Lidé

Ing. Maria Rigaki

Všechny publikace

Machete: Dissecting the Operations of a Cyber Espionage Group in Latin America

  • DOI: 10.1109/EuroSPW.2019.00058
  • Odkaz: https://doi.org/10.1109/EuroSPW.2019.00058
  • Pracoviště: Katedra počítačů, Centrum umělé inteligence
  • Anotace:
    Reports on cyber espionage operations have been on the rise in the last decade. However, operations in Latin America are heavily under researched and potentially underestimated. In this paper we analyze and dissect a cyber espionage tool known as Machete. Our research shows that Machete is operated by a highly coordinated and organized group who focuses on Latin American targets. We describe the five phases of the APT operations from delivery to exfiltration of information and we show why Machete is considered a cyber espionage tool. Furthermore, our analysis indicates that the targeted victims belong to military, political, or diplomatic sectors. The review of almost six years of Machete operations show that it is likely operated by a single group, and their activities are possibly state-sponsored. Machete is still active and operational to this day.

Bringing a GAN to a Knife-Fight: Adapting Malware Communication to Avoid Detection

  • DOI: 10.1109/SPW.2018.00019
  • Odkaz: https://doi.org/10.1109/SPW.2018.00019
  • Pracoviště: Katedra počítačů, Centrum umělé inteligence
  • Anotace:
    Generative Adversarial Networks (GANs) have been successfully used in a large number of domains. This paper proposes the use of GANs for generating network traffic in order to mimic other types of traffic. In particular, our method modifies the network behavior of a real malware in order to mimic the traffic of a legitimate application, and therefore avoid detection. By modifying the source code of a malware to receive parameters from a GAN, it was possible to adapt the behavior of its Command and Control (C2) channel to mimic the behavior of Facebook chat network traffic. In this way, it was possible to avoid the detection of new-generation Intrusion Prevention Systems that use machine learning and behavioral characteristics. A real-life scenario was successfully implemented using the Stratosphere behavioral IPS in a router, while the malware and the GAN were deployed in the local network of our laboratory, and the C2 server was deployed in the cloud. Results show that a GAN can successfully modify the traffic of a malware to make it undetectable. The modified malware also tested if it was being blocked and used this information as a feedback to the GAN. This work envisions the possibility of self-adapting malware and self-adapting IPS.

Za stránku zodpovídá: Ing. Mgr. Radovan Suk