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

Comprehensive analysis of security issues in cloud-based Internet of Things: A survey

The Internet of Things (IoT )has emerged as the largest computing platform, enabling IoT devices to sense real-world conditions such as temperature, humidity, pressure, and cloud prediction. However, the security of IoT systems is crucial due to their direct impact on human life. With the expansion of processing and communication capabilities to numerous devices, IoT has become a vast network where connectivity is ubiquitous. This paper focuses on the security issues of cloud-based IoT, specifically access control, network security, data security, and privacy, which are the four main components of cloud-based IoT. By analyzing and comparing existing research papers on security in cloud IoT and IoT in general, we identify proposed solutions. Most researchers have concentrated on a single component, while only a few have addressed two components. Consequently, our research aims to bridge the gap in Cloud IoT security by focusing on more than two components. We propose the utilization of methods such as Machine Learning and blockchain to enhance security, drawing on the strengths highlighted in previous works. Our future focus will involve exploring potential attacks in cloud IoT and developing a comprehensive method that encompasses atleast three security components of cloud IoT security

A flow-based intrusion detection framework for internet of things networks

The application of the Internet of Things concept in domains such as industrial control, building automation, human health, and environmental monitoring, introduces new privacy and security challenges. Consequently, traditional implementation of monitoring and security mechanisms cannot always be presently feasible and adequate due to the number of IoT devices, their heterogeneity and the typical limitations of their technical specifications. In this paper, we propose an IP flow-based Intrusion Detection System (IDS) framework to monitor and protect IoT networks from external and internal threats in real-time. The proposed framework collects IP flows from an IoT network and analyses them in order to monitor and detect attacks, intrusions, and other types of anomalies at different IoT architecture layers based on some flow features instead of using packet headers fields and their payload. The proposed framework was designed to consider both the IoT network architecture and other IoT contextual characteristics such as scalability, heterogeneity, interoperability, and the minimization of the use of IoT networks resources. The proposed IDS framework is network-based and relies on a hybrid architecture, as it involves both centralized analysis and distributed data collection components. In terms of detection method, the framework uses a specification-based approach drawn on normal traffic specifications. The experimental results show that this framework can achieve & 100% success and 0% of false positives in detection of intrusions and anomalies. In terms of performance and scalability in the operation of the IDS components, we study and compare it with three different conventional IDS (Snort, Suricata, and Zeek) and the results demonstrate that the proposed solution can consume fewer computational resources (CPU, RAM, and persistent memory) when compared to those conventional IDS.This work was supported by Portuguese national funds through the FCT—Foundation for Science and Technology, I.P., under the project UID/CEC/04524/2019info:eu-repo/semantics/publishedVersio

A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT

Internet of Things (IoT) forms the foundation of next generation infrastructures, enabling development of future cities that are inherently sustainable. Intrusion detection for such paradigms is a non-trivial challenge which has attracted further significance due to extraordinary growth in the volume and variety of security threats for such systems. However, due to unique characteristics of such systems i.e., battery power, bandwidth and processor overheads and network dynamics, intrusion detection for IoT is a challenge, which requires taking into account the trade-off between detection accuracy and performance overheads. In~this context, we are focused at highlighting this trade-off and its significance to achieve effective intrusion detection for IoT. Specifically, this paper presents a comprehensive study of existing intrusion detection systems for IoT systems in three aspects: computational overhead, energy consumption and privacy implications. Through extensive study of existing intrusion detection approaches, we have identified open challenges to achieve effective intrusion detection for IoT infrastructures. These include resource constraints, attack complexity, experimentation rigor and unavailability of relevant security data. Further, this paper is envisaged to highlight contributions and limitations of the state-of-the-art within intrusion detection for IoT, and~aid the research community to advance it by identifying significant research directions

A Signature-based Intrusion Detection System for the Internet of Things

Internet of Things (IoT) is envisioned as a transformative approach with a wide range of applications in various sectors such as home automation, industrial control, and agriculture. It promises innovative business models and improved user experience. However, as evidenced by recent attacks such as the Mirai botnet, IoT networks and systems remain very vulnerable and require stronger protection mechanisms. Furthermore, due to processing, memory, and power constraints of typical IoT devices, traditional Internet security mechanisms are not always feasible or appropriate. In this work, we are concerned with designing an Intrusion Detection System (IDS) for protecting IoT networks from external threats as well as internal compromised devices. Our proposed design adopts a signature-based intrusion detection approach and involves both certralised and distributed IDS modules. Using the Cooja simulator, we have implemented a Denial of Service (DoS) attack scenario on IoT devices. This scenario exploits the RPL protocol, which is widely used for routing in low-power networks, including IoT networks. In particular, we have implemented two variants of DoS attacks, namely “Hello” flooding and version number modification. As shown by simulation results, these attacks may impact the reachability of certain IoT devices and their power consumption

A novel framework for collaborative intrusion detection for M2M networks

The proliferation of sensor devices has introduced exciting possibilities such as the Internet of Things (IoT). Machine to Machine (M2M) communication underpins efficient interactions within such infrastructures. The resource constraints and ad-hoc nature of these networks have significant implications for security in general and with respect to intrusion detection in particular. Consequently, contemporary solutions mandating a stable infrastructure are inadequate to fulfill these defining characteristics of M2M networks. In this paper, we present COLIDE (COLlaborative Intrusion Detection Engine) a novel framework for effective intrusion detection in the M2M networks without incurring high energy and communication cost on the participating host and edge nodes. The framework is envisioned to address challenges such as flexibility, resource constraints, and the collaborative nature of the M2M networks. The paper presents a detailed system description along with its formal and empirical evaluation using Contiki OS. Our evaluation for different communication scenarios demonstrates that the proposed approach has limited overhead in terms of energy utilization and memory consumption

Clust-IT:Clustering-Based Intrusion Detection in IoT Environments

Low-powered and resource-constrained devices are forming a greater part of our smart networks. For this reason, they have recently been the target of various cyber-attacks. However, these devices often cannot implement traditional intrusion detection systems (IDS), or they can not produce or store the audit trails needed for inspection. Therefore, it is often necessary to adapt existing IDS systems and malware detection approaches to cope with these constraints. We explore the application of unsupervised learning techniques, specifically clustering, to develop a novel IDS for networks composed of low-powered devices. We describe our solution, called Clust-IT (Clustering of IoT), to manage heterogeneous data collected from cooperative and distributed networks of connected devices and searching these data for indicators of compromise while remaining protocol agnostic. We outline a novel application of OPTICS to various available IoT datasets, composed of both packet and flow captures, to demonstrate the capabilities of the proposed techniques and evaluate their feasibility in developing an IoT IDS

Exploring placement of intrusion detection systems in rpl-based internet of things

Intrusion detection is an indispensable part of RPL security due to its nature opening to attacks from insider attackers. While there are a good deal of studies that analyze different types of attack and propose intrusion detection systems based on various techniques that are proposed in the literature, how to place such intrusion detection systems on RPL topology is not investigated. This is the main contribution of this study, and three intrusion detection architectures based on central and distributed placement of intrusion detection nodes are analyzed rigorously against different types of attacks and attackers at various locations in the RPL topology and evaluated from different aspects including their effectiveness, cost, and security.Comment: 15 pages, 5 figures, research articl