Redefining IoT Security: Hybrid Deep Learning Against DDoS Attacks

By: Nicko Cajes; Northern Bukidnon State College, Philippines

ABSTRACT

The rapid proliferation of Internet of Things (IoT) devices increases their vulnerability to cyber-attacks specifically the Distributed Denial of Service Attacks. Traditional security systems are having a hard time keeping up with the evolving techniques of the attackers. Hybrid deep learning that combines multiple machine learning models provides a promising solution to detect and mitigate DDoS attacks. Hybrid deep learning technique excels in identifying difficult patterns and can also compensate for the limitations of the traditional model. Despite the challenges related to dataset reliability, the hybrid deep learning model can provide a proactive approach to enhance the protection of IoT devices against evolving cyber threats.

INTRODUCTION

The Internet of Things (IoT) is one of the products of emerging technologies. This device is one of the most rapidly growing technological trends to date. According to Statista, the total number of installed IoT-capable devices was estimated to reach at least 30.9 billion units in the year 2025 [1], which indicates a huge difference in numbers if we compare it to the 13.8 billion units in the year 2021 as shown in Figure 1.

Figure 1: Number of IoT devices installed (orange bar indicates estimation)

These stats show proof that IoT helped make the lives of people easier by connecting devices. However, because of these numbers that grow a lot. Attackers eyes become interested in exploiting these devices, since it is easy to penetrate because of their weaknesses.

Distributed Denial of Service (DDoS) attack is one of the top picks that can hit this. DDOS attack is a type of cyber-attack where the criminal finds a way to prevent the user from using a legitimate service by sending a flood of traffic on the server, it makes the server overwhelmed and eventually becomes unavailable. This article will discuss how effective hybrid deep learning techniques are in the context of solving DDoS attack problems. With the help of this article, the researcher hopes to show the huge potential of these techniques in helping and securing IoT devices.

HYBRID DEEP LEARNING IN DDOS ATTACK

Maybe you are wondering. Why is it that this problem is still not solved, even though there are a lot of cybersecurity experts in this field? The answer is yes, it is true that there are lots of experts. However, attackers have evolved dynamically in their attack methods, they become cleverer and more creative in finding the weaknesses of the security systems developed by the researchers. One of the weaknesses of the traditional security systems is their limitation such as the resources, privacy-related constraints, models used, dataset used in training, and the IoT device itself. One of the suggested things to do in this field to solve this problem is the use of hybrid deep learning techniques.

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Figure 2: Hybrid Deep Learning Technique Approach Pros

Hybrid deep learning techniques are the combination of two or more deep learning techniques that work effectively against the problem provided by the DDoS attack.

Why use this? It is because these techniques excel in identifying patterns which are complex in an IoT environment. Just imagine combining Stephen Curry and Shaquille O’Neale in the NBA to win a championship, notice that these two players are in different sizes and different positions played, but they have their respective abilities that excel in their positions. When you combine the impressive shooting of Curry and the dominance of Shaquille O’Neale in the paint. There is no doubt that winning a championship is possible. That works similarly to applying hybrid deep learning techniques, aside from their unique abilities, they can also compensate for each other’s limitations or weaknesses that made them strong.

DDOS ATTACK MECHANISM

IoT has transformed the ease of collecting data into the most convenient way. Their ability to interact with another machine without the intervention of humans (interoperability) becomes the uniqueness that the people see and persuades them to use it aside from its low operation cost. How do the attackers move in these devices? The DDoS attacker will find vulnerabilities of these devices, it will then turn them into slaves, inject some attack codes, and proceed to attack once the command is given. Just the same as the Mirai botnet attack in the past few years, where the attackers converted some Linux operating systems into slaves and then launched an attack on a service provider company in France, shows the necessity of creating a strong hybrid deep learning defense mechanism, as that attack gives huge damage not only on the company but also to the users trust on their security systems performance [2].

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Figure 2: DDoS Botnet Attack Diagram in an IoT Network

Hybrid deep learning techniques highlight its ability to effectively predict things using data with the help of its structure coming from the human brain. This gives most of the solutions to the problems experienced nowadays by traditional security systems. By combining deep learning techniques detection, mitigation, and prevention is highly possible. A study conducted by [3] that uses hybrid deep learning techniques in detecting cyber-attacks in IoT achieved a 99.6% accuracy rate by using the Kitsune dataset in training their model due to its relevance and the record it contains about IoT-related attacks. There is still a lot to improve even though the number shown was already satisfying, as per the recommendation of the researchers in [3], the integration of Natural Language Processing would greatly enhance the performance of their model. However, even with the great performance it provides, challenges in development are inevitable. To achieve the result, the challenge of acquiring a consistent and reliable dataset to train the model is one of the huge challenges they faced during development, and it is not new in these types of papers that involve machine learning and deep learning algorithms. In addressing this challenge, the hybrid deep learning approach can’t just enhance the security system but also neutralize the uprising records of DDoS-related attacks.

CONCLUSION

Using a traditional security system is allowed. But, if we look at their performance in these modern times especially in the evolving techniques of cyber criminals, there are a lot of questions that will come to mind. Is it still okay to use traditional security systems? Can this system cope up when I am attacked? Well, we don’t know if it will work or not in a real-life environment. But will you wait for the time to get hit before you evade? Can you compensate for the damage it will cause when you are hit by the attack? If you are not sure, think twice. Everything nowadays is penetrable, technological tools are available everywhere. There is a famous quote that says, “Prevention is better than cure” and it is relatable in every field.

REFERENCES

  1. Global IoT and non-IoT connections 2010-2025 | Statista. (2022, September 6). Statista. https://www.statista.com/statistics/1101442/iot-number-of-connected-devices-worldwide/
  2. Vishwakarma, R., & Jain, A. K. (2019b). A survey of DDoS attacking techniques and defence mechanisms in the IoT network. Telecommunication Systems, 73(1), 3–25. https://doi.org/10.1007/s11235-019-00599-z
  3. Empowering IoT resilience: hybrid deep learning techniques for enhanced security. (2024). IEEE Journals & Magazine | IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/10720064
  4. Lv, L., Wu, Z., Zhang, L., Gupta, B. B., & Tian, Z. (2022). An edge-AI based forecasting approach for improving smart microgrid efficiency. IEEE Transactions on Industrial Informatics, 18(11), 7946-7954.
  5. Jain, A. K., & Gupta, B. B. (2022). A survey of phishing attack techniques, defence mechanisms and open research challenges. Enterprise Information Systems, 16(4), 527-565.
  6. Chokkappagari R. (2024) IoT and Cybersecurity: Best Practices for Protecting Connected Devices, Insights2Techinfo, pp. 1; https://insights2techinfo.com/iot-and-cybersecurity-best-practices-for-protecting-connected-devices/

Cite As

Cajes N. (2025) Redefining IoT Security: Hybrid Deep Learning Against DDoS Attacks, Insights2Techinfo, pp.1

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