Detecting Energy Consumption Cyber Attacks on Resource-Constrained Smart Home Devices

Concepts de base

A lightweight algorithm to detect energy consumption attacks on smart home devices by monitoring their packet reception rates across different protocols.

Résumé

The paper presents a lightweight algorithm to detect energy consumption attacks on smart home devices. The key aspects are:

1. The algorithm considers three popular IoT protocols - TCP, UDP, and MQTT - and analyzes the packet reception rate of the devices under normal and attack conditions.
2. It also takes into account different device statuses - idle, active, and under attack - to accurately differentiate normal and abnormal packet reception behaviors.
3. The algorithm measures the energy consumption of the smart devices in parallel to determine if the observed packet reception patterns are caused by an energy consumption attack.
4. It uses a multi-stage approach to classify the device behavior as normal or abnormal, triggering an alert if the abnormal behavior persists beyond a threshold.
5. The proposed technique is designed to be lightweight and resource-efficient, making it suitable for deployment directly on the resource-constrained smart home devices.
6. Experiments on a Raspberry Pi testbed show the algorithm can effectively detect energy consumption attacks by analyzing the packet reception rates across different protocols.

Stats

The normal average of the received packets for the TCP protocol in 30 minutes fluctuates between 2,000 and 6,000 packets.
The abnormal behavior of the received packets by the smart device for the UDP protocol is between 9,000 and more than 12,000 packets.
The normal behavior of the packet reception rate of the Raspberry Pi is between 1,500 packets and less than or equal to 6,000 packets, while the abnormal behavior is between 7,000 and more than 12,000 packets.

Citations

"One of the critical tasks to be solved by the concept of a modern smart home is the problem of preventing energy attacks spread and the usage of IoT infrastructure."
"Monitoring the energy consumption of IoT devices is a possible way to detect those performing attacks which require significant energy consumption."

Idées clés tirées de

Detection of Energy Consumption Cyber Attacks on Smart Devices

by Zainab Alwai... à arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19434.pdf

Questions plus approfondies

How can this algorithm be extended to detect other types of cyber attacks on smart home devices beyond energy consumption attacks

To extend the algorithm to detect other types of cyber attacks on smart home devices beyond energy consumption attacks, several modifications and additions can be made. One approach could involve incorporating anomaly detection techniques to identify abnormal behaviors in addition to energy consumption spikes. By analyzing patterns in network traffic, device behavior, and communication protocols, the algorithm can be trained to recognize patterns indicative of various cyber attacks such as DDoS attacks, malware infiltration, or unauthorized access attempts. Furthermore, integrating machine learning algorithms could enhance the algorithm's ability to adapt and detect new forms of cyber threats based on historical data and real-time monitoring. Additionally, incorporating threat intelligence feeds and known attack signatures can help the algorithm proactively identify and mitigate potential cyber attacks before they cause significant damage to the smart home ecosystem.

What are the potential limitations of using packet reception rate as the sole indicator for detecting energy consumption attacks, and how could this approach be combined with other detection techniques

Using packet reception rate as the sole indicator for detecting energy consumption attacks may have limitations in terms of accuracy and specificity. While changes in packet reception rate can provide valuable insights into potential energy consumption attacks, it may not always be a definitive indicator of malicious activity. False positives could occur due to legitimate spikes in network traffic or device activity, leading to unnecessary alerts and potential disruptions in the smart home environment. To address this limitation, the approach of combining packet reception rate analysis with other detection techniques such as anomaly detection, behavior analysis, and signature-based detection can enhance the algorithm's effectiveness and reliability. By cross-referencing multiple indicators and leveraging a multi-layered detection approach, the algorithm can improve its accuracy in identifying energy consumption attacks while reducing false positives.

What are the broader implications of securing resource-constrained IoT devices against energy-based attacks, and how could this contribute to the overall resilience of smart home ecosystems

Securing resource-constrained IoT devices against energy-based attacks has significant implications for the overall resilience of smart home ecosystems. By implementing robust security measures to detect and prevent energy consumption attacks, smart home devices can operate efficiently and securely, safeguarding sensitive data and ensuring uninterrupted service delivery. Protecting IoT devices from cyber threats not only enhances the privacy and safety of homeowners but also contributes to the stability and reliability of smart home ecosystems. Furthermore, by fortifying the security of resource-constrained IoT devices, the overall cybersecurity posture of smart homes is strengthened, creating a more resilient infrastructure against evolving cyber threats. This proactive approach to security can instill confidence in users, promote the widespread adoption of IoT technology, and foster a safer and more secure smart home environment for all stakeholders.