In part because hybrid and remote workplaces are the new normal for most businesses, the sophistication of cyberattacks and the risks they pose have grown rapidly over the past few years . In fact, these new ways of working open up a whole new set of phishing methods for threat actors.
It is estimated that global cybercrime is expected to grow at an annual rate of 15% over the next five years, causing losses of approximately US$10.5 billion by 2025.
The cyberspace is huge. Although hundreds of IT experts analyze threats every day, this is a difficult task. Because humans have limited ability to respond to emerging threats, new, faster and more effective technologies are needed. Artificial intelligence is a potential solution. In this article, I’ll review some common attack methods and how AI solutions can combat cyberattacks.
Cybercriminals have registered thousands of similar domain names, disguising themselves as well-known brands or trustworthy individuals, tricking victims into submitting sensitive certificates or conducting financial transactions. In this case, the hacker registered a domain name similar to the target company. They change URL names and create fake websites and email addresses by adding characters or replacing single letters. For example, "1" means "l" and "0" means "o". They can also use a series of letters, such as "vv" means "w" and "rn" means "m".
Spelling errors are another common tactic to trick the eye. Imagine if someone registered "gooogle.com" instead of "google.com" or "yahooo.com" instead of "yahoo.com".
Protecting businesses against lookalike domain attacks can be difficult, automation, machine learning and artificial intelligence, brand protection solutions have evolved to:
Impersonation refers to cybercriminals using fake display names to impersonate legitimate businesses or individuals. Most email providers allow users to edit their display name, making it easy for hackers to trick victims into believing the email is legitimate. Impersonation spoofing is harder to detect when emails are read on a mobile phone.
Cybercriminals use impersonation to conduct crimes such as account takeovers, whaling, and CEO fraud. A successful name spoofing attack can result in financial losses, reputational damage, and compromised security.
Artificial intelligence solutions can combine predictive threat intelligence, machine learning, and advanced content analytics to detect impersonation attacks. The machine creates a baseline for regular email traffic, and any email that deviates from that baseline is considered unusual and malicious.
URL phishing is a growing threat in which cyber actors create a legitimate-looking website to trick victims into submitting sensitive login credentials. The 2021 Cybersecurity Threat Report stated that about 86% of companies have at least one employee who clicked on a phishing link.
Different methods based on deep learning and machine learning are introduced to protect against URL phishing. One of the ways artificial intelligence can detect URL phishing attacks is to use deep neural networks to discover unusual patterns in URLs. In this way, AI generates alerts that draw attention to suspicious URLs and stop cybercriminals in their tracks.
To combat these cyber threats, artificial intelligence solutions can leverage machine learning and recurrent neural networks. When a pattern of data typical of a phishing website is detected, the interconnected neurons fire together. Collect benign URLs and phishing URLs to create a dataset and identify content-based features. Combined with supervised machine learning, determine the probability that a website is legitimate or malicious.
All businesses are at risk of being attacked by cyber actors. Lookalike, name spoofing and phishing attacks can target any industry, including public administration, healthcare, pharmaceuticals, insurance, research and retail.
When it comes to lookalikes and name spoofing, AI solutions constantly examine domain names and names of organizations that appear to be logged in to uncover hidden patterns that indicate the company may be under spoofing attacks.
Taking phishing URL detection as an example, the algorithm can be trained on millions of phishing samples. Therefore, it detects phishing URLs based on thousands of features extracted from a single URL in a high-dimensional space.
It is difficult for humans to imagine four- or five-dimensional space, because to the human eye, the world is three-dimensional, but artificial intelligence can observe a one-dimensional space and draw conclusions based on it.
Despite these benefits, implementing functional AI solutions with high accuracy remains a challenge for most enterprises. To do this, businesses should consider these best practices.
1.AI models must be trained on real-world data in production. Businesses should start data collection long before developing AI solutions.
2. Enterprises should monitor how the characteristics of the data change over time. Pandemics or climate change may be changes worth tracking.
3. Enterprises should develop and use explainable artificial intelligence technology. Only explainable AI can not only spot phishing attacks but also reason about the origin of decisions.
The field of cyberattacks is getting bigger and bigger, and it’s still growing. Analyzing enterprise threats involves more than just human intervention. Enterprises need emerging technologies to support security teams.
Artificial intelligence is still new in the cybersecurity world, but its ability to learn new things, make informed decisions, and improve models is unparalleled because it can analyze large amounts of information and provide the information security professionals need data to enhance security and protect against cyberattacks.
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