DDoS Evolution and Impact

By: Nicko Cajes; Northern Bukidnon State College, Philippines

ABSTRACT

The increase in Distributed Denial of Service attacks shows the evolving nature of these cyber threats. DDoS attacks which overwhelm the target servers to disrupt the services can cause great damage to businesses and can destroy customer trust. Traditional security systems have found it difficult to detect these advanced attacks exposing their limitation against this threat. Hybrid deep learning techniques which excel in identifying complex attack patterns have emerged as a promising solution. As the DDoS attack continues to evolve, the organization must adopt advanced security mechanisms, like hybrid deep learning, to stay ahead of cybercriminals and protect their critical services.

INTRODUCTION

Approximately 55% of the incidence of Distributed Denial of Service (DDoS) attacks in 2024 have different types and where only executed by one attacker, this shows a 24% growth compared to last year [1]. This event shows the evolution of attackers’ techniques in their attacks. DDoS is not just an attack that causes frustration, it also causes huge damage if you are targeted because it will stop the service that you provide or you allocate which will result in the downfall of their business and loss of trust by customers, especially on the security part. This article will discuss how to mitigate DDoS attacks and provide insights on how to solve the rising record of DDoS, due to the new techniques that attackers made in their attacks and methods that leap together with the advent of technology, some questions will come to our mind if we are aware of the effects that it can give us like, is my security system able to cope up with these new kinds of attack? Can my firewall or traditional intrusion detection system detect this new kind of attack?

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DOS TO DDOS ATTACK

DDoS attacks simply overwhelmed target servers back then. However, as years passed by, attacks that they are doing have evolved significantly making it hard for traditional security systems to detect them. The old style of DDoS attackers involves remote scanning machine vulnerability, when vulnerability is found they then exploit it and make it as a slave machine. After exploitation, injection of attack code will be done where it will contain commands on attacking a target server by sending a huge request that will overwhelm, exhaust, and make the server down [2]. This is somehow different to the traditional way of shutting the server down which is the Denial of Service (DoS) attack, which was simply done by the attacker itself with the use of its own device without compromising any vulnerabilities from other device and create any network of botnets.

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Figure 1: Denial of Service (DoS) and Distributed Denial of Service (DDoS) Attack

DDOS INCIDENT AND HYBRID DEEP LEARNING SOLUTION

In 2016, there was an attack named Mirai botnet that infected 15 million devices that run on the Linux operating system, it then launched an attack on a French hosting provider that resulted in the downfall of their server [2]. To solve this problem, researchers explored possible solutions that can be found. The technique of applying hybrid deep learning techniques is a top pick that they mention, as it is not only good in identifying data in real-time but also good in identifying patterns. A study conducted by [3], uses hybrid deep learning techniques in enhancing security in the IoT environment. For context, this is one of the huge threats in the IoT environment, as identifying patterns are complex in these devices. However, the results in [3], have shown that their approach has a good result achieving 99.6% accuracy in detecting malicious and non-malicious attacks which effectively demonstrates the huge potential of hybrid deep learning techniques in solving this problem. But even with the benefit given, how can organizations or companies easily adapt to this, so they do not fall behind the evolving DDoS attack techniques?

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Figure 2: Hybrid Deep Learning Approach Methodology

CONCLUSION

DDoS attacks have advanced and are already targeting large companies. The necessity of a strong, reliable, and efficient security mechanism is considered a must. The continuous rise of cyber-attacks, even with the constant development of security measures against them, simply testifies that no device is safe. Companies or organizations need to find a way to counter this by applying hybrid deep learning techniques in their security mechanisms to prevent them from being targeted in the future, which can cause the downfall of their business.

The war against DDoS attacks is far from over, especially if we are not armed or prepared to fight against it. The question is, are we ready to face it? If not, future problems related to this are expected. In this modern time, almost all are connected through devices, and transactions are mostly done digitally whether it is normal or emergency. The idea of security is not just a requirement anymore, it is needed. That is why, being aware of this topic and applying the necessary steps to avoid the DDoS attack becomes handy.

REFERENCES

  1. D’Souza, J. (2024, October 30). DDOS Statistics by market share, organisation size, impact and facts. Sci-Tech Today. https://www.sci-tech-today.com/stats/ddos-statistics/#:~:text=According%20to%20DDoS%20Statistics%2C%202024%20is%20expected%20to,for%20a%2012%25%20increase%20over%20the%20previous%20year.
  2. Mirkovic, J., & Reiher, P. (2004). A taxonomy of DDoS attack and DDoS defense mechanisms. ACM SIGCOMM Computer Communication Review34(2), 39-53.
  3. Maaz, M., Ahmed, G., Al-Shamayleh, A. S., Akhunzada, A., Siddiqui, S., & Al-Ghushami, A. H. (2024). Empowering IoT Resilience: Hybrid Deep Learning Techniques for Enhanced Security. IEEE Access.
  4. Dahiya, A., & Gupta, B. B. (2021). A reputation score policy and Bayesian game theory based incentivized mechanism for DDoS attacks mitigation and cyber defense. Future Generation Computer Systems, 117, 193-204.
  5. Manasrah, A. M., Aldomi, A. A., & Gupta, B. B. (2019). An optimized service broker routing policy based on differential evolution algorithm in fog/cloud environment. Cluster Computing, 22, 1639-1653.
  6. Mrunal K. Shende (2021) Incorporation of cyber security in intelligent transportation systems (ITS), Insights2Techinfo, pp. 1

Cite As

Cajes N. (2025) DDoS Evolution and Impact, Insights2Techinfo, pp.1

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