Cyber-Attacks on Internet of Things (IoT) Devices, Attack Vectors, and Remedies: A Position Paper

2021 ◽  
pp. 277-295
Author(s):  
Shubham Prajapati ◽  
Amit Singh
Keyword(s):  
Internet Of Things ◽  
Position Paper ◽  
Cyber Attacks ◽  
Iot Devices

scholarly journals State-of-the-Art Software-Based Remote Attestation: Opportunities and Open Issues for Internet of Things

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1598
Author(s):  
Sigurd Frej Joel Jørgensen Ankergård ◽  
Edlira Dushku ◽  
Nicola Dragoni
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Cyber Attacks ◽  
Resource Constrained ◽  
Remote Attestation ◽  
Comprehensive Overview ◽  
Research Issues ◽  
Advantages And Disadvantages ◽  
Iot Devices ◽  
Specialized Hardware

The Internet of Things (IoT) ecosystem comprises billions of heterogeneous Internet-connected devices which are revolutionizing many domains, such as healthcare, transportation, smart cities, to mention only a few. Along with the unprecedented new opportunities, the IoT revolution is creating an enormous attack surface for potential sophisticated cyber attacks. In this context, Remote Attestation (RA) has gained wide interest as an important security technique to remotely detect adversarial presence and assure the legitimate state of an IoT device. While many RA approaches proposed in the literature make different assumptions regarding the architecture of IoT devices and adversary capabilities, most typical RA schemes rely on minimal Root of Trust by leveraging hardware that guarantees code and memory isolation. However, the presence of a specialized hardware is not always a realistic assumption, for instance, in the context of legacy IoT devices and resource-constrained IoT devices. In this paper, we survey and analyze existing software-based RA schemes (i.e., RA schemes not relying on specialized hardware components) through the lens of IoT. In particular, we provide a comprehensive overview of their design characteristics and security capabilities, analyzing their advantages and disadvantages. Finally, we discuss the opportunities that these RA schemes bring in attesting legacy and resource-constrained IoT devices, along with open research issues.

Botnet and Internet of Things (IoTs)

2020 ◽  
pp. 304-316
Author(s):  
Kamal Alieyan ◽  
Ammar Almomani ◽  
Rosni Abdullah ◽  
Badr Almutairi ◽  
Mohammad Alauthman
Keyword(s):  
Internet Of Things ◽  
Cyber Attacks ◽  
The Internet ◽  
Ddos Attacks ◽  
Botnet Detection ◽  
The Public ◽  
Detection Techniques ◽  
Channel Information ◽  
Iot Devices ◽  
The Internet Of Things

In today's internet world the internet of things (IoT) is becoming the most significant and developing technology. The primary goal behind the IoT is enabling more secure existence along with the improvement of risks at various life levels. With the arrival of IoT botnets, the perspective towards IoT products has transformed from enhanced living enabler into the internet of vulnerabilities for cybercriminals. Of all the several types of malware, botnet is considered as really a serious risk that often happens in cybercrimes and cyber-attacks. Botnet performs some predefined jobs and that too in some automated fashion. These attacks mostly occur in situations like phishing against any critical targets. Files sharing channel information are moved to DDoS attacks. IoT botnets have subjected two distinct problems, firstly, on the public internet. Most of the IoT devices are easily accessible. Secondly, in the architecture of most of the IoT units, security is usually a reconsideration. This particular chapter discusses IoT, botnet in IoT, and various botnet detection techniques available in IoT.

Botnet and Internet of Things (IoTs)

2021 ◽  
pp. 138-150
Author(s):  
Kamal Alieyan ◽  
Ammar Almomani ◽  
Rosni Abdullah ◽  
Badr Almutairi ◽  
Mohammad Alauthman
Keyword(s):  
Internet Of Things ◽  
Cyber Attacks ◽  
The Internet ◽  
Ddos Attacks ◽  
Botnet Detection ◽  
The Public ◽  
Detection Techniques ◽  
Channel Information ◽  
Iot Devices ◽  
The Internet Of Things

In today's internet world the internet of things (IoT) is becoming the most significant and developing technology. The primary goal behind the IoT is enabling more secure existence along with the improvement of risks at various life levels. With the arrival of IoT botnets, the perspective towards IoT products has transformed from enhanced living enabler into the internet of vulnerabilities for cybercriminals. Of all the several types of malware, botnet is considered as really a serious risk that often happens in cybercrimes and cyber-attacks. Botnet performs some predefined jobs and that too in some automated fashion. These attacks mostly occur in situations like phishing against any critical targets. Files sharing channel information are moved to DDoS attacks. IoT botnets have subjected two distinct problems, firstly, on the public internet. Most of the IoT devices are easily accessible. Secondly, in the architecture of most of the IoT units, security is usually a reconsideration. This particular chapter discusses IoT, botnet in IoT, and various botnet detection techniques available in IoT.

Decades of Internet of Things towards 21st Century: A Research-Based Introspective

2021 ◽  
Author(s):  
NAGAJAYANTHI BOOBALAKRISHNAN
Keyword(s):  
Internet Of Things ◽  
Cyber Attacks ◽  
Embedded Internet ◽  
Industrial Iot ◽  
Voice User ◽  
Block Chain ◽  
Iot Devices ◽  
Data Ecosystem ◽  
The Internet Of Things ◽  
Exchange Data

Abstract Internet connects people to people, people to machine, and machine to machine for a life of serendipity through a Cloud. Internet of Things networks objects or people and integrates them with software to collect and exchange data. The Internet of things (IoT) influences our lives based on how we ruminate, respond, and anticipate. IoT 2020 heralds from the fringes to the data ecosystem and panaches a comfort zone. IoT is overwhelmingly embraced by businessmen and consumers due to increased productivity and convenience. Internet of Things facilitates intelligent device control with cloud vendors like Amazon and Google using artificial intelligence for data analytics, and with digital assistants like Alexa and Siri providing a voice user interface. Smart IoT is all about duplex connecting, processing, and implementing. With 5G, lightning faster rate of streaming analytics is realistic. An amalgamation of technologies has led to this techno-industrial IoT revolution. Centralized IoT architecture is vulnerable to cyber-attacks. With Block Chain, it is possible to maintain transparency and security of the transaction's data. Standardization of IoT devices is achievable with limited vendors based on Platform, Connectivity, and Application. Robotic Process Automation (RPA) using bots has automated laborious tasks in 2019. Embedded Internet using Facial Recognition could reduce the pandemic crisis. Security concerns are addressed with micro-segmentation approaches. IoT, an incredible vision of the future makes systems adaptive with customized features, responsive with increased efficiency, and procurable with optimized cost. This paper delivers a comprehensive insight into the technical perspectives of IoT, focusing on interoperability, flexibility, scalability, mobility, security, transparency, standardization, and low energy.

Survey of Machine Learning Algorithms to Detect Malware in Consumer Internet of Things Devices

2021 ◽  
Vol 30 (04) ◽  
pp. 2150020
Author(s):  
Luke Holbrook ◽  
Miltiadis Alamaniotis
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Internet Of Things ◽  
Deep Neural Network ◽  
Learning Algorithms ◽  
Cyber Attacks ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Iot Devices

With the increase of cyber-attacks on millions of Internet of Things (IoT) devices, the poor network security measures on those devices are the main source of the problem. This article aims to study a number of these machine learning algorithms available for their effectiveness in detecting malware in consumer internet of things devices. In particular, the Support Vector Machines (SVM), Random Forest, and Deep Neural Network (DNN) algorithms are utilized for a benchmark with a set of test data and compared as tools in safeguarding the deployment for IoT security. Test results on a set of 4 IoT devices exhibited that all three tested algorithms presented here detect the network anomalies with high accuracy. However, the deep neural network provides the highest coefficient of determination R2, and hence, it is identified as the most precise among the tested algorithms concerning the security of IoT devices based on the data sets we have undertaken.

scholarly journals A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1177
Author(s):  
Javed Asharf ◽  
Nour Moustafa ◽  
Hasnat Khurshid ◽  
Essam Debie ◽  
Waqas Haider ◽  
...  
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Cyber Attacks ◽  
The Internet ◽  
Cyber Attack ◽  
Control Approach ◽  
Detection Systems ◽  
Desktop Computers ◽  
Iot Devices

The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

scholarly journals GRADIENT BOOSTING ALGORITHM FOR EARLY DETECTION OF UNKNOWN INTERNET OF THINGS DEVICES

2021 ◽  
Vol 25 (Special) ◽  
pp. 1-115-1-126
Author(s):  
Vian A. Ferman ◽  
◽  
Mohammed A. Tawfeeq ◽  
Keyword(s):  
Internet Of Things ◽  
Early Detection ◽  
Cyber Attacks ◽  
Identification Accuracy ◽  
Gradient Boosting ◽  
Network Protection ◽  
Boosting Algorithm ◽  
Iot Devices ◽  
Setup Phase ◽  
The Internet Of Things

The pervasive availability of the Internet of Things (IoT) markets lures targets for cyber-attacks since most manufactured IoT devices are usually resource-constrained devices. The first powerful line of IoT network protection from these vulnerabilities is detecting IoT devices especially the unauthorized ones by utilizing machine learning (ML) algorithms. Actually, it is so difficult or even impossible to find individual unknown IoT devices during the setup phase but, knowing their manufacturers is a matter to be deliberate. In this paper, a new method based fingerprints generation is introduced to detect the connected devices in the setup phase. Fingerprints for 21 different IoT devices are generated using devices’ network traffic. The whole produced fingerprints of devices are divided into four groups according to their manufacturers or fingerprints similarity proportion. Gradient Boosting Algorithm is applied to achieve the identified purposes. The proposed method is considered as a preparatory study for early detection of unauthorized. The performance evaluation for the proposed method was calculated based on two metrics: Identification accuracy and F1-score. The average identification accuracy rate was around 98.65%, while the average F1-score was about 99%.

IoT and Cyber Security

2020 ◽  
pp. 203-235
Author(s):  
Keyurbhai Arvindbhai Jani ◽  
Nirbhay Chaubey
Keyword(s):  
Internet Of Things ◽  
Cyber Security ◽  
Cyber Attacks ◽  
The Internet ◽  
Smart Objects ◽  
The Third ◽  
Different Types ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) connects different IoT smart objects around people to make their life easier by connecting them with the internet, which leads IoT environments vulnerable to many attacks. This chapter has few main objectives: to understand basics of IoT; different types of attacks possible in IoT; and prevention steps to secure IoT environment at some extent. Therefore, this chapter is mainly divided into three parts. In first part discusses IoT devices and application of it; the second part is about cyber-attacks possible on IoT environments; and in the third part is discussed prevention and recommendation steps to avoid damage from different attacks.

Security in Application Layer Protocols of IoT

2020 ◽  
pp. 46-60
Author(s):  
Jasmine Norman ◽  
Paul Joseph
Keyword(s):  
Internet Of Things ◽  
Electronic Devices ◽  
Cyber Attacks ◽  
Application Layer ◽  
Communication Medium ◽  
Security Issues ◽  
Digital World ◽  
Iot Devices

IoT is an acronym for Internet of Things. It is the revolutionary area that transforms the digital world into a device world. IoT helps in not only fulfilling human requirements, but also they act as a communication medium between humans and electronic devices. The birth of IoT started in early 2000s, but since then, it is an amazing fact that now at least 65% of devices are connected with IoT technology with the term “smart” in their prefix and it would be up by 30% at the end of 2016 (Gartner Survey, 2015). Since then, many security issues were raised, and have been risen all these years due to the flaws in that devices. This made attackers to take advantage over that devices and started controlling them. This chapter studies IoT application layer protocols, services offered and gives an idea of existing cyber attacks and threat. In addition, the authors give the possible attacks on the IoT devices, in particular at application layer, and give the necessary precautions to overcome the cyber attacks both for consumers and vendors.

scholarly journals Improving Internet of Things (IoT) Security with Software-Defined Networking (SDN)

Computers ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 8 ◽  
Author(s):  
Abdullah Al Hayajneh ◽  
Md Zakirul Alam Bhuiyan ◽  
Ian McAndrew
Keyword(s):  
Internet Of Things ◽  
Computer Network ◽  
Software Defined Networking ◽  
Cyber Attacks ◽  
System Model ◽  
Raspberry Pi ◽  
Media Center ◽  
Area Of Interest ◽  
Man In The Middle ◽  
Iot Devices

There has been an increase in the usage of Internet of Things (IoT), which has recently become a rising area of interest as it is being extensively used for numerous applications and devices such as wireless sensors, medical devices, sensitive home sensors, and other related IoT devices. Due to the demand to rapidly release new IoT products in the market, security aspects are often overlooked as it takes time to investigate all the possible vulnerabilities. Since IoT devices are internet-based and include sensitive and confidential information, security concerns have been raised and several researchers are exploring methods to improve the security among these types of devices. Software defined networking (SDN) is a promising computer network technology which introduces a central program named ‘SDN Controller’ that allows overall control of the network. Hence, using SDN is an obvious solution to improve IoT networking performance and overcome shortcomings that currently exist. In this paper, we (i) present a system model to effectively use SDN with IoT networks; (ii) present a solution for mitigating man-in-the-middle attacks against IoT that can only use HTTP, which is a critical attack that is hard to defend; and (iii) implement the proposed system model using Raspberry Pi, Kodi Media Center, and Openflow Protocol. Our system implementation and evaluations show that the proposed technique is more resilient to cyber-attacks.

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