Mitigating and Analysis of Memory Usage Attack in IoE System

TL;DR

This paper analyzes memory usage attacks in IoE systems, creates a simulation environment for dynamic analysis, and proposes a lightweight detection algorithm that effectively identifies and mitigates such attacks in resource-constrained devices.

Contribution

It introduces a low-cost simulation environment for analyzing memory attacks and develops a lightweight detection algorithm for IoE devices.

Findings

High efficiency in detecting memory usage attacks

Effective mitigation of attacks in resource-constrained IoE devices

Successful dynamic analysis of resource usage attacks

Abstract

Internet of Everything (IoE) is a newly emerging trend, especially in homes. Marketing forces toward smart homes are also accelerating the spread of IoE devices in households. An obvious risk of the rapid adoption of these smart devices is that many lack controls for protecting the privacy and security of end users from attacks designed to disrupt lives and incur financial losses. Today the smart home is a system for managing the basic life support processes of both small systems, e.g., commercial, office premises, apartments, cottages, and largely automated complexes, e.g., commercial and industrial complexes. One of the critical tasks to be solved by the concept of a modern smart home is the problem of preventing the usage of IoE resources. Recently, there has been a rapid increase in attacks on consumer IoE devices. Memory corruption vulnerabilities constitute a significant class of vulnerabilities in software security through which attackers can gain control of an entire system. Numerous memory corruption vulnerabilities have been found in IoE firmware already deployed in the consumer market. This paper aims to analyze and explain the resource usage attack and create a low-cost simulation environment to aid in the dynamic analysis of the attack. Further, we perform controlled resource usage attacks while measuring resource consumption on resource-constrained victims' IoE devices, such as CPU and memory utilization. We also build a lightweight algorithm to detect memory usage attacks in the IoE environment. The result shows high efficiency in detecting and mitigating memory usage attacks by detecting when the intruder starts and stops the attack.