How hackers are using AI and ML to target businesses

Cybersecurity benefits from advances in AI and ML. Today’s security teams are inundated with data about potentially suspicious activity, often looking for the needle in the haystack. Artificial intelligence helps security teams uncover real threats in this data through pattern recognition in network traffic, malware indicators, and user behavior trends.

Hackers often use artificial intelligence and machine learning to deal with enterprises. Easy access to cloud environments, for example, makes it simple to start using AI and build powerful, capable learning models.

Let’s look at how hackers are using artificial intelligence and machine learning to target businesses, and ways to prevent AI-focused cyberattacks.

3 Ways Hackers Are Using AI Against Security Teams

1. Test Malware Success on AI-Based Tools

Hackers can use ML in a variety of ways. The first is by building their own machine learning environment and modeling their own malware and attack practices to determine the types of events and behaviors security teams are looking for.

For example, a sophisticated malware might modify local system libraries and components, run processes in memory, and communicate with one or more domains owned by the hacker-controlled infrastructure. All these activities combine to create a profile called Tactics, Techniques and Procedures (TTP). Machine learning models can observe TTPs and use them to build detection capabilities.

By observing and predicting how security teams detect TTPs, hackers can modify indicators and behaviors subtly and frequently, staying ahead of security teams that rely on AI-based tools to detect attacks.

2. Use inaccurate data to destroy AI models

Hackers also use machine learning and artificial intelligence to destroy artificial intelligence with inaccurate data Intelligent models, thereby damaging the environment. Machine learning and artificial intelligence models rely on correctly labeled data samples to build accurate and repeatable detection profiles. By introducing benign files that look similar to malware or creating patterns of behavior that prove to be false positives, hackers can trick an AI model into believing that an attack is not malicious, or by introducing malicious files that the AI ​​was trained to mark as safe. Poisoning AI models.

3. Mapping existing AI models

Hackers actively seek to map existing and developing AI models used by cybersecurity vendors and operations teams AI model. By understanding the capabilities of an AI model and what it does, hackers can actively interfere with machine learning operations and models during their life cycle. This allows a hacker to influence the model by tricking the system in the hacker's favor. It also allows hackers to evade detection based on identified patterns by subtly modifying data to avoid known models entirely.

How to Defend against AI-Focused Attacks

Defending against AI-focused attacks is extremely difficult. Security teams must ensure that the labels associated with the data used in the development of learning models and patterns are accurate. By ensuring that the data has accurate label identifiers, the dataset used to train the model may become smaller, which does not help the efficiency of the AI.

For those building AI security detection models, introducing adversarial techniques and strategies when modeling can help combine pattern recognition with strategies seen in the wild. Researchers at Johns Hopkins University developed the Trojan Software Framework to help generate artificial intelligence models of Trojan and other malware patterns. Researchers at the Massachusetts Institute of Technology (MIT) have released TextFooler, a tool for natural language patterns that may help build more resilient artificial intelligence models to detect problems such as bank fraud.

As artificial intelligence grows in importance, hackers will seek to outpace the efforts of security teams through their own research. It’s crucial for security teams to stay up to date on hackers’ attack strategies in order to defend against them.

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