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Blog Internet Security The What, Why, and How of AI and Threat Detection


THE WHAT, WHY, AND HOW OF AI AND THREAT DETECTION



Vishnu Varadaraj

Mar 07, 2024

6 MIN READ

There are more online users now than ever before, thanks to the availability of
network-capable devices and online services. The internet population in the US
is the highest it has been, topping the charts at 299 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 688 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 90%
of the Americans own 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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Stay Updated

Follow us to stay updated on all things McAfee and on top of the latest consumer
and mobile security threats.


Vishnu Varadaraj Senior Director, McAfee Consumer Engineering

Vishnu Varadaraj leads product development in the areas of mobile & desktop
security services, identity platform and mobile threat research. Vishnu brings
more than two decades of experience in building...


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