Development and implementation of a Honeypot-trap

Егупов, А. А.; Zareshin, Sergey V.; Yadikin, Igor M.; Silnov, Dmitry S.

doi:10.1109/eiconrus.2017.7910572

Cited by 8 publications

(2 citation statements)

References 4 publications

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“…There is also extensive work done on utilizing adversarial transferability in other forms of adversarial attacks, deep learning vulnerabilities in DNNs, and black-box attacks in machine learning. Among other interesting work that served as motivation for this thesis include: utilizing honeypots in defense techniques, such as design and implementation of a honey-trap [12]; deception in decentralized system environments [34]; and using containers in deceptive honeypots [18].…”

Section: Related Workmentioning

confidence: 99%

Using Honeypots in a Decentralized Framework to Defend Against Adversarial Machine-Learning Attacks

Younis

Miri

2019

Lecture Notes in Computer Science

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The market demand for online machine-learning services is increasing, and so to have are the threats to them. Adversarial inputs represent a new threat to Machine-Learningas-a-Services (MLaaSs). Meticulously crafted malicious inputs can be used to mislead and confuse the learning model, even in cases where the adversary only has access to input and output labels. As a result, there has been increased interest in defence techniques to combat these types of attacks.In this thesis, we propose a network of high-interaction honeypots as a decentralized defence framework that prevents an adversary from corrupting the learning model, primarily through the use of deception. We accomplish our aim by 1) preventing the attacker from correctly learning the labels and approximating the architecture of the black-box system;2) luring the attacker away, towards a decoy model, using HoneyTokens; and 3) creating

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Section: Related Workmentioning

confidence: 99%

Using Honeypots in a Decentralized Framework to Defend Against Adversarial Machine-Learning Attacks

Younis

Miri

2019

Lecture Notes in Computer Science

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“…It can counter the attackers to safeguard the security of the information system. Honeypot technology is the most common active defense technology, which designs deliberate system vulnerabilities to guide hackers to attack [84]. It can detect eavesdropping hackers and collects all kinds of hacker attack tools for later defense.…”

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confidence: 99%

Blockchain Technology for Information Security of the Energy Internet: Fundamentals, Features, Strategy and Application

Zeng

Cao

et al. 2020

Energies

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In order to ensure the information security, most of the important information including the data of advanced metering infrastructure (AMI) in the energy internet is currently transmitted and exchanged through the intranet or the carrier communication. The former increases the cost of network construction, and the latter is susceptible to interference and attacks in the process of information dissemination. The blockchain is an emerging decentralized architecture and distributed computing paradigm. Under the premise that these nodes do not need mutual trust, the blockchain can implement trusted peer-to-peer communication for protecting the important information by adopting distributed consensus mechanisms, encryption algorithms, point-to-point transmission and smart contracts. In response to the above issues, this paper firstly analyzes the information security problems existing in the energy internet from the four perspectives of system control layer, device access, market transaction and user privacy. Then blockchain technology is introduced, and its working principles and technical characteristics are analyzed. Based on the technical characteristics, we propose the multilevel and multichain information transmission model for the weak centralization of scheduling and the decentralization of transaction. Furthermore, we discuss that the information transmission model helps solve some of the information security issues from the four perspectives of system control, device access, market transaction and user privacy. Application examples are used to illustrate the technical features that benefited from the blockchain for the information security of the energy internet.

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Abnormal User Pattern Detection Using Semi-structured Server Log File Analysis

Charan

2018

Smart Intelligent Computing and Applications

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Development and implementation of a Honeypot-trap

Cited by 8 publications

References 4 publications

Using Honeypots in a Decentralized Framework to Defend Against Adversarial Machine-Learning Attacks

Using Honeypots in a Decentralized Framework to Defend Against Adversarial Machine-Learning Attacks

Blockchain Technology for Information Security of the Energy Internet: Fundamentals, Features, Strategy and Application

Abnormal User Pattern Detection Using Semi-structured Server Log File Analysis

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