Search

The growth of the Internet of Things (IoT) has led to a significant rise in cyber attacks and an expanded attack surface for the average consumer. In order to protect consumers and infrastructure, research into detecting malicious IoT activity must be of the highest priority. Security research in this area has two key issues: the lack of datasets for training artificial intelligence (AI)-based intrusion detection models and the fact that most existing datasets concentrate only on one type of network traffic. Thus, this study introduces Dragon_Pi, an intrusion detection dataset designed for IoT devices based on side-channel power consumption data. Dragon_Pi comprises a collection of normal and under-attack power consumption traces from separate testbeds featuring a DragonBoard 410c and a Raspberry Pi. Dragon_Slice is trained on this dataset; it is an unsupervised convolutional autoencoder (CAE) trained exclusively on held-out normal slices from Dragon_Pi for anomaly detection. The Dragon_Slice network has two iterations in this study. The original achieves 0.78 AUC without post-processing and 0.876 AUC with post-processing. A second iteration of Dragon_Slice, utilising dropout to further impede the CAE's ability to reconstruct anomalies, outperforms the original network with a raw AUC of 0.764 and a post-processed AUC of 0.89. [ABSTRACT FROM AUTHOR]

Summary: In this article, we propose an ensemble neural network approach so that multiple neuron network models can be deployed concurrently for detecting anomalies in a system. Additionally, a high‐performance architecture integrating the neural networks for reconfigurable hardware is also designed to exploit the parallelism of the technology. We carefully implement four different models for detecting SYN, DNS, UNP, and ICMP attacks with the same configuration of neural networks on the NetFPGA platform. The neural networks can be executed in the pipeline to improve performance. Experiments with the generated dataset are conducted to test both performance in terms of throughput and accuracy. Our experimental results show that the system can process packets with throughput up to 30.48Gbps and is able to recognize 99.98% attacking packets with only 0.98% false alarms. [ABSTRACT FROM AUTHOR]

This study proposes the wider use of non-intrusive side-channel power data in cybersecurity for intrusion detection. An in-depth analysis of side-channel IoT power behaviour is performed on two well-known IoT devices—a Raspberry Pi 3 model B and a DragonBoard 410c—operating under normal conditions and under attack. Attacks from the categories of reconnaissance, brute force and denial of service are applied, and the side-channel power data of the IoT testbeds are then studied in detail. These attacks are used together to further compromise the IoT testbeds in a "capture-the-flag scenario", where the attacker aims to infiltrate the device and retrieve a secret file. Some clear similarities in the side-channel power signatures of these attacks can be seen across the two devices. Furthermore, using the knowledge gained from studying the features of these attacks individually and the signatures witnessed in the "capture the flag scenario", we show that security teams can reverse engineer attacks applied to their system to achieve a much greater understanding of the events that occurred during a breach. While this study presents behaviour signatures analysed visually, the acquired power series datasets will be instrumental for future human-centred AI-assisted intrusion detection. [ABSTRACT FROM AUTHOR]

This study proposes a heterogeneous hardware-based framework for network intrusion detection using lightweight artificial neural network models. With the increase in the volume of exchanged data, IoT networks' security has become a crucial issue. Anomaly-based intrusion detection systems (IDS) using machine learning have recently gained increased popularity due to their generation's ability to detect unseen attacks. However, the deployment of anomaly-based AI-assisted IDS for IoT devices is computationally expensive. A high-performance and ultra-low power consumption anomaly-based IDS framework is proposed and evaluated in this paper. The framework has achieved the highest accuracy of 98.57% and 99.66% on the UNSW-NB15 and IoT-23 datasets, respectively. The inference engine on the MAX78000EVKIT AI-microcontroller is 11.3 times faster than the Intel Core i7-9750H 2.6 GHz and 21.3 times faster than NVIDIA GeForce GTX 1650 graphics cards, when the power drawn was 18mW. In addition, the pipelined design on the PYNQ-Z2 SoC FPGA board with the Xilinx Zynq xc7z020-1clg400c device is optimised to run at the on-chip frequency (100 MHz), which shows a speedup of 53.5 times compared to the MAX78000EVKIT. [ABSTRACT FROM AUTHOR]

Software-Defined Networking has became one of the most efficient network architectures to deal with complexity, policy control improvement, and vendor dependencies removal. Besides, with the diversity of network attacks, the SDN architecture faces many security issues that need to be taken into account. In this work, we propose an architecture for SDN-based secured forwarding devices (switches) by extending our previous architecture - HPOFS with multiple security functions including lightweight DDoS mechanisms, signature-based and anomaly-based IDS. We implement our architecture on a heterogeneous system including host processors, GPU, and FPGA boards. To the best of our knowledge, this is the first forwarding device for SDN implemented on a heterogeneous system in the literature. Our system not only is enhanced security but also provides a high-speed switching capacity based on the OpenFlow standard. The implemented design on GTX Geforce 1080 G1 for training phase is 14× faster when compared to CPU Intel Core i7 – 4770, 3.4GHz, 16GB of RAM on the Ubuntu version 14.04. The switching function along with three lightweight DDoS detection/prevention mechanisms provide processing speed at 39.48 Gbps on a NetFPGA-10G board (with a Xilinx xc5vtx240t FPGA device). Especially, our neural network models on the NetFPGA-10G board outperform CPU in processing performance by reaching throughputs at 4.84 Gbps. Moreover, the implemented neural network model achieves 99.01% precision with only 0.02% false positive rate when processing a dataset. [ABSTRACT FROM AUTHOR]