Intelligent Resilience in the IoT (original) (raw)

Mitigating Attacks in the Internet of Things with a Self-protecting Architecture

Internet of Things (IoT) applications run in environments that have resource constraints and are unsecured. Due to the nature of their environment, IoT systems should be able to reason autonomously and take self-protecting decisions. Currently, an adequate architecture to incorporate self-protection in the IoT is not available. Thus, we design a new self-protecting architecture based on the MAPE-K (Monitor, Analyze, Plan, Execute and Knowledge) autonomic control loop that will run at the application layer so that developers can add several security services. In this paper, we address the impact caused by attacks (SinkHole, Selective Forward, Black Hole and Flooding) in relation to power consumption and interference in the operation of the network created from the Routing Protocol for Low Power and Lossy Networks (RPL) routing protocol.

IANVS: A Moving Target Defense Framework for a Resilient Internet of Things

2020 IEEE Symposium on Computers and Communications (ISCC), 2020

The Internet of Things (IoT) is more and more present in fundamental aspects of our societies and personal life. Billions of objects now have access to the Internet. This networking capability allows for new beneficial services and applications. However, it is also the entry-point for a wide variety of cyber-attacks that target these devices. The security measures present in real IoT systems lag behind those of the standard Internet. Security is sometimes completely absent. Moving Target Defense (MTD) is a 10-year-old cyber-defense paradigm. It proposes to randomize components of a system. Reasonably, an attacker will have a higher cost attacking an MTD-version of a system compared with a static-version of it. Even if MTD has been successfully applied to standard systems, its deployment for IoT is still lacking. In this paper, we propose a generic MTD framework suitable for IoT systems: IANVS (pronounced Janus). Our framework has a modular design. Its components can be adapted accor...

Vulnerabilities, Attacks and their Mitigation: An Implementation on Internet of Things (IoT)

VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE, 2019

IoT is much-hyped technology in today’s world still it helps us to achieve the goals of ubiquitous computing. Although, there are many challenges in adoption and implementation of IoT based solutions. One of the major challenges is security of IoT products and services. Day after day exposures like Insecure Firmware, Data Protection, Identity Thefts and DoS/DDoS Attack in the field of IoT are being oppressed with malicious objectives so we need to focus on these security issues. A framework of IoT may use many services on a single platform to give a specific applicability to the applications. These services can be sensor fields, cloud computing and data analytics so the security architecture should ensure its measures on each level such as physical access, remote access and secured data access. This paper presents the study on existing attacks and mitigation in IoT Services which enables for finding and patching security vulnerabilities. With the help of machine learning and data an...

On Resilience of IoT Systems

Ubiquity, 2016

At the very high level of abstraction, the Internet of Things (IoT) can be modeled as the hyper-scale, hyper-complex cyber-physical system. Study of resilience of IoT systems is the first step towards engineering of the future IoT eco-systems. Exploration of this domain is highly promising avenue for many aspiring Ph.D. and M.Sc. students.

Artificial Intelligence-Based Security Protocols to Resist Attacks in Internet of Things

Wireless Communications and Mobile Computing, 2022

IoT (Internet of Things) usage in industrial and scientific domains is progressively increasing. Currently, IoTs are utilized in numerous applications in different domains, similar to communication technology, environmental monitoring, agriculture, medical services, and manufacturing purposes. But, the IoT systems are vulnerable against various intrusions and attacks in the perspective on the security view. It is essential to create an intrusion detection model to detect and secure the network from different attacks and anomalies that continually happen in the network. In this paper, the anomaly detection model for an IoT network using deep neural networks (DNN) with chicken swarm optimization (CSO) algorithm was proposed. Presently, the DNN has demonstrated its efficiency in different fields that are applicable to its usage. Deep learning is the type of algorithm based on machine learning which used many layers to gradually extricate more significant features of level from the raw ...

Using Redundancy to Detect Security Anomalies: Towards IoT security attack detectors

Ubiquity, 2016

Cyber-attacks and breaches are often detected too late to avoid damage. While "classical" reactive cyber defenses usually work only if we have some prior knowledge about the attack methods and "allowable" patterns, properly constructed redundancy-based anomaly detectors can be more robust and often able to detect even zero day attacks. They are a step toward an oracle that uses knowable behavior of a healthy system to identify abnormalities. In the world of Internet of Things (IoT), security, and anomalous behavior of sensors and other IoT components, will be orders of magnitude more difficult unless we make those elements security aware from the start. In this article we examine the ability of redundancy-based anomaly detectors to recognize some high-risk and difficult to detect attacks on web servers-a likely management interface for many IoT stand-alone elements. In real life, it has taken long, a number of years in some cases, to identify some of the vulnerabilities and related attacks. We discuss practical relevance of the approach in the context of providing high-assurance Webservices that may belong to autonomous IoT applications and devices. Ubiquity Symposium Ubiquity, an ACM publication January 2016 http://ubiquity.acm.org 2

Toward a Secure IoT Architecture

Studies in Systems, Decision and Control, 2019

The design of a cyber-secure, Internet of Things (IoT), supply chain risk management architecture is proposed. The purpose of the architecture is to reduce vulnerabilities of malicious supply chain risks by applying machine learning (ML), cryptographic hardware monitoring (CHM), and distributed network coordination (DNC) techniques to guard against unforeseen hardware component failures and malicious attacks. These crosscutting technologies are combined into an Instrumentation-and-Control/Operator-in-the-Loop (ICOL) architecture that learns normal and abnormal system behaviors. In the event that the ICOL detects possible abnormal system-component behaviors, an ICOL network alert is triggered that requires an operator verification-response action before any control parameters can affect the operation of the system. The operator verification-response is fed back into the ML systems to recalibrate the classification of normal and abnormal states of the system. As a consequence, this pr...

An Architecture for Self-protection in Internet of Things

2016

To make the Internet of Things a more receptive environment and well regarded by everyone, it is important to invest in security. The devices involved in the Internet of things environment have limited computational resources and expose the network to many threats. With the use of an architecture to be able to detect, classify and mitigate the effects of these threats, it is possible to create a safer environment. This paper proposes a security architecture for the Internet of Things, considering the limited computational resources of the environment in question. This Architecture uses the Dendritic Cells Algorithm (DCA) combined with a neural network to detect attacks and use the White List to ignore the malicious nodes in the network. Keywords–Internet of Things; Autonomic Computing; SelfProtection.

Preemptive Anomaly Prediction in IoT Components (short paper)

2021

The Internet-of-Things (IoT) has become a very promising and fruitful area of research. The rapid development of IoT is revolutionizing our daily utilization of technology in every way. The IoT paradigm is that the devices making up an IoT system have resource constraints such as storage, computing and energy consumption. That paradigm makes possible a flexible and pervasive communication between devices that are bound to low resources. These constraints may create a state where there is anomaly occurrence on the component level that may impact the whole system. Some innovative techniques have been proposed to quantify the reliability of these devices for the aforementioned constraints. However, there is a gap between the quantification of the component reliability and the predictive and preemptive maintenance of these components. In this study, we propose an approach combining reliability quantification and reinforcement learning to build a mechanism that can achieve a predictive m...

An Anomaly Mitigation Framework for IoT Using Fog Computing

Electronics

The advancement in IoT has prompted its application in areas such as smart homes, smart cities, etc., and this has aided its exponential growth. However, alongside this development, IoT networks are experiencing a rise in security challenges such as botnet attacks, which often appear as network anomalies. Similarly, providing security solutions has been challenging due to the low resources that characterize the devices in IoT networks. To overcome these challenges, the fog computing paradigm has provided an enabling environment that offers additional resources for deploying security solutions such as anomaly mitigation schemes. In this paper, we propose a hybrid anomaly mitigation framework for IoT using fog computing to ensure faster and accurate anomaly detection. The framework employs signature- and anomaly-based detection methodologies for its two modules, respectively. The signature-based module utilizes a database of attack sources (blacklisted IP addresses) to ensure faster d...