1. Introduction

Why IoT Security Needs an Upgrade

The Internet of Things (IoT) is everywhere—from smart homes and wearable devices to industrial automation and healthcare systems. As convenient as these connected devices are, they come with a serious downside: they are vulnerable to cyberattacks. An AI-driven model enhances security by intelligently adapting to new threats, providing real-time protection against cyber risks. Unlike regular computers, IoT devices have limited processing power, meaning they can’t run strong security software the way a laptop or a smartphone would (Alsulami, 2024).

Hackers know this. As IoT networks grow, cyber threats are evolving too, becoming more aggressive and harder to detect. Standard security techniques, like firewalls and antivirus software, aren’t built for IoT’s unique challenges—which leaves many devices exposed to attacks (Htwe et al., 2020; Al Razib et al., 2022).

Why Traditional Security Methods Fall Short

Most cybersecurity systems rely on signature-based detection, meaning they recognize threats based on known attack patterns. This works fine for existing threats, but fails when a new attack emerges. Hackers can modify malware, creating new variations that bypass traditional security checks (Alsulami, 2024; Mishra et al., 2022).

IoT security needs a real-time, adaptive approach—something smarter than a simple database of threats. That is where AI-driven cybersecurity models come in, using machine learning to detect attacks before they happen (Le et al., 2022).

Introducing AI for Cybersecurity

To tackle IoT’s security challenges, researchers developed Artificial Fish Swarm-driven Weight-normalized Adaboost (AF-WAdaBoost)—an AI-powered threat detection model that automatically adjusts its detection strategies to keep up with evolving cyber threats (Alsulami, 2024).

AF-WAdaBoost is designed to:

• Improve accuracy in detecting attacks.
• Identify new threats without relying on preset signatures.
• Enhance cybersecurity in IoT environments, making them more resilient to hacking attempts.

2. AI-Driven Model: The Rise of AI in Cybersecurity

How AI is Changing Cybersecurity

For years, cybersecurity relied on manual threat analysis, meaning security teams had to analyze network traffic and identify threats themselves. But with the explosion of connected devices, this is no longer possible. There are simply too many threats for humans to track alone (Sarker et al., 2021).

AI is changing the game. Instead of reacting to attacks, AI-driven models predict them by analyzing patterns in network behavior. Using machine learning, AI can detect anomalies, flag suspicious activity, and respond to threats automatically (Alsulami, 2024).

AI-Driven Model: Machine Learning and Ensemble Learning for Cybersecurity

Machine learning models help security systems learn from data rather than just relying on pre-programmed rules. One of the most powerful techniques is ensemble learning, which combines multiple models to improve accuracy (Alsulami, 2024).

AF-WAdaBoost uses two advanced techniques:

• Artificial Fish Swarm Optimization (AF) – Inspired by how fish hunt in groups, this method allows AI to detect threats dynamically, adjusting its detection strategy based on real-time data.
• Weight-normalized Adaboost (WAdaBoost) – This technique boosts the accuracy of weak classifiers, allowing AI to focus on high-risk attack patterns.

AI-Driven Cybersecurity Model: AF-WAdaBoost in Action

Researchers tested AF-WAdaBoost against three major IoT threat datasets—CICIDS2017, NSL-KDD, and UNSW-NB15—showing impressive accuracy improvements (Alsulami, 2024).

Dataset	Accuracy
CICIDS2017	98.5%
NSL-KDD	97.3%
UNSW-NB15	99.9%

AF-WAdaBoost outperformed traditional models, proving it can better detect cyber threats in IoT environments.

Why AI-Driven Security is Essential for IoT

Cyberattacks are getting more sophisticated—and that means security measures need to keep evolving. AI-driven models like AF-WAdaBoost give security systems the ability to adapt, identifying new types of attacks without needing constant updates (Alsulami, 2024).

With AI, cybersecurity becomes more efficient, scalable, and resilient, helping businesses protect their networks without manual intervention.

3. AI-Driven Model for Cyber Threat Detection: AF-WAdaBoost

What is AF-WAdaBoost, and Why Does It Matter?

Cybersecurity is getting trickier, especially in Internet of Things (IoT) environments. With so many smart devices connected, the chances of cyberattacks increase, and older security methods just can’t keep up. That is where Artificial Fish Swarm-driven Weight-normalized Adaboost (AF-WAdaBoost) comes in—a powerful AI-driven model designed to make cyber threat detection faster, smarter, and more efficient (Alsulami, 2024).

AF-WAdaBoost works differently from traditional security models. Instead of relying on fixed rules or signature-based detection (which hackers can easily bypass), it learns from attack patterns, dynamically adjusting its detection techniques to spot threats before they cause damage (Alsulami, 2024).

AI-Driven Model: How AF-WAdaBoost Improves Accuracy and Resilience Against Cyberattacks

Most security models either misidentify threats (false positives) or miss real attacks altogether. AF-WAdaBoost eliminates these problems by using intelligent algorithms to refine threat classification.

Here’s why AF-WAdaBoost is different: ✔ Higher accuracy – It recognizes threats even when they don’t match known attack patterns. ✔ Smarter threat detection – Unlike standard models, it learns and adapts, making it effective against new, evolving attacks. ✔ Fewer false alarms – No more wasting time chasing down harmless activity mistaken for cyber threats.

A comparison of security models shows AF-WAdaBoost performing better than traditional methods, as seen in :

Model	Accuracy (%)	Precision (%)	F-Measure (%)
MLP	88.20	88.17	89.62
Random Forest	90.06	92.22	91.71
Boosting Algorithm	99.98	–	–
Decision Tree	99.40	99.00	99.00
SVM	98.20	98.00	98.00
AF-WAdaBoost	99.99	99.40	99.70

With better accuracy and fewer false positives, AF-WAdaBoost proves to be one of the most effective AI-driven cybersecurity models for IoT environments (Alsulami, 2024).

What’s Inside AF-WAdaBoost? The Key Components

AF-WAdaBoost combines two advanced AI techniques to make cyber threat detection faster, more reliable, and highly adaptive:

1. Artificial Fish Swarm Optimization (AF)

This is inspired by how fish work together to locate food efficiently. In cybersecurity terms, this method optimizes the detection of cyber threats by adjusting model parameters dynamically, ensuring that the security system always stays ahead of attackers (Alsulami, 2024).

2. Weight-normalized Adaboost (WAdaBoost)

This technique boosts the accuracy of weak classifiers, refining cybersecurity models to focus on high-risk attack patterns while minimizing false alarms. Unlike traditional Adaboost methods, WAdaBoost makes adjustments based on real-time cyberattack trends, improving long-term threat detection in IoT environments (Alsulami, 2024).

By combining Artificial Fish Swarm Optimization and Weight-normalized Adaboost, AF-WAdaBoost transforms IoT security into a self-learning, adaptive defense system.

4. AI-Driven Model: Data Collection and Preprocessing

Where the Data Comes From: Cyber Threat Datasets

To make sure AF-WAdaBoost works in real-world cybersecurity applications, researchers tested the model on three widely recognized IoT threat datasets:

• CICIDS2017 – Contains a mix of real-world cyber threats, including denial-of-service (DoS) attacks and malware infections (Alsulami, 2024).
• NSL-KDD – Used to evaluate intrusion detection systems, covering a broad range of known IoT security threats (Alsulami, 2024).
• UNSW-NB15 – Features modern attack scenarios, helping to train AI models on new and emerging cyber threats (Alsulami, 2024).

Accuracy results of AF-WAdaBoost across these datasets:

Dataset	Accuracy (%)
CICIDS2017	98.50
NSL-KDD	97.30
UNSW-NB15	99.90

With UNSW-NB15 showing the highest accuracy, AF-WAdaBoost proves its effectiveness in identifying cyber threats across multiple IoT environments (Alsulami, 2024).

AI-Driven Model: Why Min-Max Normalization is Crucial for Model Accuracy

Before training AF-WAdaBoost, researchers applied Min-Max Normalization to scale feature values between 0 and 1. This ensures balanced data processing, preventing biases caused by uneven feature distributions (Alsulami, 2024).

Normalization formula:

Where:

• W is the normalized value.
• b is the original feature value.
• b_max and b_min represent the maximum and minimum values in the dataset.

By normalizing the dataset, AF-WAdaBoost learns more effectively, ensuring high accuracy without favoring certain variables over others.

AI-Driven Model: AI-Based Feature Selection for Efficient Threat Detection

A cybersecurity model is only as good as the data it learns from. AF-WAdaBoost incorporates smart feature selection techniques to optimize detection performance (Alsulami, 2024).

Key improvements include:

• Eliminating redundant variables that slow down cybersecurity responses.
• Focusing on high-impact threat indicators, such as network traffic anomalies and unusual behavior patterns.
• Prioritizing real-time sensor data, allowing IoT security systems to detect cyberattacks faster.

By refining feature selection, AF-WAdaBoost avoids wasting computational resources, making cyber threat detection faster, smarter, and more efficient.

5. Comparing AF-WAdaBoost to Traditional Cybersecurity Models

How AF-WAdaBoost Stacks Up Against Other Models

Cyber threat detection is essential for keeping Internet of Things devices secure. To understand how AF-WAdaBoost improves cybersecurity, researchers compared it against traditional machine learning models used for intrusion detection.

Some commonly used models include:

• Random Forest: A reliable ensemble method, but it can be slow in real-time applications.
• Support Vector Machine: Works well for structured data but struggles with rapidly evolving threats.
• Decision Trees: Good for classification but prone to errors, especially when data gets complex (Alsulami, 2024).

Each of these models has strengths, but none match AF-WAdaBoost’s ability to adjust on the fly, making cyber threat detection more reliable for Internet of Things systems.

AI-Driven Model: Breaking Down Performance Metrics

To compare different cybersecurity models, three main factors are evaluated:

• Accuracy: Determines how well a model correctly identifies cyber threats.
• Precision: Ensures fewer false alarms so security teams can focus on actual dangers.
• F-Measure: Balances precision and recall to gauge overall detection quality.

AF-WAdaBoost was tested against other models, and the results showed clear improvements.

Model	Accuracy (%)	Precision (%)	F-Measure (%)
MLP	88.2	88.17	89.62
Random Forest	90.06	92.22	91.71
Boosting Algorithm	99.98	–	–
Decision Tree	99.4	99.0	99.0
SVM	98.2	98.0	98.0
AF-WAdaBoost	99.99	99.4	99.7

AF-WAdaBoost performs better than traditional models, proving that AI-driven cybersecurity can be more accurate and efficient (Alsulami, 2024).

Why AF-WAdaBoost is More Effective Than Older Methods

Traditional cybersecurity models follow fixed rules, meaning they recognize threats based on existing patterns. This approach works for known attacks but fails against new, evolving threats. AF-WAdaBoost solves this issue by learning attack patterns in real-time, making security responses smarter and faster (Alsulami, 2024).

Key advantages include:

• Better accuracy: The AI adapts to new threats, identifying attacks that traditional models might miss.
• Fewer false alarms: Security teams waste less time on non-threats, focusing on actual dangers.
• Scalability: Works across different Internet of Things applications, from smart homes to industrial automation (Alsulami, 2024).

With adaptive learning and bio-inspired optimization, AF-WAdaBoost sets a new standard for cybersecurity, making threat detection smarter and more reliable.

6. AI-Driven Model: Sustainability Benefits of AI-Driven Cybersecurity

Reducing Waste in Cyber Threat Detection

Good cybersecurity is not just about catching threats—it is about doing it efficiently. Many traditional security models require constant updates, large-scale computing resources, and heavy power consumption, which increases costs and wastes resources. AF-WAdaBoost fixes this problem by:

• Reducing false positives, so security efforts are not wasted.
• Automating threat detection, cutting down on manual monitoring.
• Using edge computing, allowing Internet of Things devices to analyze threats locally instead of relying on cloud processing (Alsulami, 2024).

By improving efficiency, AF-WAdaBoost helps keep cybersecurity sustainable while protecting Internet of Things networks.

AI-Driven Model: Making Cybersecurity More Scalable and Adaptable

Cybersecurity needs to work across many industries. Internet of Things security applies to everything from smart homes and healthcare systems to industrial automation and transportation. Traditional models often struggle with scalability, but AF-WAdaBoost is designed to adapt.

It can:

• Adjust to different network sizes, handling both small home setups and large industrial systems.
• Refine security strategies over time, improving detection as cyber threats evolve.
• Run on low-power sensors, enhancing security without draining energy (Alsulami, 2024).

Since Internet of Things networks continue to grow, having an adaptable AI model is key to keeping cybersecurity strong.

AI-Driven Model: How AI Supports Sustainable Digital Security

Older cybersecurity models require constant manual updates and high computing power, straining digital infrastructure. AI-driven cybersecurity works smarter, offering solutions that are efficient and scalable.

AF-WAdaBoost helps sustainability by:

• Adapting to new threats without manual updates, keeping cybersecurity reliable.
• Reducing unnecessary cloud processing, cutting down on bandwidth usage.
• Protecting Internet of Things devices, preventing cyberattacks from shortening the lifespan of smart technologies (Alsulami, 2024).

7. Challenges and Where We Go from Here

What Are the Limitations of AI-Driven Cybersecurity?

AI-powered cybersecurity models like AF-WAdaBoost are making huge strides, but they are not perfect. Some challenges still need to be tackled before AI can fully revolutionize threat detection in IoT networks.

• Too Much Computing Power Needed – AI models require a lot of processing capacity, but many IoT devices are small and limited in power. This makes running advanced security algorithms on them difficult (Alsulami, 2024).
• Training Data Can Be Outdated – AI models rely on datasets to learn how to detect cyber threats, but many datasets do not account for new and emerging cyberattacks. That means some threats might slip through unnoticed (Alsulami, 2024).
• Cybercriminals Are Evolving Too – AI models improve over time, but hackers are also getting smarter. Attackers develop new techniques to bypass security systems, meaning cybersecurity AI needs constant updates to stay ahead (Alsulami, 2024).

These challenges do not mean AI-driven cybersecurity is failing; rather, they show where improvements are needed to ensure long-term protection.

Can TinyML Make AI Cybersecurity Even Better?

A promising solution to some of these issues is TinyML—a lightweight version of machine learning designed specifically for small devices like IoT sensors.

TinyML could make cybersecurity faster, more adaptive, and energy-efficient by:

• Reducing Processing Load – Instead of relying on cloud computing, TinyML allows AI models to run directly on IoT devices, improving speed and reducing dependency on external networks.
• Detecting Threats in Real-Time – By operating locally, TinyML can catch suspicious activity the moment it happens, reducing delays in cyber threat responses (Alsulami, 2024).
• Saving Power – AI-driven cybersecurity models often require significant battery life, but TinyML is designed to be efficient, meaning devices can stay secure without draining their energy (Alsulami, 2024).

With AI models becoming more advanced, the combination of TinyML and cybersecurity could be the next big leap forward in IoT security.

What’s Next for AI-Powered Cybersecurity?

Cybersecurity is constantly evolving, and AI-driven solutions are expected to improve in the following ways:

• AI Models That Learn on Their Own – Future cybersecurity systems will use reinforcement learning, allowing them to continuously adapt to new attack strategies without manual updates (Alsulami, 2024).
• Stronger IoT Device Collaboration – AI-powered security will integrate federated learning, meaning IoT devices will work together to train cybersecurity models while preserving privacy (Alsulami, 2024).
• Blockchain for Cybersecurity – AI-driven cybersecurity could combine blockchain technology to create secure and tamper-proof digital records, making it harder for attackers to cover their tracks (Alsulami, 2024).

These innovations will help bridge the gaps in cybersecurity, making IoT networks more resilient to sophisticated cyber threats.

8. How AI-Driven Models Are Transforming IoT Security

Artificial intelligence has changed cybersecurity for the better, allowing faster, smarter, and more accurate threat detection. Models like AF-WAdaBoost are pushing security to new levels, making IoT networks safer than ever before (Alsulami, 2024).

Why AI Cybersecurity Needs to Keep Improving

Cyber threats are constantly changing, and AI-driven security models need regular updates to stay ahead. Future improvements in AI cybersecurity will focus on:

• Real-time learning to counter new and evolving cyber threats.
• Better model accuracy using updated training datasets.
• More energy-efficient AI models for low-power IoT devices (Alsulami, 2024).

Without these improvements, even the best AI-driven security models could fall behind, making continued innovation crucial.

Why Businesses Should Invest in AI Cybersecurity

Organizations that rely on IoT networks need strong security measures to protect against cyberattacks. AI-powered cybersecurity solutions like AF-WAdaBoost offer:

• Stronger protection for IoT devices with adaptive AI security.
• Faster cyber threat detection and prevention using real-time AI monitoring.
• Improved compliance with cybersecurity regulations through automated security management (Alsulami, 2024).

By investing in AI-driven cybersecurity, businesses can stay ahead of cyber threats while ensuring their devices and data remain secure.

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Reference

Alsulami, M.H. An AI-Driven Model to Enhance Sustainability for the Detection of Cyber Threats in IoT Environments. Sensors, 2024, 24, 7179. https://doi.org/10.3390/s24227179

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