scholarly journals Decision Tree Based Interference Recognition for Fog Enabled IOT Architecture

2020 ◽  
Vol 2 (1) ◽  
pp. 15-25
Author(s):  
Dr. Mugunthan S. R.
Keyword(s):  
Internet Of Things ◽  
Initial Data ◽  
Decision Tree ◽  
False Alarm ◽  
Final Stage ◽  
Cyber Attacks ◽  
The Internet ◽  
Data Set ◽  
The Internet Of Things ◽  
Decision Forest

The cyber-attacks nowadays are becoming more and more erudite causing challenges in distinguishing them and confining. These attacks affect the sensitized information’s of the network by penetrating into the network and behaving normally. The paper devises a system for such interference recognition in the internet of things architecture that is aided by the FOG. The proposed system is a combination of variety of classifiers that are founded on the decision tree as well as the rule centered conceptions. The system put forth involves the JRip and the REP tree algorithm to utilize the features of the data set as input and distinguishes between the benign and the malicious traffic in the network and includes an decision forest that is improved with the penalizing attributes of the previous trees in the final stage to classify the traffic in the network utilizing the initial data set as well as the outputs of the classifiers that were engaged in the former stages. The proffered system was examined using the dataset such BOT-Internet of things and the CICIDS2017 to evince its competence in terms of rate of false alarm, detection, and accuracy. The attained results proved that the performance of the proposed system was better compared to the exiting methodologies to recognize the interference.

Sustainable Security for the Internet of Things Using Artificial Intelligence Architectures

2021 ◽  
Vol 21 (3) ◽  
pp. 1-22
Author(s):  
Celestine Iwendi ◽  
Saif Ur Rehman ◽  
Abdul Rehman Javed ◽  
Suleman Khan ◽  
Gautam Srivastava
Keyword(s):  
Internet Of Things ◽  
Detection System ◽  
Experimental Testing ◽  
Cyber Attacks ◽  
Internet Technology ◽  
The Internet ◽  
Business Operations ◽  
Private Data ◽  
The Internet Of Things ◽  
Better Than

In this digital age, human dependency on technology in various fields has been increasing tremendously. Torrential amounts of different electronic products are being manufactured daily for everyday use. With this advancement in the world of Internet technology, cybersecurity of software and hardware systems are now prerequisites for major business’ operations. Every technology on the market has multiple vulnerabilities that are exploited by hackers and cyber-criminals daily to manipulate data sometimes for malicious purposes. In any system, the Intrusion Detection System (IDS) is a fundamental component for ensuring the security of devices from digital attacks. Recognition of new developing digital threats is getting harder for existing IDS. Furthermore, advanced frameworks are required for IDS to function both efficiently and effectively. The commonly observed cyber-attacks in the business domain include minor attacks used for stealing private data. This article presents a deep learning methodology for detecting cyber-attacks on the Internet of Things using a Long Short Term Networks classifier. Our extensive experimental testing show an Accuracy of 99.09%, F1-score of 99.46%, and Recall of 99.51%, respectively. A detailed metric representing our results in tabular form was used to compare how our model was better than other state-of-the-art models in detecting cyber-attacks with proficiency.

scholarly journals Hardware Security of Fog End-Devices for the Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5729 ◽  
Author(s):  
Ismail Butun ◽  
Alparslan Sari ◽  
Patrik Österberg
Keyword(s):  
Internet Of Things ◽  
Fog Computing ◽  
Hardware Security ◽  
Cyber Attacks ◽  
The Internet ◽  
Edge Component ◽  
Security Module ◽  
System On A Chip ◽  
Lower Spectrum ◽  
The Internet Of Things

The proliferation of the Internet of Things (IoT) caused new application needs to emerge as rapid response ability is missing in the current IoT end-devices. Therefore, Fog Computing has been proposed to be an edge component for the IoT networks as a remedy to this problem. In recent times, cyber-attacks are on the rise, especially towards infrastructure-less networks, such as IoT. Many botnet attack variants (Mirai, Torii, etc.) have shown that the tiny microdevices at the lower spectrum of the network are becoming a valued participant of a botnet, for further executing more sophisticated attacks against infrastructural networks. As such, the fog devices also need to be secured against cyber-attacks, not only software-wise, but also from hardware alterations and manipulations. Hence, this article first highlights the importance and benefits of fog computing for IoT networks, then investigates the means of providing hardware security to these devices with an enriched literature review, including but not limited to Hardware Security Module, Physically Unclonable Function, System on a Chip, and Tamper Resistant Memory.

scholarly journals Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1977 ◽  
Author(s):  
Geethapriya Thamilarasu ◽  
Shiven Chawla
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Learning Algorithm ◽  
Detection System ◽  
Cyber Attacks ◽  
The Internet ◽  
Critical Systems ◽  
The Internet Of Things

Cyber-attacks on the Internet of Things (IoT) are growing at an alarming rate as devices, applications, and communication networks are becoming increasingly connected and integrated. When attacks on IoT networks go undetected for longer periods, it affects availability of critical systems for end users, increases the number of data breaches and identity theft, drives up the costs and impacts the revenue. It is imperative to detect attacks on IoT systems in near real time to provide effective security and defense. In this paper, we develop an intelligent intrusion-detection system tailored to the IoT environment. Specifically, we use a deep-learning algorithm to detect malicious traffic in IoT networks. The detection solution provides security as a service and facilitates interoperability between various network communication protocols used in IoT. We evaluate our proposed detection framework using both real-network traces for providing a proof of concept, and using simulation for providing evidence of its scalability. Our experimental results confirm that the proposed intrusion-detection system can detect real-world intrusions effectively.

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.

Anomalies detection in the application layer with new combined methods in IoT networks

2021 ◽  
pp. 1-8
Author(s):  
Adeleh Jafar Gholi Beik ◽  
Mohammad Ebrahim Shiri Ahmad Abadib ◽  
Afshin Rezakhani
Keyword(s):  
Internet Of Things ◽  
Input Data ◽  
The Internet ◽  
Application Layer ◽  
Data Set ◽  
Malicious Activity ◽  
Proposed Model ◽  
Improved Model ◽  
Internet Networks ◽  
The Internet Of Things

Today, due to increasing dependence on the internet, the tendency to make smart and the Internet of things (IoT), has risen. Also, detecting attacks, and malicious activity as well as anomalies on the internet networks, and preventing them from different layers is a necessity. In this method, a new hybrid model of IWC clustering and Random Forest methods are introduced to identify normal and abnormal conditions. It also shows unauthorized access and attacks to different layers of the Internet of Things, especially the application layer. The IWC is a clustering and improved model of the k-means method. After being tested, evaluated, and compared with previous methods, the proposed model indicates that identifying anomalies in, its data has been efficient and useful. Unlabeled data from the Intel data set IBRL is used to cluster its input data. The NSL-KDD data set is also used in the proposed method to select the best classification and identify attacks on the network.

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.

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

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.

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

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 44 ◽  
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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