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

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%.

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Botnet and Internet of Things (IoTs)

Security, Privacy, and Forensics Issues in Big Data - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-9742-1.ch013 ◽

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.

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Botnet and Internet of Things (IoTs)

Research Anthology on Combating Denial-of-Service Attacks ◽

10.4018/978-1-7998-5348-0.ch007 ◽

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

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

10.21203/rs.3.rs-193005/v1 ◽

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.

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IoT and Cyber Security

Quantum Cryptography and the Future of Cyber Security - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-2253-0.ch010 ◽

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.

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Terrorism and the Internet of Things: Cyber-Terrorism as an Emergent Threat

Advanced Sciences and Technologies for Security Applications - Counter-Terrorism, Ethics and Technology ◽

10.1007/978-3-030-90221-6_5 ◽

2021 ◽

pp. 71-87

Author(s):

Adam Henschke

Keyword(s):

Internet Of Things ◽

Causal Link ◽

Cyber Attacks ◽

The Internet ◽

Cyber Terrorism ◽

Ethical Implications ◽

Five Factors ◽

The World ◽

Iot Devices ◽

The Internet Of Things

AbstractIn this chapter I present an argument that cyber-terrorism will happen. This argument is premised on the development of a cluster of related technologies that create a direct causal link between the informational realm of cyberspace and the physical realm. These cyber-enabled physical systems fit under the umbrella of the ‘Internet of Things’ (IoT). While this informational/physical connection is a vitally important part of the claim, a more nuanced analysis reveals five further features are central to the IoT enabling cyber-terrorism. These features are that the IoT is radically insecure, that the components of the IoT are in the world, that the sheer numbers of IoT devices mean potential attacks can be intense, that the IoT will likely be powered by a range of Artificial Intelligence aspects, making it inscrutable, and that the IoT is largely invisible. Combining these five factors together, the IoT emerges as a threat vector for cyber-terrorism. The point of the chapter is to go beyond recognising that the IoT is a thing in the world and so can enable physical impacts from cyber-attacks, to offer these five factors to say something more specific about just why the IoT can potentially be used for cyber-terrorism. Having outlined how the IoT can be used for cyber-terrorism, I attend to the question of whether such actions are actually terrorism or not. Ultimately, I argue, as the IoT grows in scope and penetration of our physical worlds and behaviours, it means that cyber-terrorism is not a question of if, but when. This, I suggest, has significant ethical implications as these five features of the IoT mean that we ought to be regulating these technologies.

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Botnet Attack Detection by Using CNN-LSTM Model for Internet of Things Applications

Security and Communication Networks ◽

10.1155/2021/3806459 ◽

2021 ◽

Vol 2021 ◽

pp. 1-23

Author(s):

Hasan Alkahtani ◽

Theyazn H. H. Aldhyani

Keyword(s):

Internet Of Things ◽

Short Term Memory ◽

Attack Detection ◽

Cyber Attacks ◽

Short Term ◽

Security Risks ◽

Optimal Accuracy ◽

Long Short Term Memory ◽

Iot Devices ◽

The Internet Of Things

The Internet of Things (IoT) has grown rapidly, and nowadays, it is exploited by cyber attacks on IoT devices. An accurate system to identify malicious attacks on the IoT environment has become very important for minimizing security risks on IoT devices. Botnet attacks are among the most serious and widespread attacks, and they threaten IoT devices. Motionless IoT devices have a security weakness due to lack of sufficient memory and computation results for a security platform. In addition, numerous existing systems present themselves for finding unknown patterns from IoT networks to improve security. In this study, hybrid deep learning, a convolutional neural network and long short-term memory (CNN-LSTM) algorithm, was proposed to detect botnet attacks, namely, BASHLITE and Mirai, on nine commercial IoT devices. Extensive empirical research was performed by employing a real N-BaIoT dataset extracted from a real system, including benign and malicious patterns. The experimental results exposed the superiority of the CNN-LSTM model with accuracies of 90.88% and 88.61% in detecting botnet attacks from doorbells (Danminin and Ennio brands), whereas the proposed system achieved good accuracy (88.53%) in identifying botnet attacks from thermostat devices. The accuracies of the proposed system in detecting botnet attacks from security cameras were 87.19%, 89.23%, 87.76%, and 89.64%, with respect to accuracy metrics. Overall, the CNN-LSTM model was successful in detecting botnet attacks from various IoT devices with optimal accuracy.

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Robust Intelligent Malware Detection using Light GBM Algorithm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.f4043.049620 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1253-1263

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Algorithm ◽

Gradient Boosting ◽

Machine Learning Algorithm ◽

Suggested Approach ◽

Light Gradient ◽

Boosting Algorithm ◽

Iot Devices ◽

The Internet Of Things

Attackers take advantage of every second that the anti- vendor delays identifying the attacking malware signature and to provide notifications. In addition, the longer the detection period delayed, the greater the damage to the host device. To put it another way, the lack of ability to detect attacks early complicates the problem and rises serious harm. Consequently, this research intends to develop a knowledgeable anti-malware system capable of immediately detecting and terminating malware actions, rather than waiting for anti-malware updates. The research concentrates in its scope on the detection of malware on the Internet of Things (IoT), based on Machine Learning (ML) techniques. A latest open source ML algorithm called the Light Gradient Boosting Algorithm (LightGBM) has been used to develop our instant host and network layer antimalware approach without any human intervention. For examination reasons, the suggested approach serves the LightGBM machine learning algorithm to adopt datasets obtained from real IoT devices using the LightGBM machine learning algorithm. The results indicate a successful method to detecting and classifying high accuracy malware at both network and host levels based on the Holdout method of cross-validation. Additionally, this result is better than many prior related studies which used different algorithms of Machine Learning and Deep Learning. Though, an old study which used the same dataset was the best among the literature. However, it still slightly less than what this study achieved, besides the complexity which deep learning adds. Lastly, the results show the ability of the proposed approach to detect IoT botnet attacks fast, which is a vital feature to end botnet activity before spreading to any new network device.

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A Comprehensive and Systematic Survey on the Internet of Things: Security and Privacy Challenges, Security Frameworks, Enabling Technologies, Threats, Vulnerabilities and Countermeasures

Computers ◽

10.3390/computers9020044 ◽

2020 ◽

Vol 9 (2) ◽

pp. 44 ◽

Cited By ~ 2

Author(s):

Muath A. Obaidat ◽

Suhaib Obeidat ◽

Jennifer Holst ◽

Abdullah Al Hayajneh ◽

Joseph Brown

Keyword(s):

Internet Of Things ◽

Cyber Attacks ◽

Security And Privacy ◽

The Internet ◽

Comprehensive Overview ◽

Architecture Model ◽

Research Areas ◽

Iot Devices ◽

The Impact ◽

The Internet Of Things

The Internet of Things (IoT) has experienced constant growth in the number of devices deployed and the range of applications in which such devices are used. They vary widely in size, computational power, capacity storage, and energy. The explosive growth and integration of IoT in different domains and areas of our daily lives has created an Internet of Vulnerabilities (IoV). In the rush to build and implement IoT devices, security and privacy have not been adequately addressed. IoT devices, many of which are highly constrained, are vulnerable to cyber attacks, which threaten the security and privacy of users and systems. This survey provides a comprehensive overview of IoT in regard to areas of application, security architecture frameworks, recent security and privacy issues in IoT, as well as a review of recent similar studies on IoT security and privacy. In addition, the paper presents a comprehensive taxonomy of attacks on IoT based on the three-layer architecture model; perception, network, and application layers, as well as a suggestion of the impact of these attacks on CIA objectives in representative devices, are presented. Moreover, the study proposes mitigations and countermeasures, taking a multi-faceted approach rather than a per layer approach. Open research areas are also covered to provide researchers with the most recent research urgent questions in regard to securing IoT ecosystem.

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Predicting Internet of Things (IOT) Security and Privacy Risks – A Proposal Model: توقع مخاطر أمن وخصوصية إنترنت الأشياء (IOT) – نموذج مقترح بحثي

Journal of engineering sciences and information technology - مجلة العلوم الهندسية و تكنولوجيا المعلومات ◽

10.26389/ajsrp.q070621 ◽

2021 ◽

Vol 5 (3) ◽

pp. 133-112

Author(s):

Awad Saad Al-Qahtani, Mohammad Ayoub Khan Awad Saad Al-Qahtani, Mohammad Ayoub Khan

Keyword(s):

Internet Of Things ◽

Security Management ◽

Cyber Attacks ◽

Security Requirements ◽

Security And Privacy ◽

The Internet ◽

Security Risks ◽

Iot Security ◽

Iot Devices ◽

The Internet Of Things

The Internet of things (IOT) users lack awareness of IOT security infrastructure to handle the risks including Threats, attack and penetration associated with its use. IOT devices are main targets for cyber-attacks due to variable personally identifiable information (PII) stored and transmit in the cyber centers. The security risks of the Internet of Things aimed to damage user's security and privacy. All information about users can be collected from their related objects which are stored in the system or transferred through mediums among diverse smart objects and may exposed to exposed dangerous of attacks and threats if it lack authentication so there are essential need to make IOT security requirements as important part of its efficient implementation. These requirements include; availability, accountability, authentication, authorization, privacy and confidentiality, Integrity and Non-repudiation. The study design is a survey research to investigate the visibility of the proposed model of security management for IOT uses, the security risks of IOT devices, and the changes IOT technology on the IT infrastructure of IOT users through answering of the research questionnaires. This work proposes a model of security management for IOT to predict IOT security and privacy threats, protect IOT users from any unforeseen dangers, and determine the right security mechanisms and protocols for IOT security layers, as well as give the most convenient security mechanisms. Moreover, for enhancing the performance of IOT networks by selecting suitable security mechanisms for IOT layers to increase IOT user's security satisfaction.

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Agricultural Application of Web of Things Architecture

Impact ◽

10.21820/23987073.2019.10.61 ◽

2019 ◽

Vol 2019 (10) ◽

pp. 61-63 ◽

Cited By ~ 1

Author(s):

Akihiro Fujii

Keyword(s):

Internet Of Things ◽

Agricultural Sector ◽

Electronic Device ◽

Specific Work ◽

Science And Engineering ◽

Unique Identifier ◽

Agricultural Application ◽

Iot Devices ◽

The Internet Of Things ◽

Type Pressure

The Internet of Things (IoT) is a term that describes a system of computing devices, digital machines, objects, animals or people that are interrelated. Each of the interrelated 'things' are given a unique identifier and the ability to transfer data over a network that does not require human-to-human or human-to-computer interaction. Examples of IoT in practice include a human with a heart monitor implant, an animal with a biochip transponder (an electronic device inserted under the skin that gives the animal a unique identification number) and a car that has built-in sensors which can alert the driver about any problems, such as when the type pressure is low. The concept of a network of devices was established as early as 1982, although the term 'Internet of Things' was almost certainly first coined by Kevin Ashton in 1999. Since then, IoT devices have become ubiquitous, certainly in some parts of the world. Although there have been significant developments in the technology associated with IoT, the concept is far from being fully realised. Indeed, the potential for the reach of IoT extends to areas which some would find surprising. Researchers at the Faculty of Science and Engineering, Hosei University in Japan, are exploring using IoT in the agricultural sector, with some specific work on the production of melons. For the advancement of IoT in agriculture, difficult and important issues are implementation of subtle activities into computers procedure. The researchers challenges are going on.

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