Internet of Things: Security vulnerabilities and challenges

Andrea, Ioannis; Chrysostomou, Chrysostomos; Hadjichristofi, George

doi:10.1109/iscc.2015.7405513

Cited by 380 publications

(203 citation statements)

References 29 publications

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“…DOS attacks either disable the IoT device itself or use a compromised IoT device in order to launch other DoS attacks. 34 The first approach aims to overwhelm the lightweight communication channel that is utilized. The second approach takes advantage of weak security and maintenance (eg, default authorization credentials set by the vendor of the IoT device are not changed or the owner of the IoT device fails to regularly update the device's firmware and software), which often exists in many of these devices.…”

Section: Common Attacks On Iot Devicesmentioning

confidence: 99%

Securing Internet of Things (IoT) with machine learning

Zeadally

Tsikerdekis

2019

Int J Communication

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Advances in hardware, software, communication, embedding computing technologies along with their decreasing costs and increasing performance have led to the emergence of the Internet of Things (IoT) paradigm. Today, several billions of Internet-connected devices are part of the IoT ecosystem.IoT devices have become an integral part of the information and communication technology (ICT) infrastructure that supports many of our daily activities. The security of these IoT devices has been receiving a lot of attention in recent years. Another major recent trend is the amount of data that is being produced every day which has reignited interest in technologies such as machine learning and artificial intelligence. We investigate the potential of machine learning techniques in enhancing the security of IoT devices. We focus on the deployment of supervised, unsupervised learning techniques, and reinforcement learning for both host-based and network-based security solutions in the IoT environment. Finally, we discuss some of the challenges of machine learning techniques that need to be addressed in order to effectively implement and deploy them so that they can better protect IoT devices. KEYWORDSattack, internet of things, machine learning, security

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Section: Common Attacks On Iot Devicesmentioning

confidence: 99%

Securing Internet of Things (IoT) with machine learning

Zeadally

Tsikerdekis

2019

Int J Communication

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“…Andrea et al 15 have presented a classification of attacks in IoT based on four layers of IoT, ie, the perception or physical, network, software, and encryption attacks. Andrea et al 15 have presented a classification of attacks in IoT based on four layers of IoT, ie, the perception or physical, network, software, and encryption attacks.…”

Section: Perception Layermentioning

confidence: 99%

An opportunistic resource management model to overcome resource‐constraint in the Internet of Things

Safa

Maple

Haghparast

et al. 2018

Concurrency and Computation

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Experts believe that the Internet of Things (IoT) is a new revolution in technology and has brought many advantages for our society. However, there are serious challenges in terms of information security and privacy protection. Smart objects usually do not have malware detection due to resource limitations and their intrusion detection work on a particular network. Low computation power, low bandwidth, low battery, storage, and memory contribute to a resource-constrained effect on information security and privacy protection in the domain of IoT. The capacity of fog and cloud computing such as efficient computing, data access, network and storage, supporting mobility, location awareness, heterogeneity, scalability, and low latency in secure communication positively influence information security and privacy protection in IoT. This study illustrates the positive effect of fog and cloud computing on the security of IoT systems and presents a decision-making model based on the object's characteristics such as computational power, storage, memory, energy consumption, bandwidth, packet delivery, hop-count, etc. This helps an IoT system choose the best nodes for creating the fog that we need in the IoT system. Our experiment shows that the proposed approach has less computational, communicational cost, and more productivity in compare with the situation that we choose the smart objects randomly to create a fog.KEYWORDS cloud, Fog, information security, Internet of Things, privacy, resource INTRODUCTIONThe Internet has provided a backbone for connecting different objects to each other and has changed human life significantly. The ubiquitous things is a new concept that has been created by the interconnection and intercommunication amongst smart objects. 1 We are faced with a huge volume of data in this environment. Network traffic, increasing demands of real time, latency-sensitive applications, resource-constraints (computational power, storage, memory, etc) in actuators, mobility and geo-distribution of smart objects, and heterogeneity that are new challenges in the domain of IoT. 2 These weaknesses directly or indirectly influence information security and privacy violation in IoT systems. 3Resource-constraint in actuators jeopardises secure communication between different smart objects. 4 Messages need to be sent encrypted;they will be decrypted for process in the target. The result of the process should be encrypted and send to the same object or other smart objects.We have latency; this delays not only creates risk for information security, but it may also impact human safety in some applications of IoT, such as smart vehicles. Lightweight cryptography has been presented to overcome this challenge in vehicle-to-vehicle (V2V) communication and in many other secure communications in IoT. 5 Smart objects usually do not have malware detection due to limitation in resources; their intrusion detection only can detect attacks in particular networks (not hybrid). These are examples of common problems in the IoT domain.Cloud...

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“…Andrea et al [13] think of another characterization of IoT gadgets assaults exhibited in four particular sorts: 1) physical; 2) arrange; 3) programming; and 4) encryption assaults. Every one covers a layer of the IoT structure (physical, arrange, and application), notwithstanding the IoT conventions for information encryption.…”

Section: Classifications On Iot Attacksmentioning

confidence: 99%

Survey on Protection and Privacy Challenges in Smart Objects

2018

IJRTER

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Abstract-Internet-of-Things (IoT) are wherever in our day by day life. They are utilized as a part of our homes, in healing facilities, conveyed outside to control and report the adjustments in condition, forestall fires, what's more, numerous more useful usefulness. Be that as it may, every one of those advantages can happen to colossal dangers of protection misfortune and security issues. To secure the IoT gadgets, many research works have been led to countermeasure those issues and locate a superior way to wipe out those dangers, or if nothing else limit their consequences for the client's protection and security necessities. The review comprises of four fragments. The primary portion will investigate the most important constraints of IoT gadgets and their answers. The second one will introduce the characterization of IoT assaults. The following fragment will concentrate on the systems and designs for verification what's more, get to control. The last fragment will dissect the security issues in various layers.

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Internet of Things: Security vulnerabilities and challenges

Cited by 380 publications

References 29 publications

Securing Internet of Things (IoT) with machine learning

Securing Internet of Things (IoT) with machine learning

An opportunistic resource management model to overcome resource‐constraint in the Internet of Things

Survey on Protection and Privacy Challenges in Smart Objects

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