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 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.

scholarly journals Recent Advancements in Intrusion Detection Systems for the Internet of Things

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Zeeshan Ali Khan ◽  
Peter Herrmann
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Ad Hoc ◽  
Denial Of Service ◽  
Research Work ◽  
Intrusion Detection Systems ◽  
Future Research ◽  
Detection Systems ◽  
Iot Applications ◽  
Iot Devices

Many Internet of Things (IoT) systems run on tiny connected devices that have to deal with severe processor and energy restrictions. Often, the limited processing resources do not allow the use of standard security mechanisms on the nodes, making IoT applications quite vulnerable to different types of attacks. This holds particularly for intrusion detection systems (IDS) that are usually too resource-heavy to be handled by small IoT devices. Thus, many IoT systems are not sufficiently protected against typical network attacks like Denial-of-Service (DoS) and routing attacks. On the other side, IDSs have already been successfully used in adjacent network types like Mobile Ad hoc Networks (MANET), Wireless Sensor Networks (WSN), and Cyber-Physical Systems (CPS) which, in part, face limitations similar to those of IoT applications. Moreover, there is research work ongoing that promises IDSs that may better fit to the limitations of IoT devices. In this article, we will give an overview about IDSs suited for IoT networks. Besides looking on approaches developed particularly for IoT, we introduce also work for the three similar network types mentioned above and discuss if they are also suitable for IoT systems. In addition, we present some suggestions for future research work that could be useful to make IoT networks more secure.

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.

scholarly journals A Hybrid Framework for Intrusion Detection in Healthcare Systems Using Deep Learning

2022 ◽  
Vol 9 ◽  
Author(s):  
M. Akshay Kumaar ◽  
Duraimurugan Samiayya ◽  
P. M. Durai Raj Vincent ◽  
Kathiravan Srinivasan ◽  
Chuan-Yu Chang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Healthcare Systems ◽  
Cyber Attacks ◽  
Optimization Techniques ◽  
Machine Learning Algorithms ◽  
Cyber Attack ◽  
Network Intrusion ◽  
Hybrid Framework

The unbounded increase in network traffic and user data has made it difficult for network intrusion detection systems to be abreast and perform well. Intrusion Systems are crucial in e-healthcare since the patients' medical records should be kept highly secure, confidential, and accurate. Any change in the actual patient data can lead to errors in the diagnosis and treatment. Most of the existing artificial intelligence-based systems are trained on outdated intrusion detection repositories, which can produce more false positives and require retraining the algorithm from scratch to support new attacks. These processes also make it challenging to secure patient records in medical systems as the intrusion detection mechanisms can become frequently obsolete. This paper proposes a hybrid framework using Deep Learning named “ImmuneNet” to recognize the latest intrusion attacks and defend healthcare data. The proposed framework uses multiple feature engineering processes, oversampling methods to improve class balance, and hyper-parameter optimization techniques to achieve high accuracy and performance. The architecture contains <1 million parameters, making it lightweight, fast, and IoT-friendly, suitable for deploying the IDS on medical devices and healthcare systems. The performance of ImmuneNet was benchmarked against several other machine learning algorithms on the Canadian Institute for Cybersecurity's Intrusion Detection System 2017, 2018, and Bell DNS 2021 datasets which contain extensive real-time and latest cyber attack data. Out of all the experiments, ImmuneNet performed the best on the CIC Bell DNS 2021 dataset with about 99.19% accuracy, 99.22% precision, 99.19% recall, and 99.2% ROC-AUC scores, which are comparatively better and up-to-date than other existing approaches in classifying between requests that are normal, intrusion, and other cyber attacks.

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.

Techniques to Model and Derive a Cyber-Attacker’s Intelligence

2013 ◽  
pp. 162-180 ◽  
Author(s):  
Peter J. Hawrylak ◽  
Chris Hartney ◽  
Michael Haney ◽  
Jonathan Hamm ◽  
John Hale
Keyword(s):  
Intrusion Detection ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Intrusion Detection Systems ◽  
Cyber Attack ◽  
Computationally Efficient ◽  
Attack Graph ◽  
Detection Systems ◽  
Specialized Hardware ◽  
Time Required

Identifying the level of intelligence of a cyber-attacker is critical to detecting cyber-attacks and determining the next targets or steps of the adversary. This chapter explores intrusion detection systems (IDSs) which are the traditional tool for cyber-attack detection, and attack graphs which are a formalism used to model cyber-attacks. The time required to detect an attack can be reduced by classifying the attacker’s knowledge about the system to determine the traces or signatures for the IDS to look for in the audit logs. The adversary’s knowledge of the system can then be used to identify their most likely next steps from the attack graph. A computationally efficient technique to compute the likelihood and impact of each step of an attack is presented. The chapter concludes with a discussion describing the next steps for implementation of these processes in specialized hardware to achieve real-time attack detection.

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