The artificial intelligence race is here. According to research by the Security Industry Association (SIA), machine learning and cybersecurity are now inextricably linked. When SIA surveyed security industry business leaders about artificial intelligence (AI) and machine learning (ML), it found that:
- 93% expect generative AI services to impact their business strategies in the next 5 years
- 89% report that AI projects are already in their research and development pipelines
As a company at the forefront of machine learning and advanced analytics, Intellias understands the evolving relationship between machine learning and cybersecurity. We stand ready to help cybersecurity professionals leverage advanced AI/ML technology to its fullest. Our customers use artificial intelligence and machine learning to automate threat detection, improve incident response times, and keep ahead of emerging cyber risks.
Companies in all industries are training machine learning to detect cyber attacks, prevent fraud, and create defenses against malicious actors. Read on to learn more about the benefits of ML in cybersecurity. We’ll also show innovative ways you can use machine learning in cybersecurity.
The current state of cybersecurity and why ML
As new threats rapidly escalate, leaders face anxiety-inducing new challenges. Cybercriminals are leveraging AI technologies for sophisticated attacks that make traditional cyber defense obsolete. At the same time, there’s a shortage of experts who can address the challenge. The global cybersecurity workforce gap has grown to nearly 4 million professionals.
If cybersecurity seems like a trivial risk, consider that the cost of cyber attacks is on the rise. Statista estimates that in 2024, the financial damage of cybercrime is $9.22 trillion US. By 2028, the consequences will jump to $13.82 trillion.
Why is the cost of cybercrime rising so quickly? The increasing availability of sophisticated AI opens the doors to the dark side of AI in cybersecurity. Expect to see many disruptions in traditional cybersecurity practices.
Key security challenges highlight the threat of malicious machine learning in cybersecurity:
- Deepfakes and large language models (LLMs) like ChatGPT, Claude, and Gemini make it much easier for bad actors to convincingly impersonate trusted individuals in phishing attacks
- ChatGPT malware is a risk as hackers find loopholes in generative chatbots and use them to create and refine malicious code
- Data leakage from AI the security of machine learning algorithms is often lacking, providing opportunities for other users to access private or proprietary data
With the widespread use of machine learning, cybersecurity is changing rapidly. Individuals and companies will increasingly need to fight fire with fire. The future of cybersecurity will have to include machine learning to detect cyber attacks and proactively monitor against malicious uses of AI.
Businesses are significantly increasing their cybersecurity budgets. We already saw $20.78 billion US go to AI and ML in cybersecurity worldwide in 2023. That number is forecast to grow massively to $102.78 billion by 2032.
Benefits of machine learning for cybersecurity
There’s no denying that the rapid growth of AI/ML technology raises the risk of sophisticated cyber attacks. Fortunately, the same technology creates opportunities for smarter and faster defenses.
Consider these benefits of AI and ML in cyber security:
Rapid data synthesis
Faced with overwhelming amounts of information from multiple sources, cybersecurity analysts need tools that help them synthesize data quickly. Machine learning is the ideal method for rapidly synthesizing large volumes of data. Incorporating machine learning in cybersecurity workflows gives cybersecurity analysts a distinct speed advantage.
Real-time analysis
Identifying threats and vulnerabilities quickly is key to preventing loss and damage. Machine learning isn’t just fast when it comes to processing batches of data. AI algorithms can work lightning-fast, even streaming data in real-time, to analyze data for patterns. For example, we’ve helped customers use machine learning for real-time object detection in self-driving cars. What activity requires real-time analysis more than driving safely on a busy road?
Personalization
When you think about personalization and machine learning, security might not be the first use case that comes to mind. You might be more likely to think of marketing, where AI-driven personalization can target individuals with tailored offers.
A system that can learn about employees’ attributes and behaviors can tailor messages in the same way. Personalization can help ensure that all employees adopt cybersecurity practices into their daily routine. Building personalization with machine learning in cybersecurity could revolutionize how your employees engage with your security system.
“Organizations that haven’t yet embraced GenAI capabilities should evaluate their current external security awareness partner to understand how it is leveraging GenAI as part of its solution roadmap,” says Gartner Analyst Deepti Gopal.
Automation of repetitive tasks
Machine learning’s automation capabilities alleviate security teams from mundane and repetitive tasks, allowing them to focus on more strategic projects. BCG emphasizes that automating specific security tasks with machine learning acts as a force multiplier, enabling teams to scale their response to incoming alerts efficiently.
At Intellias, for example, we built a back-office automation system that relies on Robotic Process Automation (RPA). Automating our routine profit-and-loss paperwork saves the company at least one worker day per week, freeing up time for other tasks. The RPA solution also eliminates errors and facilitates summarizing costs and expenses.
Augmented analyst efficiency
Augmenting analyst insight with real-time intelligence, machine learning enhances the efficiency of security analysts across threat hunting and security operations. By providing up-to-date information and insights, machine learning helps analysts effectively prioritize resources to address cybersecurity threats.
Main types of ML in cybersecurity
There are three main models of machine learning for cybersecurity. It’s impossible to say that one type of ML model is generally better than the others. Each type has its strengths for different use cases. A thoughtful cybersecurity strategy is likely to employ a combination of them.
Supervised learning in cybersecurity
Supervised learning involves training ML models on labeled data. In this model, the algorithm learns to map input data to corresponding output labels.
Applications of supervised learning in cybersecurity:
- Intrusion detection systems (IDS): Supervised learning algorithms can analyze network traffic data to identify patterns indicative of malicious activity, such as DDoS attacks or malware
- Malware detection: By training on labeled datasets of known malware samples, supervised learning models can classify new files as either malicious or benign
Supervised learning models are highly effective for cybersecurity applications. However, obtaining large-scale labeled datasets can be costly and time-consuming.
Unsupervised machine learning in cybersecurity
Unsupervised learning involves training ML models on unlabeled data. In this approach, an algorithm learns to identify patterns in data.
Applications of unsupervised learning in cybersecurity:
- Anomaly detection: Unsupervised learning techniques can detect unusual behavior or deviations from normal patterns in system logs or network traffic, signaling potential security breaches
- Clustering: Unsupervised algorithms can group similar entities, which helps identify clusters of malicious activities or categorize threats
Unsupervised learning is particularly valuable in cybersecurity because it can detect novel and previously unseen threats. That makes it a vital component of proactive defense strategies.
Reinforcement learning in cybersecurity
Reinforcement learning involves training ML models to make decisions by interacting with data using trial and error. This approach coaches the algorithm with rewards and penalties for right and wrong decisions.
Applications of reinforcement learning in cybersecurity:
- Adaptive security systems: Reinforcement learning can make adaptive security systems that dynamically adjust their defense strategies based on evolving threats and attack patterns
- Automated response systems: ML models trained with reinforcement learning can learn the best responses to cyber threats, such as deploying countermeasures or isolating compromised systems
With reinforcement learning, autonomous cybersecurity systems can learn and adapt in real-time. This agility is vital for blocking rapidly evolving threats.
Machine learning use cases in cybersecurity
Use cases for machine learning in cybersecurity that you can build with Intellias:
Malware detection
Machine learning algorithms can be trained to recognize signs of malware. By analyzing characteristics of known malware samples and identifying similarities across datasets, machine learning models can effectively detect and classify malware instances in real time.
Intellias has expertise in developing and deploying machine learning-powered malware detection systems for our clients. With malware detection, malicious software threats can be detected and mitigated.
Anomaly detection
The onslaught of modern online data can overwhelm traditional fraud detection systems. Rules-based engines almost always generate false positives, flagging genuine transactions as fraudulent.
Fortunately, machine learning is great at studying behavior within a system or network and identifying deviations from normal behavior. In cybersecurity, anomaly detection algorithms can learn the typical patterns of user activity, network traffic, or system behavior. They raise alerts when deviations occur.
Intellias offers anomaly detection solutions that leverage machine learning to detect suspicious activities or potential security breaches. These solutions enable organizations to respond swiftly to abnormal events and mitigate risks.
Detecting web shells
Web shells are a type of malicious script that attackers implant on web servers. These scripts exploit weak passwords or misconfigurations, then execute commands to gain unauthorized backdoor access and control. Web shells are a particularly stealthy type of malware. They can lie dormant, hiding in the noise of web traffic, until activated. It’s very difficult for traditional signature-based detection methods to identify them.
For machine learning algorithms that learn patterns of normal server behavior, identifying the presence of a web shell is as straightforward as other anomaly detection. By analyzing network traffic, system logs, and file system changes, machine learning algorithms can pinpoint suspicious behaviors and alert administrators to potential threats.
Intellias specializes in developing machine learning-driven solutions and can provide organizations with robust defenses against this common cybersecurity risk.
Smart surveillance
The power of computer vision and machine learning revolutionize security surveillance. Manual security monitoring is hard to maintain around the clock and comes with high labor costs. Traditional automated systems such as motion detector cameras raise the risk of false positives. A machine learning system with computer vision can differentiate between potential threats and benign activity, improving efficiency and accuracy. Intellias specializes in integrating advanced computer vision technology into cybersecurity systems, enhancing surveillance capabilities with precision and expertise.
Machine learning for cybersecurity with Intellias
As you’ve seen, there are many ways to leverage machine learning in cybersecurity projects, including intelligent threat detection, proactive defense, and enhanced incident response.
At Intellias, we provide cybersecurity consulting services across the Software Development Life Cycle. From security assessments and vulnerability mitigation throughout the solution lifecycle to managing compliance, governance, and third-party risks, we ensure cyber hygiene and implement intelligent cyber defense strategies Join the list of companies who’ve turned to Intellias to . Now is the time to take advantage of the power of machine learning in cybersecurity. set up security awareness programs. Now is the time to take advantage of the power of machine learning in cybersecurity. If you’re unsure where to start with AI and machine learning for cybersecurity purposes, and need guidance, contact Intellias. We’ll help you identify the right approach, develop a strategy, and implement practical solutions to protect your organization from current and future threats.
