character-by-character basis, independently recognizing meaningful patterns like “g – o – o – g – l – e” and reading URL characters from left to right, akin to human reading. When identifying malicious URLs, specific words are crucial to consider.

Our approach differs as it doesn’t directly input literal characters like “g” and “o” to the model; instead, it utilizes “embeddings.” These numerical representations of letters provide a richer contextual understanding. For instance, the embeddings of “A” and “Z” indicate similarities due to their uppercase status, facilitating easier scoring of URLs.

The embeddings work with convolutional neural networks to encode general relationships among letters, combining them into useful patterns for detecting malicious websites. While aiding this process, embeddings contain enough information about individual characters to allow learning of specific words like “Google” when needed.

Conclusion

The image above shows how the URL model is queried as the user types the URL. However, many malicious URLs hide within static links in emails, Word documents, and webpages. Social engineering attacks exploit URLs to create malicious links from seemingly legitimate text, like “www.google.com.” Attackers manipulate similar-looking characters, such as lowercase “L’s” and uppercase “I’s” or zeroes, to obscure the true destination. Verifying the URL’s authenticity is tough for humans, and even security-aware users can be deceived by benign-looking email links. Detecting link characters is a simple yet effective means to thwart attacks.

In conclusion, integrating robust URL detection mechanisms into cybersecurity protocols can significantly enhance defence against evolving threats. AVIANET, with its cutting-edge technologies and cybersecurity expertise, is ready to assist organizations in fortifying their digital defences against malicious URLs and other cyber threats.