There are more online users now than ever before, thanks to the availability of network-capable devices and online services. The internet population in Canada is the highest it has been, topping the charts at 33 million. That number is only expected to increase through the upcoming years. However, this growing number and continued adoption of online services pose increasing cybersecurity risks as cybercriminals take advantage of more online users and exploit vulnerabilities in online infrastructure. This is why we need AI-backed software to provide advanced protection for online users.
The nature of these online threats is ever-changing, making it difficult for legacy threat detection systems to monitor threat behavior and detect new malicious code. Fortunately, threat detection systems such as McAfee’s Antivirus and Threat Detection Defense adapt to incorporate the latest threat intelligence and artificial intelligence (AI) driven behavioral analysis. Here’s how AI impacts cybersecurity to go beyond traditional methods to protect online users.
What is AI?
Most of today’s antivirus and threat detection software leverages behavioral heuristic-based detection based on machine learning models to detect known malicious behavior. Traditional methods rely on data analytics to detect known threat signatures or footprints with incredible accuracy. However, these conventional methods do not account for new malicious code, otherwise known as zero-day malware, for which there is no known information available. AI is mission-critical to cybersecurity since it enables security software and providers to take a more intelligent approach to virus and malware detection. Unlike AI–backed software, traditional methods rely solely on signature-based software and data analytics.
Similar to human-like reasoning, machine learning models follow a three-stage process to gather input, process it, and generate an output in the form of threat leads. Threat detection software can gather information from threat intelligence to understand known malware using these models. It then processes this data, stores it, and uses it to draw inferences and make decisions and predictions. Behavioral heuristic-based detection leverages multiple facets of machine learning, one of which is deep learning.
Deep learning employs neural networks to emulate the function of neurons in the human brain. This architecture uses validation algorithms for crosschecking data and complex mathematical equations, which applies an “if this, then that” approach to reasoning. It looks at what occurred in the past and analyzes current and predictive data to reach a conclusion. As the numerous layers in this framework process more data, the more accurate the prediction becomes.
Many antivirus and detection systems also use ensemble learning. This process takes a layered approach by applying multiple learning models to create one that is more robust and comprehensive. Ensemble learning can boost detection performance with fewer errors for a more accurate conclusion.
Additionally, today’s detection software leverages supervised learning techniques by taking a “learn by example” approach. This process strives to develop an algorithm by understanding the relationship between a given input and the desired output.
Machine learning is only a piece of an effective antivirus and threat detection framework. A proper framework combines new data types with machine learning and cognitive reasoning to develop a highly advanced analytical framework. This framework will allow for advanced threat detection, prevention, and remediation.
How Can AI Help Cybersecurity?
Online threats are increasing at a staggering pace. McAfee Labs observed an average of 588 malware threats per minute. These risks exist and are often exacerbated for several reasons, one of which is the complexity and connectivity of today’s world. Threat detection analysts are unable to detect new malware manually due to their high volume. However, AI can identify and categorize new malware based on malicious behavior before they get a chance to affect online users. AI–enabled software can also detect mutated malware that attempts to avoid detection by legacy antivirus systems.
Today, there are more interconnected devices and online usage ingrained into people’s everyday lives. However, the growing number of digital devices creates a broader attack surface. In other words, hackers will have a higher chance of infiltrating a device and those connected to it.
Additionally, mobile usage is putting online users at significant risk. Over 85% of the Canadian population owns a smartphone. Hackers are noticing the rising number of mobile users and are rapidly taking advantage of the fact to target users with mobile-specific malware.
The increased online connectivity through various devices also means that more information is being stored and processed online. Nowadays, more people are placing their data and privacy in the hands of corporations that have a critical responsibility to safeguard their users’ data. The fact of the matter is that not all companies can guarantee the safeguards required to uphold this promise, ultimately resulting in data and privacy breaches.
In response to these risks and the rising sophistication of the online landscape, security companies combine AI, threat intelligence, and data science to analyze and resolve new and complex cyber threats. AI-backed threat protection identifies and learns about new malware using machine learning models. This enables AI-backed antivirus software to protect online users more efficiently and reliably than ever before.
Top 3 Benefits of AI-backed Threat Detection Software
AI addresses numerous challenges posed by increasing malware complexity and volume, making it critical for online security and privacy protection. Here are the top 3 ways AI enhances cybersecurity to better protect online users.
1. Effective threat detection
The most significant difference between traditional signature-based threat detection methods and advanced AI-backed methods is the capability to detect zero-day malware. Functioning exclusively from either of these two methods will not result in an adequate level of protection. However, combining them results in a greater probability of detecting more threats with higher precision. Each method will ultimately play on the other’s strengths for a maximum level of protection.
2. Enhanced vulnerability management
AI enables threat detection software to think like a hacker. It can help software identify vulnerabilities that cybercriminals would typically exploit and flag them to the user. It also enables threat detection software to better pinpoint weaknesses in user devices before a threat has even occurred, unlike conventional methods. AI-backed security advances past traditional methods to better predict what a hacker would consider a vulnerability.
2. Better security recommendations
AI can help users understand the risks they face daily. An advanced threat detection software backed by AI can provide a more prescriptive solution to identifying risks and how to handle them. A better explanation results in a better understanding of the issue. As a result, users are more aware of how to mitigate the incident or vulnerability in the future.
Take a Smarter Approach to Security
AI and machine learning are only a piece of an effective threat detection framework. A proper threat detection framework combines new data types with the latest machine learning capabilities to develop a highly advanced analytical framework. This framework will allow for better threat cyber threat detection, prevention, and remediation.
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