A botnets control strategy based on variable forgetting rate of control commands

Abstract

Botnets take advantage of the vulnerabilities in the device-to-device (D2D) communication in the Internet of Things (IoT) systems. The infect devices may become part of the command and control mechanism of Botnets. These infected devices may forget control commands to remain undetected. State-of-the-art control models for botnets based on how devices are infected and patched in IoT systems while dealing with Botnets assume that the rate at which IoT devices forget the control commands (forgetting rate) is constant and known. These control models ignore the possible variability of the forgetting rate, which may enable bots to operate without being detected. This research fills this gap by proposing a modeling-based strategy that considers the changes in equipment states and repair speed when the forgetting rate is variable. In this regard, first, based on the state-of-the-art botnets control model, the effect of variation in the forgetting rate is analyzed. After that, an improved strategy is proposed for the botnets control model when the forgetting rate is unknown. Finally, the experimental evaluation of the proposed strategy is performed that proves the correctness of the proposed control strategy.

A Hybridly Optimized LSTM‐Based Data Flow Prediction Model for Dependable Online Ticketing

“…Unexpected fluctuations in service request rates and corresponding changes in Internet traffic can directly affect the availability of webbased systems, and a substantial change in traffic may even result in crashing various application services (https:// techcrunch.com/2018/12/26/alexa-crashed-on-christmasday/). Consequently, we see that special measures and strategic approaches are used for load balancing, enhanced stability, and detecting and mitigating malicious actors affecting the availability of Internet-based services and systems [9][10][11].…”