The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by usingmore »
This content will become publicly available on November 1, 2023
An Efficient One-Class SVM for Novelty Detection in IoT
One-Class Support Vector Machines (OCSVMs) are a set of common approaches for novelty
detection due to their flexibility in fitting complex nonlinear boundaries between normal
and novel data. Novelty detection is important in the Internet of Things (“IoT”) due to
the potential threats that IoT devices can present, and OCSVMs often perform well in
these environments due to the variety of devices, traffic patterns, and anomalies that IoT
devices present. Unfortunately, conventional OCSVMs can introduce prohibitive memory
and computational overhead in detection. This work designs, implements, and evaluates an
efficient OCSVM for such practical settings. We extend Nyström and (Gaussian) Sketching
approaches to OCSVM, combining these methods with clustering and Gaussian mixture
models to achieve 15-30x speedup in prediction time and 30-40x reduction in memory
requirements without sacrificing detection accuracy. Here, the very nature of IoT devices is
crucial: they tend to admit few modes of normal operation, allowing for efficient pattern
compression.
- Award ID(s):
- 1953740
- Publication Date:
- NSF-PAR ID:
- 10395945
- Journal Name:
- Transactions on machine learning research
- Volume:
- 2022
- Issue:
- 11
- Page Range or eLocation-ID:
- 1-24
- ISSN:
- 2835-8856
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Edge devices with attentive sensors enable various intelligent services by exploring streams of sensor data. However, anomalies, which are inevitable due to faults or failures in the sensor and network, can result in incorrect or unwanted operational decisions. While promptly ensuring the accuracy of IoT data is critical, lack of labels for live sensor data and limited storage resources necessitates efficient and reliable detection of anomalies at edge nodes. Motivated by the existence of unique sparsity profiles that express original signals as a combination of a few coefficients between normal and abnormal sensing periods, we propose a novel anomaly detection approach, called ADSP (Anomaly Detection with Sparsity Profile). The key idea is to apply a transformation on the raw data, identify top-K dominant components that represent normal data behaviors, and detect data anomalies based on the disparity from K values approximating the periods of normal data in an unsupervised manner. Our evaluation using a set of synthetic datasets demonstrates that ADSP can achieve 92%–100% of detection accuracy. To validate our anomaly detection approach on real-world cases, we label potential anomalies using a range of error boundary conditions using sensors exhibiting a straight line in Q-Q plot and strong Pearson correlationmore »
-
Internet of Things (IoT) devices and applications can have significant vulnerabilities, which may be exploited by adversaries to cause considerable harm. An important approach for mitigating this threat is remote attestation, which enables the defender to remotely verify the integrity of devices and their software. There are a number of approaches for remote attestation, and each has its unique advantages and disadvantages in terms of detection accuracy and computational cost. Further, an attestation method may be applied in multiple ways, such as various lev- els of software coverage. Therefore, to minimize both security risks and computational overhead, defenders need to decide strategically which attestation methods to apply and how to apply them, depending on the characteristic of the devices and the potential losses. To answer these questions, we first develop a testbed for remote attestation of IoT devices, which enables us to measure the detection accuracy and performance overhead of various attestation methods. Our testbed integrates two example IoT applications, memory-checksum based attestation, and a variety of software vulnerabilities that allow adversaries to inject arbitrary code into running applications. Second, we model the problem of finding an optimal strategy for applying remote attestation as a Stackelberg security game between amore »
-
Skateboarding as a method of transportation has become prevalent, which has increased the occurrence and likelihood of pedestrian–skateboarder collisions and near-collision scenarios in shared-use roadway areas. Collisions between pedestrians and skateboarders can result in significant injury. New approaches are needed to evaluate shared-use areas prone to hazardous pedestrian–skateboarder interactions, and perform real-time, in situ (e.g., on-device) predictions of pedestrian–skateboarder collisions as road conditions vary due to changes in land usage and construction. A mechanism called the Surrogate Safety Measures for skateboarder–pedestrian interaction can be computed to evaluate high-risk conditions on roads and sidewalks using deep learning object detection models. In this paper, we present the first ever skateboarder–pedestrian safety study leveraging deep learning architectures. We view and analyze state of the art deep learning architectures, namely the Faster R-CNN and two variants of the Single Shot Multi-box Detector (SSD) model to select the correct model that best suits two different tasks: automated calculation of Post Encroachment Time (PET) and finding hazardous conflict zones in real-time. We also contribute a new annotated data set that contains skateboarder–pedestrian interactions that has been collected for this study. Both our selected models can detect and classify pedestrians and skateboarders correctly and efficiently. However, duemore »
-
Advances in low-power electronics and machine learning techniques lead to many novel wearable IoT devices. These devices have limited battery capacity and computational power. Thus, energy harvesting from ambient sources is a promising solution to power these low-energy wearable devices. They need to manage the harvested energy optimally to achieve energy-neutral operation, which eliminates recharging requirements. Optimal energy management is a challenging task due to the dynamic nature of the harvested energy and the battery energy constraints of the target device. To address this challenge, we present a reinforcement learning-based energy management framework, tinyMAN, for resource-constrained wearable IoT devices. The framework maximizes the utilization of the target device under dynamic energy harvesting patterns and battery constraints. Moreover, tinyMAN does not rely on forecasts of the harvested energy which makes it a prediction-free approach. We deployed tinyMAN on a wearable device prototype using TensorFlow Lite for Micro thanks to its small memory footprint of less than 100 KB. Our evaluations show that tinyMAN achieves less than 2.36 ms and 27.75 μJ while maintaining up to 45% higher utility compared to prior approaches.
