Internet of Things and the Man-in-the-Middle Attacks – Security and Economic Risks

Čekerevac, Zoran; Dvořák, Zdeněk; Prigoda, Ludmila; Čekerevac, Petar

doi:10.12709/mest.05.05.02.03

Cited by 64 publications

(24 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Attackers can redirect network traffic using Address Resolution Protocol (ARP) poisoning or by altering Domain Name System (DNS) settings or intercept HTTPS traffic by using self-signed certificates or tools such as (Secure Sockets Layer) SSL strip Fig. 2 A simple representation of a Man-in-the-Middle attack [19]. Many IoT devices do not verify the authenticity or the trust level of certificates, making the self-signed certificate method particularly effective.…”

Section: Man-in-the-middlementioning

confidence: 99%

“…By doing this, mobile apps can locate new devices by probing every IP address on the local network for a specific port. An attacker can do the same thing to discover devices on the network [19]. An example of an MITM IoT attack is that of a smart refrigerator that could display the user's Google calendar.…”

Section: Man-in-the-middlementioning

confidence: 99%

“…An example of an MITM IoT attack is that of a smart refrigerator that could display the user's Google calendar. It seems like a harmless feature, but attackers found that the system did not validate SSL certificates, which allowed them to perform an MITM attack and steal the user's Google credentials [19].…”

Section: Man-in-the-middlementioning

confidence: 99%

See 2 more Smart Citations

Role of Artificial Intelligence in the Internet of Things (IoT) cybersecurity

Kuzlu

Fair

Güler³

2021

Discov Internet Things

159

View full text Add to dashboard Cite

In recent years, the use of the Internet of Things (IoT) has increased exponentially, and cybersecurity concerns have increased along with it. On the cutting edge of cybersecurity is Artificial Intelligence (AI), which is used for the development of complex algorithms to protect networks and systems, including IoT systems. However, cyber-attackers have figured out how to exploit AI and have even begun to use adversarial AI in order to carry out cybersecurity attacks. This review paper compiles information from several other surveys and research papers regarding IoT, AI, and attacks with and against AI and explores the relationship between these three topics with the purpose of comprehensively presenting and summarizing relevant literature in these fields.

show abstract

Section: Man-in-the-middlementioning

confidence: 99%

Section: Man-in-the-middlementioning

confidence: 99%

See 1 more Smart Citation

Role of Artificial Intelligence in the Internet of Things (IoT) cybersecurity

Kuzlu

Fair

Güler³

2021

Discov Internet Things

159

View full text Add to dashboard Cite

show abstract

“…Many cases of data and identity theft through hacked vehicles and hacked smart refrigerators have been previously reported. In 2014, a Samsung smart refrigerator, RF28HMELBSR, was a target of a man-in-the-middle attack to steal victims' Google credentials [22]. The hacking of Jeep Cherokee in July 2016 through MITM attack has also enabled hackers to access and control the vehicles' basic functions and consequently endangered the human life.…”

Section: A Geopolitical Risk and Threatsmentioning

confidence: 99%

IoT Technological Development: Prospect and Implication for Cyberstability

Anawar¹,

Zakaria²,

Masu’d³

et al. 2019

ijacsa

View full text Add to dashboard Cite

Failure to address the risk poses by future technological development could cause devastating damage to public trust in the technologies. Therefore, ascendant technologies such as artificial intelligence are the key components to provide solutions for new cybersecurity threats and strengthen the capabilities of the future technological developments. In effect, ability of the technologies to prevent and withstand a cyber-attack could become the new deterrence. This paper will provide gaps to guide the government, industry, and the research community in pursuing Internet of Things (IoT) technological development that may be in need of improvement. The contribution of this paper is as follows: First, a roadmap that outline security requirements and concerns of future technology and the significant of IoT technology in addressing the concerns. Second, an assessment that illustrates the expected and unexpected impact of future technology adoption and its significant geopolitical implication on potential impacted areas such as regulatory, legal, political, military, and intelligence.

show abstract

“…The authors also suggested a countermeasure called Bloom filters and indicated the efficiency of the Bloom filter monitoring system. Cekerevac et al [29] showed a variety of attacks using MitM, including MIT-Cloud (MITC), MIT-Browser (MITB), and MIT-mobile (MITMO). They showed that MitM is not uncommon and can damage the IoT, even though it is an old attack.…”

mentioning

confidence: 99%

Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning

et al. 2020

View full text Add to dashboard Cite

As the number of Internet of Things (IoT) devices connected to the network rapidly increases, network attacks such as flooding and Denial of Service (DoS) are also increasing. These attacks cause network disruption and denial of service to IoT devices. However, a large number of heterogenous devices deployed in the IoT environment make it difficult to detect IoT attacks using traditional rule-based security solutions. It is challenging to develop optimal security models for each type of the device. Machine learning (ML) is an alternative technique that allows one to develop optimal security models based on empirical data from each device. We employ the ML technique for IoT attack detection. We focus on botnet attacks targeting various IoT devices and develop ML-based models for each type of device. We use the N-BaIoT dataset generated by injecting botnet attacks (Bashlite and Mirai) into various types of IoT devices, including a Doorbell, Baby Monitor, Security Camera, and Webcam. We develop a botnet detection model for each device using numerous ML models, including deep learning (DL) models. We then analyze the effective models with a high detection F1-score by carrying out multiclass classification, as well as binary classification, for each model.

show abstract

Internet of Things and the Man-in-the-Middle Attacks – Security and Economic Risks

Cited by 64 publications

References 0 publications

Role of Artificial Intelligence in the Internet of Things (IoT) cybersecurity

Role of Artificial Intelligence in the Internet of Things (IoT) cybersecurity

IoT Technological Development: Prospect and Implication for Cyberstability

Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning

Contact Info

Product

Resources

About