Cyber threats have become very sophisticated in the digital age we live in. Nowadays, cybersecurity experts are challenged to develop more advanced solutions than ever to protect our systems against an expansive range of threats.
One of these solutions has been antivirus software, which has become the first line of defense in all systems—including highly secure operating systems like Windows and even ChromeOS. According to tech expert Ilija Miljkovac, while ChromeOS is relatively secure, Chromebook devices can be hit by malware. This can result in devastating consequences like identity theft or users being spied on, so installing an antivirus on these devices is crucial (source: https://www.techopedia.com/antivirus/best-antivirus-for-chromebook).
As protections get more stringent, unfortunately, malware has also been evolving too. In some cases, this means traditional antivirus methods that rely on signature detection struggle to make accurate detections. This is why many antivirus software providers have started to incorporate artificial intelligence (AI) into their solutions. Here’s a more detailed look at how AI is being incorporated into antivirus software.
The Evolution of Antivirus Software
Before we get into the details of how AI is used in antivirus solutions, we need to understand exactly why cybersecurity experts started applying it to this technology. To do that, the first thing we need to know is that antivirus software has always been tweaked and changed ever since it was first introduced into the market.
However, most of these changes were still based on a signature-based detection model that would require the software to search for known patterns of malicious code to identify threats. This model used to work well when the types and volumes of malware were relatively limited.
Unfortunately, as more kinds of malware started being available to cyber criminals, this model’s efficacy waned as these bad actors would change their malware’s code and create new versions that could slip through signature-based detection.
To stop this, cybersecurity experts started using a heuristic-based detection model that focused on detecting behaviors that could be harmful instead of specific code patterns. Even though this model worked better, it still had its problems, which is why recently it has started being used in tandem with AI solutions that will help it keep up with the complex malware we have today.
How AI Enhances Antivirus Capabilities
AI has enhanced antivirus software in many ways, which has made these programs more effective as it allows them to analyze and learn from large datasets at high speeds. This ability has been very useful for identifying new threats as they come, as cybercriminals keep developing new strains of malware.
Another enhancement AI has brought is enabling antivirus software to recognize anomalies and patterns as they previously struggled with malware that kept changing to evade detection. With the help of AI algorithms, however, they can now identify the underlying characteristics and behaviors of malware, no matter how much their code has been altered.
These new abilities, such as being able to learn by themselves, have helped antivirus solutions to always stay up to date or one step ahead of cybercriminals. Also, pattern recognition AI-powered antivirus software can detect and mitigate cyber threats more effectively.
However, AI has enabled antivirus programs to analyze behaviors too, which has made these solutions able to identify malware that doesn’t have any known signature. These programs have been able to do this by also looking at the actions of the apps and on a system.
By monitoring these actions, antivirus software can now spot a program that exhibits suspicious behaviors, such as trying to communicate with a command-and-control server or attempting to access sensitive data.
AI in Real-Time Threat Detection
Now that we’ve seen how antivirus programs have evolved into being powered by AI and the benefits this development has brought, let us see how this technology is being used by these programs today.
Firstly, the most frequent way in which antivirus programs use AI is with real time threat detection, which has helped them move away from relying on regular updates to expand their virus definitions so they can remain effective.
This has been even more useful as these updates can have definitions that don’t have the latest data on what new threats have become, thus leaving some systems open to attacks. With AI though, these solutions can keep analyzing data and use it to adapt to new threats.
One of the ways that antiviruses have used AI to adapt is by responding to new threats in real-time as well. For example, when a threat is detected, the program will neutralize or quarantine the malicious code automatically to stop it from causing more harm or from spreading.
This ability to respond immediately has been cherished a lot by large enterprises, as spotting malware on their vast networks was challenging before, which gave cybercriminals enough time to do a lot of damage.
Reducing False Positives and Negatives
AI is also now being used to reduce false positives, which is when legitimate files are flagged as malware, which used to be a common issue with antivirus programs. This issue was very disruptive, especially in business operations, as it often led to unnecessary downtimes. Similarly, false negatives were another common issue now being addressed with AI. Those involved threats going undetected which posed substantial security risks.
Using AI has helped address these issues by improving threat detection accuracy with machine learning models that are trained to spot the difference between benign and malicious behaviors to reduce the likelihood of false alerts and threats slipping through antiviruses.
Since AI models have a continuous learning process, antiviruses can improve themselves over time. They can now do this by analyzing the outcomes of both their correct and incorrect detections and using their results to refine their criteria to improve their accuracy.
Challenges and Limitations of AI in Antivirus Software
Even with the uses and benefits that we’ve mentioned, AI also has its challenges, the main one being its reliance on large datasets for training. What makes this a challenge is that the diversity and quality of these data are crucial in determining their effectiveness, as biased or inadequate data can lead to inaccurate detection results.
Another challenge is that cybercriminals might also start to exploit AI systems themselves so they can feed manipulated data into these systems to deceive them. They can do this by making AI-based antivirus software to misclassify malicious files as safe so they won’t get quarantined or eliminated. However, cybersecurity experts are working hard at fortifying and enhancing AI models so they won’t get manipulated.
AI-powered antiviruses also need a lot of computational power and memory, which can be a challenge for devices with limited resources. However, many of these programs have opted for cloud-based AI solutions to mitigate this issue, even though these solutions can experience outages and do face challenges like data privacy and latency.
Conclusion
Antivirus providers integrating AI into their software has led to great advancements in cybersecurity efforts. That’s because, with cyber threats continuing to become more complex, AI has offered an adaptable and scalable solution that traditional methods could not match.
This is why we can expect to see more reliance on AI-powered systems, especially with developments in deep-learning technologies and machine learning continuing to advance their capabilities. Developments in AI research are also going to potentially play a role in creating more sophisticated threat detection techniques, such as the ability to predict and prevent attacks before they happen.
