Biblio

Advanced metamorphic malware and ransomware use techniques like obfuscation to alter their internal structure with every attack. Therefore, any signature extracted from such attack, and used to bolster endpoint defense, cannot avert subsequent attacks. Therefore, if even a single such malware intrudes even a single device of an IoT network, it will continue to infect the entire network. Scenarios where an entire network is targeted by a coordinated swarm of such malware is not beyond imagination. Therefore, the IoT era also requires Industry-4.0 grade AI-based solutions against such advanced attacks. But AI-based solutions need a large repository of data extracted from similar attacks to learn robust representations. Whereas, developing a metamorphic malware is a very complex task and requires extreme human ingenuity. Hence, there does not exist abundant metamorphic malware to train AI-based defensive solutions. Also, there is currently no system that could generate enough functionality preserving metamorphic variants of multiple malware to train AI-based defensive systems. Therefore, to this end, we design and develop a novel system, named X-Swarm. X-Swarm uses deep policy-based adversarial reinforcement learning to generate swarm of metamorphic instances of any malware by obfuscating them at the opcode level and ensuring that they could evade even capable, adversarial-attack immune endpoint defense systems.

This paper presents Zero-Day Attack Packet Highlighting System. Proposed system outputs zero-day attack packet information from flow extracted as result of regression inspection of packets stored in flow-based PCA. It also highlights raw data of the packet matched with rule. Also, we design communication protocols for sending and receiving data within proposed system. Purpose of the proposed system is to solve existing flow-based problems and provides users with raw data information of zero-day packets so that they can analyze raw data for the packets.

Botnets are one of the major threats on the Internet. They are used for malicious activities to compromise the basic network security goals, namely Confidentiality, Integrity, and Availability. For reliable botnet detection and defense, deep learning-based approaches were recently proposed. In this paper, four different deep learning models, namely Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), hybrid CNN-LSTM, and Multi-layer Perception (MLP) are applied for botnet detection and simulation studies are carried out using the CTU-13 botnet traffic dataset. We use several performance metrics such as accuracy, sensitivity, specificity, precision, and F1 score to evaluate the performance of each model on classifying both known and unknown (zero-day) botnet traffic patterns. The results show that our deep learning models can accurately and reliably detect both known and unknown botnet traffic, and show better performance than other deep learning models.

The zero-day attack in networks exploits an undiscovered vulnerability, in order to affect/damage networks or programs. The term “zero-day” refers to the number of days available to the software or the hardware vendor to issue a patch for this new vulnerability. Currently, the best-known defense mechanism against the zero-day attacks focuses on detection and response, as a prevention effort, which typically fails against unknown or new vulnerabilities. To the best of our knowledge, this attack has not been widely investigated for Software-Defined Networks (SDNs). Therefore, in this work we are motivated to develop anew zero-day attack detection and prevention mechanism, which is designed and implemented for SDN using a modified sandbox tool, named Cuckoo. Our experiments results, under UNIX system, show that our proposed design successfully stops zero-day malwares by isolating the infected client, and thus, prevents these malwares from infesting other clients.

A3 is an execution management environment that aims to make network-facing applications and services resilient against zero-day attacks. A3 recently underwent two adversarial evaluations of its defensive capabilities. In one, A3 defended an App Store used in a Capture the Flag (CTF) tournament, and in the other, a tactically relevant network service in a red team exercise. This paper describes the A3 defensive technologies evaluated, the evaluation results, and the broader lessons learned about evaluations for technologies that seek to protect critical systems from zero-day attacks.