IoT Security Vulnerabilities and Predictive Signal Jamming Attack Analysis in LoRaWAN

Internet of Things (IoT) gains popularity in recent times due to its flexibility, usability, diverse applicability and ease of deployment. However, the issues related to security is less explored. The IoT devices are light weight in nature and have low computation power, low battery life and low memory. As incorporating security features are resource expensive, IoT devices are often found to be less protected and in recent times, more IoT devices have been routinely attacked due to high profile security flaws. This paper aims to explore the security vulnerabilities of IoT devices particularly that use Low Power Wide Area Networks (LPWANs). In this work, LoRaWAN based IoT security vulnerabilities are scrutinised and loopholes are identified. An attack was designed and simulated with the use of a predictive model of the device data generation. The paper demonstrated that by predicting the data generation model, jamming attack can be carried out to block devices from sending data successfully. This research will aid in the continual development of any necessary countermeasures and mitigations for LoRaWAN and LPWAN functionality of IoT networks in general

A trust model mechanism based on quality of service to reduce energy consumption in the internet of things network

The Internet of Things (IoT) is a network of connected devices that have emerged as a promising technology to handle small network-based devices. In recent years, adoption of this relatively new technology has grown immensely. The energy consumption for IoT devices is regarded as one of the most critical factors affecting IoT networks’ lifespan. Quality of Service (QoS) is considered one of the leading research concerns in IoT networks. Communication between IoT devices needs a suitable and reliable service model to meet the requirements of IoT applications to handle the levels of QoS and maximize network lifespans. Therefore, this study aims to propose a trust model mechanism to provide different levels of QoS (QoST-IoT) and maximize IoT network lifespans. The QoS trust model includes four main steps. The first step is trust level calculation, which is calculated for each of the IoT nodes to find the trust level. Then, in the second step, query trust, the IoT node sends the trust values of various components to the cluster head (CH). The third step involves the clustering of the IoT nodes. Subsequently, the fourth step deals with the trust level update. The experiments conducted in this study revealed that the proposed QoS trust model mechanism (QoST-IoT) reduced the energy consumption compared to the trust model mechanisms previously proposed in the literature. The results of the first simulation round showed that the QoST-IoT outperformed the security & trusted devices in the context of IoT (STD-IoT) by 41.2%, trust-based adaptive security in IoT (TAS-IoT) by 43.7%, and the context-aware and multiservice approach in IoT (Context-IoT) by 45.2%. In addition, the second simulation round showed that the QoST-IoT consumed less energy than STD-IoT by 47.5%, TAS-IoT by 50.5%, and Context-IoT by 53.8%. The findings of this study extend the understanding of designing a QoS trust model with energy consumption reduction for IoT networks, which could be beneficial for researchers, IoT developers, and policymakers

Enhancing Cyber Security through Machine Learning-Based Anomaly Detection in IoT Networks

The rapid proliferation of IOT (Internet of Things) networks has brought transformative benefits to industries and everyday life. However, it has also introduced unprecedented cyber security challenges, necessitating advanced techniques for anomaly detection. This research focuses on enhancing cyber security through the application of machine learning-based anomaly detection methods, specifically One-Class Support Vector Machine (SVM) and Isolation Forest, in the context of IOT networks. While Isolation Forest effectively isolates anomalies by building isolation trees, One-Class SVM models the normal data distribution, effectively separating anomalies. To provide a strong security framework for IoT networks, we suggest a comprehensive strategy that combines both algorithms. Our method enables the detection of anomalies in real-time IOT data streams, facilitating prompt responses to new threats. Data collection, preprocessing, and model training are key components. This study helps protect IOT ecosystems and maintain data integrity and privacy in an increasingly connected world by utilizing the benefits of One-Class SVM and Isolation Forest

Security techniques for sensor systems and the Internet of Things

Sensor systems are becoming pervasive in many domains, and are recently being generalized by the Internet of Things (IoT). This wide deployment, however, presents significant security issues. We develop security techniques for sensor systems and IoT, addressing all security management phases. Prior to deployment, the nodes need to be hardened. We develop nesCheck, a novel approach that combines static analysis and dynamic checking to efficiently enforce memory safety on TinyOS applications. As security guarantees come at a cost, determining which resources to protect becomes important. Our solution, OptAll, leverages game-theoretic techniques to determine the optimal allocation of security resources in IoT networks, taking into account fixed and variable costs, criticality of different portions of the network, and risk metrics related to a specified security goal. Monitoring IoT devices and sensors during operation is necessary to detect incidents. We design Kalis, a knowledge-driven intrusion detection technique for IoT that does not target a single protocol or application, and adapts the detection strategy to the network features. As the scale of IoT makes the devices good targets for botnets, we design Heimdall, a whitelist-based anomaly detection technique for detecting and protecting against IoT-based denial of service attacks. Once our monitoring tools detect an attack, determining its actual cause is crucial to an effective reaction. We design a fine-grained analysis tool for sensor networks that leverages resident packet parameters to determine whether a packet loss attack is node- or link-related and, in the second case, locate the attack source. Moreover, we design a statistical model for determining optimal system thresholds by exploiting packet parameters variances. With our techniques\u27 diagnosis information, we develop Kinesis, a security incident response system for sensor networks designed to recover from attacks without significant interruption, dynamically selecting response actions while being lightweight in communication and energy overhead

IoT Threat Detection Testbed Using Generative Adversarial Networks

The Internet of Things(IoT) paradigm provides persistent sensing and data collection capabilities and is becoming increasingly prevalent across many market sectors. However, most IoT devices emphasize usability and function over security, making them very vulnerable to malicious exploits. This concern is evidenced by the increased use of compromised IoT devices in large scale bot networks (botnets) to launch distributed denial of service(DDoS) attacks against high value targets. Unsecured IoT systems can also provide entry points to private networks, allowing adversaries relatively easy access to valuable resources and services. Indeed, these evolving IoT threat vectors (ranging from brute force attacks to remote code execution exploits) are posing key challenges. Moreover, many traditional security mechanisms are not amenable for deployment on smaller resource-constrained IoT platforms. As a result, researchers have been developing a range of methods for IoT security, with many strategies using advanced machine learning(ML) techniques. Along these lines, this paper presents a novel generative adversarial network(GAN) solution to detect threats from malicious IoT devices both inside and outside a network. This model is trained using both benign IoT traffic and global darknet data and further evaluated in a testbed with real IoT devices and malware threats.Comment: 8 pages, 5 figure

Security for the Industrial IoT: The Case for Information-Centric Networking

Industrial production plants traditionally include sensors for monitoring or documenting processes, and actuators for enabling corrective actions in cases of misconfigurations, failures, or dangerous events. With the advent of the IoT, embedded controllers link these `things' to local networks that often are of low power wireless kind, and are interconnected via gateways to some cloud from the global Internet. Inter-networked sensors and actuators in the industrial IoT form a critical subsystem while frequently operating under harsh conditions. It is currently under debate how to approach inter-networking of critical industrial components in a safe and secure manner. In this paper, we analyze the potentials of ICN for providing a secure and robust networking solution for constrained controllers in industrial safety systems. We showcase hazardous gas sensing in widespread industrial environments, such as refineries, and compare with IP-based approaches such as CoAP and MQTT. Our findings indicate that the content-centric security model, as well as enhanced DoS resistance are important arguments for deploying Information Centric Networking in a safety-critical industrial IoT. Evaluation of the crypto efforts on the RIOT operating system for content security reveal its feasibility for common deployment scenarios.Comment: To be published at IEEE WF-IoT 201

IoT-based Secure Data Transmission Prediction using Deep Learning Model in Cloud Computing

The security of Internet of Things (IoT) networks has become highly significant due to the growing number of IoT devices and the rise in data transfer across cloud networks. Here, we propose Generative Adversarial Networks (GANs) method for predicting secure data transmission in IoT-based systems using cloud computing. We evaluated our model’s attainment on the UNSW-NB15 dataset and contrasted it with other machine-learning (ML) methods, comprising decision trees (DT), random forests, and support vector machines (SVM). The outcomes demonstrate that our suggested GANs model performed better than expected in terms of precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The GANs model generates a 98.07% accuracy rate for the testing dataset with a precision score of 98.45%, a recall score of 98.19%, an F1 score of 98.32%, and an AUC-ROC value of 0.998. These outcomes show how well our suggested GANs model predicts secure data transmission in cloud-based IoT-based systems, which is a crucial step in guaranteeing the confidentiality of IoT networks

Intelligent Control and Security of Fog Resources in Healthcare Systems via a Cognitive Fog Model

There have been significant advances in the field of Internet of Things (IoT) recently, which have not always considered security or data security concerns: A high degree of security is required when considering the sharing of medical data over networks. In most IoT-based systems, especially those within smart-homes and smart-cities, there is a bridging point (fog computing) between a sensor network and the Internet which often just performs basic functions such as translating between the protocols used in the Internet and sensor networks, as well as small amounts of data processing. The fog nodes can have useful knowledge and potential for constructive security and control over both the sensor network and the data transmitted over the Internet. Smart healthcare services utilise such networks of IoT systems. It is therefore vital that medical data emanating from IoT systems is highly secure, to prevent fraudulent use, whilst maintaining quality of service providing assured, verified and complete data. In this paper, we examine the development of a Cognitive Fog (CF) model, for secure, smart healthcare services, that is able to make decisions such as opting-in and opting-out from running processes and invoking new processes when required, and providing security for the operational processes within the fog system. Overall, the proposed ensemble security model performed better in terms of Accuracy Rate, Detection Rate, and a lower False Positive Rate (standard intrusion detection measurements) than three base classifiers (K-NN, DBSCAN and DT) using a standard security dataset (NSL-KDD)

Rapid IoT device identification at the edge

Consumer Internet of Things (IoT) devices are increasingly common in everyday homes, from smart speakers to security cameras. Along with their benefits come potential privacy and security threats. To limit these threats we must implement solutions to filter IoT traffic at the edge. To this end the identification of the IoT device is the first natural step. In this paper we demonstrate a novel method of rapid IoT device identification that uses neural networks trained on device DNS traffic that can be captured from a DNS server on the local network. The method identifies devices by fitting a model to the first seconds of DNS second-level-domain traffic following their first connection. Since security and privacy threat detection often operate at a device specific level, rapid identification allows these strategies to be implemented immediately. Through a total of 51,000 rigorous automated experiments, we classify 30 consumer IoT devices from 27 different manufacturers with 82% and 93% accuracy for product type and device manufacturers respectively