AI in Cybersecurity: Applications in Various Fields
AI in cybersecurity is a hot topic in the infosec world, as Machine Learning (ML) algorithms become increasingly complex. AI in cybersecurity is being applied to or considered for nearly every field application you can imagine. If a team of humans can do it, then AI can do it — although perhaps with a little human help. It’s a wonderful and exciting time for cybersecurity enthusiasts, and you can stay abreast of all the latest topics on a useful website such as AntivirusRankings.
How Is AI Trained for Cybersecurity?
Intrusion signatures are a kind of digital footprint left by hackers when attempting to access internal systems. Security specialists compile large databases of digital footprints for future reference, to aid in detecting vulnerabilities, and specific patterns by attackers. With a large enough database of signatures and intrusion patterns, AI can be trained to recognize intrusions as they’re occurring.
As an example, one of the most successful methods of attack is by getting into embedded systems — video cameras, printers, and other types of devices connected to the network. The hackers gain entry into these devices by using default login credentials (many companies do not bother to change the administrator password on ‘mundane’ devices). By breaching these devices, the hackers gain access to the rest of the network.
AI cybersecurity is able to scan the entire network for such weaknesses, preventing many of the common kinds of attacks. However, AI is only a tool 3 it still requires human interference, not only to train AI, but step in if AI makes mistakes.
Where Can AI in Cybersecurity Be Applied?
In cybersecurity solutions, AI is either already being applied to, or being heavily considered for, some of the following fields:
- Spam filter applications: Gmail uses AI to detect and block unwanted spam and fraudulent emails. Gmail’s AI was trained by the billions of active Gmail users – whenever you click “Spam” or “Not Spam” on an email, you are actually helping train the AI recognize spam in the future. Thus, the AI has become so developed, it can detect even the sneakiest of spam mails that try to go undetected as “regular” emails.
- Fraud detection: MasterCard implemented Decision Intelligence, an AI-based fraud detection that uses algorithms based on predictable customer behavior. It assesses customer’s typical spending habits, the vendor, location of the purchase, and a variety of other sophisticated algorithms, to assess whether a purchase is out of the ordinary.
- Botnet Detection: An extremely complex field, botnet detection typically relies on recognizing patterns and timings in network requests. Because botnets are typically controlled by a master script of commands, a large-scale botnet attack will typically involve many “users” performing the same, or similar, requests on a website. This could be failed logins (a botnet bruteforce attack), scanning for network vulnerabilities, and other exploits. It’s quite difficult to summarize the extraordinarily complex role AI plays in botnet detection in only a few sentences, but here is an excellent research paper on the topic.
Those are just a few of the fields AI for cybersecurity has been applied. There are already a lot of research papers that show strong evidence in favor of AI’s efficacy regarding cybersecurity. In most research papers, the success rate varies between 85% to 99% in detecting cyber attacks. One AI-development company, DarkTrace, claims a 99% success rate, and already has thousands of global customers.
What If Hackers Use Their Own AI in Cyberattacks?
There is some concern that hackers will have their own AI cyber attacks. One of the first glimpses into what an AI-based cyber attack could look like came from DARPA’s Cyber Grand Challenge, which was an all-machine cyber hacking tournament. Several teams were able to display fully automated cyber attacks, such as generating exploits, patch generation, and launching attacks.
Furthermore, hackers are able to fool learning-based systems. As an example, a team proved they were able to fool self-driving vehicles, by exploiting the vehicle’s road sign detection system. Utilizing things as simple as graffiti and art objects, they were able to force the vehicles to misclassify road signs. That is the basis of how hackers would be able to fool AI cybersecurity — by exploiting classification systems, which the AI is trained on.
Can Blockchain Technology Prevent Log File Tampering?
Finally, an additional concern is that skillful hackers would be able to prevent AI learning. Because AI learning is based on signature databases and intrusion patterns, yet skilled hackers tend to employ methods of scrubbing their tracks — such as by tunneling protocol, and altering log files. If a hacker is able to eliminate their presence from logs, then the security team has nothing with which to train the AI on to prevent a similar attack in the future.
Developing systems that are resistant to log tampering is the task that blockchain technology is working on, AI can only make it more powerful in time. But the basic bricks are based on blockchain. As a decentralized, cryptographically-sealed system log, hackers would not be able to use traditional methods of scrubbing their presence from log files.