Machine learning and artificial intelligence (AI) are becoming core technologies for some threat detection and response tools. Its ability to learn on the fly and automatically adapt to cyber threat dynamics empowers security teams.
However, some malicious hackers will also use machine learning and AI to expand their network attacks, circumvent security controls, and find new vulnerabilities at an unprecedented speed with devastating consequences. Common ways hackers exploit these two technologies are as follows.
Omida analyst Fernando Montenegro said that epidemic prevention personnel use machine learning technology Detecting spam has been around for decades. “Spam prevention is the most successful initial use case for machine learning.” You can adjust your behavior. They use legitimate tools to make their attacks more successful. "With enough submissions, you can recover what the model is, and then you can tailor your attack to bypass that model."
It's not just spam filters that are vulnerable . Any security vendor that provides a score or some other output is open to abuse. "Not everyone has this problem, but if you're not careful, someone will take advantage of this output."
2. More sophisticated phishing emails
#The use of machine learning is highlighted when advertising these services, and it may not just be marketing rhetoric, but reality. “You’ll know if you try it,” Malone said. “The effect is really good.”
Attackers can use machine learning to creatively customize phishing emails to prevent them from being Mark your email as spam to give targeted users a chance to click through. They customize more than just email text. Attackers will use AI to generate realistic-looking photos, social media profiles and other materials to make communications appear as authentic and trustworthy as possible.
3. More efficient password guessing
Cybercriminals also use machine learning to guess passwords. "We have evidence that they are using password guessing engines more frequently and with higher success rates." Cybercriminals are creating better dictionaries to crack stolen hashes.
They also use machine learning to identify security controls so that they can guess passwords with fewer attempts, increasing the probability of successfully breaching a system.
4. Deepfakes
The most alarming misuse of artificial intelligence is deepfake tools: generating video or audio that can look fake tool. "Being able to imitate other people's voices or looks is very effective in deceiving people." Montenegro said, "If someone pretends to be my voice, you will probably be tricked as well."
In fact, in the past few years, A series of major cases disclosed here show that fake audio can cost companies hundreds, thousands or even millions of dollars. "People will get calls from their bosses — and it's fake," said Murat Kantarcioglu, a professor of computer science at the University of Texas. More commonly, scammers use AI generates photos, user profiles, and phishing emails that look authentic, making their emails look more trustworthy. This is big business. According to an FBI report, business email fraud has resulted in losses of more than $43 billion since 2016. Last fall, media reported that a bank in Hong Kong was defrauded into transferring $35 million to a criminal gang simply because a bank employee received a call from a company director whom he knew. He recognized the director's voice and authorized the transfer without question.
5. Neutralize off-the-shelf security tools
Many security tools commonly used today have some form of artificial intelligence or machine learning built into them. For example, antivirus software relies on more than basic signatures when looking for suspicious behavior. "Anything available on the Internet, especially open source, can be exploited by bad actors."
Attackers can use these tools, not to defend against attacks, but to adjust themselves of malware until detection can be bypassed. "AI models have many blind spots." Kantarcioglu said, "You can adjust it by changing the characteristics of the attack, such as the number of packets sent, the resources of the attack, etc."
Moreover, attackers are leveraging more than just AI-powered security tools. AI is just one of a bunch of different technologies. For example, users can often learn to identify phishing emails by looking for grammatical errors. And AI-powered grammar checkers, like Grammarly, can help attackers improve their writing.
Machine learning can be used for reconnaissance, allowing attackers to view a target’s traffic patterns, defenses, and potential vulnerabilities. Reconnaissance is not an easy task and is beyond the reach of ordinary cybercriminals. "If you want to use AI for reconnaissance, you have to have certain skills. So, I think that only advanced state hackers will use these technologies."
However, once to a certain extent, Once it is commercialized and this technology is provided as a service through the underground black market, many people can take advantage of it. “This could also happen if a hacker nation-state develops a toolkit that uses machine learning and releases it to the criminal community,” Mellen said. “But cybercriminals still need to understand the role and effectiveness of machine learning applications. The method of exploitation, this is the threshold for exploitation."
If the enterprise finds that it is under attack, disconnect the affected system's Internet connection, the malware may not be able to connect back to its command and control (C2) server to receive further instructions. "An attacker may want to develop an intelligent model that can persist for a long time even if it cannot be directly controlled." Kantarcioglu said, "But for ordinary cybercrime, I don't think this is particularly important."
An attacker can trick a machine learning model by feeding it new information. Alexey Rubtsov, senior associate researcher at the Global Risk Institute, said: "Adversaries can manipulate training data sets. For example, they deliberately bias the model so that the machine learns the wrong way."
For example For example, a hacker can manipulate a hijacked user account to log in to the system at 2 a.m. every day to perform harmless work, causing the system to think that there is nothing suspicious about working at 2 a.m., thus reducing the security levels that users must pass.
The Microsoft Tay chatbot was taught to be racist in 2016 for a similar reason. The same approach can be used to train a system to think that a specific type of malware is safe, or that a specific crawler behavior is completely normal.
Legitimate software developers and penetration testers use fuzz testing software to generate random sample inputs in an attempt to crash the application program or find loopholes. Enhanced versions of this type of software use machine learning to generate input in a more targeted and organized manner, such as prioritizing text strings most likely to cause problems. This type of fuzz testing tool can achieve better testing results when used by enterprises, but it is also more deadly in the hands of attackers.
These techniques are among the reasons why cybersecurity approaches such as security patches, anti-phishing education, and micro-segmentation remain vital. "That's one of the reasons why defense in depth is so important," said Forrester's Mellen. "You have to put up multiple roadblocks, not just the one that attackers can use against you."
Investing in machine learning requires a lot of expertise, and machine learning-related expertise is currently scarce Skill. And, because many vulnerabilities remain unpatched, there are many easy ways for attackers to break through corporate defenses.
“There are a lot of low-hanging fruit, and there are a lot of other ways to make money without using machine learning and artificial intelligence to launch attacks.” Mellen said, “In my experience, "In the vast majority of cases, attackers do not utilize these techniques." However, as enterprise defenses improve and cybercriminals and hacker nations continue to invest in attack development, the balance may soon begin to shift.
The above is the detailed content of Nine ways to use machine learning to launch attacks. For more information, please follow other related articles on the PHP Chinese website!
![[Web front-end] Node.js quick start](https://img.php.cn/upload/course/000/000/067/662b5d34ba7c0227.png)
