Biblio

Intrusion Detection systems are important tools in preventing malicious traffic from penetrating into networks and systems. Recently, Intrusion Detection Systems are rapidly enhancing their detection capabilities using machine learning algorithms. However, these algorithms are vulnerable to new unknown types of attacks that can evade machine learning IDS. In particular, they may be vulnerable to attacks based on Generative Adversarial Networks (GAN). GANs have been widely used in domains such as image processing, natural language processing to generate adversarial data of different types such as graphics, videos, texts, etc. We propose a model using GAN to generate adversarial DDoS attacks that can change the attack profile and can be undetected. Our simulation results indicate that by continuous changing of attack profile, defensive systems that use incremental learning will still be vulnerable to new attacks.

Due to improvements in defensive systems, network threats are becoming increasingly sophisticated and complex as cybercriminals are using various methods to cloak their actions. This, among others, includes the application of network steganography e.g. to hide the communication between an infected host and a malicious control server by embedding commands into innocent-looking traffic. Currently, a new subtype of such methods called inter-protocol steganography emerged. It utilizes relationships between two or more overt protocols to hide data. In this paper, we present new inter-protocol hiding techniques which are suitable for real-time services. Afterwards, we introduce and present preliminary results of a novel steganography detection approach which relies on network traffic coloring.