Man-in-the-Middle Attacks to Detect and Identify Services in Encrypted Network Flows using Machine Learning

Al-Hababi, Abdulrahman; Tokgoz, Sezer C.

doi:10.1109/commnet49926.2020.9199617

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…Nonetheless, a MITM attack can be useful even with encrypted data. Al-Hababi and Tokgoz [2] demonstrated how MLsec models can be trained to classify encrypted network traffic and determine which applications or services are being used. The attackers can use this technique for example to establish standard network behavior, or to prospect possible C2 solutions (see Section 2.6).…”

Section: Installationmentioning

confidence: 99%

Offensive Machine Learning Methods and the Cyber Kill Chain

Turtiainen

Costin

Polyakov³

et al. 2022

Artificial Intelligence and Cybersecurity

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Cyberattacks are the "new normal" in the hyper-connected and all-digitized modern world, as breaches, denial-of-service, ransomware, and a myriad of other attacks occur every single day. As the attacks and breaches increase in complexity, diversity, and frequency, cybersecurity actors (both ethical and cybercrime) turn to automating these attacks in various ways and for a variety of reasons, including the development of effective and superior cybersecurity defenses. In this chapter, we address innovations in machine learning, deep learning, and artificial intelligence within the offensive cybersecurity fields. We structure this chapter inline with the Lockheed Martin's Cyber Kill Chain taxonomy in order to cover adequate grounds on this broad topic, and occasionally refer to the more granular MITRE ATT&CK taxonomy whenever relevant.

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Section: Installationmentioning

confidence: 99%

Offensive Machine Learning Methods and the Cyber Kill Chain

Turtiainen

Costin

Polyakov³

et al. 2022

Artificial Intelligence and Cybersecurity

View full text Add to dashboard Cite

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“…But it is possible to infer the utility of nodes and the location of patients and data sources in hospitals. This can be done because packet metadata are not handled by this protocol [9]. To protect this metadata, some presented Sphinx [10].…”

Section: Related Workmentioning

confidence: 99%

Learning Health Systems: An Anonymous Network Routing Protocol

Ecarot

Fraikin

Lavoie

et al. 2021

2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)

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Learning Healthcare Systems are an emerging approach to healthcare research as translated into practice. For this purpose, a strong interconnection comes to be a necessity when dealing with healthcare services, research and knowledge transfer all at once. Practically, these connections imply that a routing protocol should guarantee anonymity to entities in compliance with both laws and ethical requirements while restricting the quantity of information obtainable had an entity been compromised. In order to bring more protection and meet all the requirements, a new message routing protocol is offered to allow the use of data access paths and to resist traffic analysis security threats. The protocol protects the addresses and roles pertaining to entities from any lurking malevolent minds by implementing proxies into a mix-network. Moreover, flows of synthetic datasets and contents identifiers are handled separately so as to curb any risk of re-identification. A model of this protocol is provided in the form of a multi-objective optimization problem, natively integrating objectives of minimizing both latency and entropy of the information exchanged. The assessment of this model shows that the constrained separation of data flows has a minimal impact on delay times, which not only reveals to be an acceptable compromise but also significantly increases security in data access.

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“…Man-in-the-middle (MITM) attack: MITM attack happened whenever the attacker intercepts the user's network, or the user traffic intercepted a network controlled by the attacker, and the attacker can decrypt the network cipher without alert the user [54].…”

Section: Man-made Hazardsmentioning

confidence: 99%

Vulnerability Assessment of Ubiquitous Cities Using the Analytic Hierarchy Process

2020

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Urbanization is a challenge faced by most countries worldwide and leads to several problems. Due to rapid communication capabilities, conforming the megacities into Ubiquitous cities (U-cities) seems to be a potential solution to mitigate the problems caused by urbanization. Extensive reliance and dependencies of U-cities on information and communication technologies (ICTs) bring forth a new set of risks and vulnerabilities to these megacities. This research investigates the vulnerabilities of ICTs against man-made and natural hazards in a systematic way using the Analytic Hierarchy Process. The study identifies the vulnerabilities of different ICTs in U-cities and helps in improving the system’s resistivity against various hazards. The task is performed by evaluating the level of disruption on the different technologies and areas under the identified man-made and natural hazards. The research provides an insight into the working mechanisms of involved ICTs. It also helps to manage U-cities with more secure and sustainable services. The research identified that the new ICTs-based hazards have emerged and have become among the most influential hazards. The research has concluded that the vulnerabilities of U-cities are significantly different from that of conventional cities and need further studies to develop further understandings. The research recommends similar vulnerability studies for regional areas as well.

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Man-in-the-Middle Attacks to Detect and Identify Services in Encrypted Network Flows using Machine Learning

Cited by 9 publications

References 16 publications

Offensive Machine Learning Methods and the Cyber Kill Chain

Offensive Machine Learning Methods and the Cyber Kill Chain

Learning Health Systems: An Anonymous Network Routing Protocol

Vulnerability Assessment of Ubiquitous Cities Using the Analytic Hierarchy Process

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