Social Media Monitoring for IoT Cyber-Threats

Alevizopoulou, Sofia; Koloveas, Paris; Tryfonopoulos, Christos; Raftopoulou, Paraskevi

doi:10.1109/csr51186.2021.9527964

Cited by 5 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Confusion matrix, which is also called the error matrix, is the matrix which is the formal way to represent the results of the classification model. The confusion matrix presented in Table 9 shows 143,145,151,152,155,164,170,174,175,183,184,185,186,194,197,199,202 -Easy to implement.…”

Section: Knnmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

Harahsheh,

Chen

2023

IJCAI

View full text Add to dashboard Cite

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration.Povzetek: Podan je pregled uporabe strojnega in globokega učenja v IoT napravah ter izzivov, rešitev in prihodnjih smeri raziskav.Informatica 47 (2023) 1-54 k. Harahsheh et al.

show abstract

Section: Knnmentioning

confidence: 99%

“…-Lower performance compared with other classifiers. 95,106,117,120,123,126,129,132,137,142,145,149,151,155,162,164,166,170,171,182,183,186,190,197,204,205 -Easy to implement.…”

Section: Knnmentioning

confidence: 99%

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

Harahsheh,

Chen

2023

IJCAI

View full text Add to dashboard Cite

show abstract

“…Borcherding et al [43] discussed various types of IoT threats, as well as shallow and deep (deep neural networks, deep belief networks, long short-term memory, and bidirectional LSTM)-based intrusion detection systems (IDS) for the IoT environment, including decision trees, random forests, and support vector machines [44].…”

Section: Threats In Iotmentioning

confidence: 99%

“…The researchers found that deep learning IDS outperformed shallow learning IDS in detecting IoT attacks. Kim-Hung Le et al [44] introduced IMIDS, a powerful intrusion detection system that uses a CNN. The purpose of this study was to identify various cyberattacks accurately using an IDS and an artificial method for generating useful training data.…”

Section: Countermeasures and Mitigation Techniques For The For Iot Th...mentioning

confidence: 99%

Cybersecurity Threats, Countermeasures and Mitigation Techniques on the IoT: Future Research Directions

2022

View full text Add to dashboard Cite

The Internet of Things (IoT) interconnects physical and virtual objects embedded with sensors, software, and other technologies, which exchange data using the Internet. This technology allows billions of devices and people to communicate, share data, and personalize services to make our lives easier. Despite the multiple benefits offered by IoT, it may also represent a critical issue due its lack of information security. Since the number of IoT devices has been rapidly increasing all over the world, they have become a target for many attackers, who try to steal sensitive information and compromise people’s privacy. As part of the IoT environment, data and services should be protected with features such as confidentiality, accuracy, comprehensiveness, authentication, access control, availability, and privacy. Cybersecurity threats are unique to the Internet of Things, which has unique characteristics and limitations. In consideration of this, a variety of threats and attacks are being launched daily against IoT. Therefore, it is important to identify these types of threats and find solutions to mitigate their risks. Therefore, in this paper, we reviewed and identified the most common threats in the IoT environment, and we classified these threats based on three layers of IoT architecture. In addition, we discussed the most common countermeasures to control the IoT threats and mitigation techniques that can be used to mitigate these threats by reviewing the related publications, as well as analyzing the popular application-layer protocols employed in IoT environments and their security risks and challenges.

show abstract

Employing social network analysis to dark web communities

Nikoletos

Raftopoulou

2022

2022 IEEE International Conference on Cyber Security and Resilience (CSR)

View full text Add to dashboard Cite

Social Media Monitoring for IoT Cyber-Threats

Cited by 5 publications

References 13 publications

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

Cybersecurity Threats, Countermeasures and Mitigation Techniques on the IoT: Future Research Directions

Employing social network analysis to dark web communities

Contact Info

Product

Resources

About