Surveying Emerging Trends in DDoS Defense

This The DDoS attack threat is evolving, and because of this, organizations are discovering and using new modern technologies to lay the ground for more effective defensive strategies. This paper is devoted to the investigation of the most efficient methods fighting DDoS – downtime of the network, and ensuring cybersecurity on different domains. First of all, the integration of Convolutional Neural Networks (CNNs) into cybersecurity is a very promising move with respect to fighting exactly the phishing and application-layer DDoS attacks in greater details than the machine learning approaches like the LSTMs and SAEs. Another aspect of building the effective opposition against the dummy data attacks on the critical infrastructures, for example on the power systems, is creating the multi-dimensional mitigation models composed of various timely detection techniques and robust network architecture. In addition, the usage of Physically Unclonable Functions (PUFs) in network architectures provides a means of authentication as well as access control that can improve the resilience of a network against DDoS attacks. PUFs enables the blockade of unwanted packets of high volume traffic, allowing granular traffic filtration and isolation. By using hardware solutions such as Distributed-Denial-of-Service (DDoS) attack prevention, SDN-biased security frame with deep learning algorithms can improve network resilience with significant detection and response to slow-rate DDoS attacks. At last EWMA, KNN, and CUSUM as statistical methods integrated with FOG computing architectures ensure real time and effective solution for the detection and mitigation of DDoS attacks in the IoT networks, making them immune to the current as well as the continuously emerging cyber threats. Through the integration of these cutting edge methods, organizations will be able to hold their ground against cyberattacks catalyzed by DDoS menace and stay ahead of dynamic threats whenever they arise.