Honeypot Implementation in a Cloud Environment

• URL: http://arxiv.org/abs/2301.00710v2
• Date: Wed, 17 Jul 2024 14:35:54 GMT
• Title: Honeypot Implementation in a Cloud Environment
• Authors: Stefan Machmeier,
• Abstract summary: This thesis presents a honeypot solution to investigate malicious activities in heiCLOUD. To detect attackers in restricted network zones at Heidelberg University, a new concept to discover leaks in the firewall will be created. A customized OpenSSH server that works as an intermediary instance will be presented.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this age of digitalization, Internet services face more attacks than ever. An attacker's objective is to exploit systems and use them for malicious purposes. Such efforts are rising as vulnerable systems can be discovered and compromised through Internet-wide scanning. One known methodology besides traditional security leverages is to learn from those who attack it. A honeypot helps to collect information about an attacker by pretending to be a vulnerable target. Thus, how honeypots can contribute to a more secure infrastructure makes an interesting topic of research. This thesis will present a honeypot solution to investigate malicious activities in heiCLOUD and show that attacks have increased significantly. To detect attackers in restricted network zones at Heidelberg University, a new concept to discover leaks in the firewall will be created. Furthermore, to consider an attacker's point of view, a method for detecting honeypots at the transport level will be introduced. Lastly, a customized OpenSSH server that works as an intermediary instance will be presented to mitigate these efforts.

Related papers

• SENet: Visual Detection of Online Social Engineering Attack Campaigns [3.858859576352153]
  Social engineering (SE) aims at deceiving users into performing actions that may compromise their security and privacy. SEShield is a framework for in-browser detection of social engineering attacks.
  arXiv  Detail & Related papers  (2024-01-10T22:25:44Z)
• Rethinking Backdoor Attacks [122.1008188058615]
  In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation. Defending against such attacks typically involves viewing these inserted examples as outliers in the training set and using techniques from robust statistics to detect and remove them. We show that without structural information about the training data distribution, backdoor attacks are indistinguishable from naturally-occurring features in the data.
  arXiv  Detail & Related papers  (2023-07-19T17:44:54Z)
• Backdoor Attack with Sparse and Invisible Trigger [57.41876708712008]
  Deep neural networks (DNNs) are vulnerable to backdoor attacks. backdoor attack is an emerging yet threatening training-phase threat. We propose a sparse and invisible backdoor attack (SIBA)
  arXiv  Detail & Related papers  (2023-05-11T10:05:57Z)
• Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems [0.7829352305480285]
  A growing number of researchers are recently investigating the feasibility of such attacks against machine learning-based security systems. This study was to investigate the actual feasibility of adversarial attacks, specifically evasion attacks, against network-based intrusion detection systems. Our goal is to create adversarial botnet traffic that can avoid detection while still performing all of its intended malicious functionality.
  arXiv  Detail & Related papers  (2023-03-12T14:01:00Z)
• Chatbots in a Honeypot World [0.0]
  Question-and-answer agents like ChatGPT offer a novel tool for use as a potential honeypot interface in cyber security. By imitating Linux, Mac, and Windows terminal commands and providing an interface for TeamViewer, nmap, and ping, it is possible to create a dynamic environment. The paper illustrates ten diverse tasks that a conversational agent or large language model might answer appropriately to the effects of command-line attacker.
  arXiv  Detail & Related papers  (2023-01-10T03:43:35Z)
• Look, Listen, and Attack: Backdoor Attacks Against Video Action Recognition [53.720010650445516]
  We show that poisoned-label image backdoor attacks could be extended temporally in two ways, statically and dynamically. In addition, we explore natural video backdoors to highlight the seriousness of this vulnerability in the video domain. And, for the first time, we study multi-modal (audiovisual) backdoor attacks against video action recognition models.
  arXiv  Detail & Related papers  (2023-01-03T07:40:28Z)
• Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attacks [76.35478518372692]
  We introduce epsilon-illusory, a novel form of adversarial attack on sequential decision-makers. Compared to existing attacks, we empirically find epsilon-illusory to be significantly harder to detect with automated methods. Our findings suggest the need for better anomaly detectors, as well as effective hardware- and system-level defenses.
  arXiv  Detail & Related papers  (2022-07-20T19:49:09Z)
• Measuring and Clustering Network Attackers using Medium-Interaction Honeypots [5.524750830120598]
  Honeypots are often used by information security teams to measure the threat landscape in order to secure their networks. In this work, we deploy such honeypots on five different protocols on the public Internet and study the intent and sophistication of the attacks we observe. We then use the information gained to develop a clustering approach that identifies correlations in attacker behavior to discover IPs that are highly likely to be controlled by a single operator.
  arXiv  Detail & Related papers  (2022-06-27T20:19:39Z)
• Security Orchestration, Automation, and Response Engine for Deployment of Behavioural Honeypots [0.0]
  Security Orchestration, Automation, and Response (SOAR) Engine dynamically deploys custom honeypots inside the internal network infrastructure based on the attacker's behavior. The presence of botnet traffic and DDOS attacks on the honeypots in the network is detected, along with a malware collection system.
  arXiv  Detail & Related papers  (2022-01-14T07:57:12Z)
• Check Your Other Door! Establishing Backdoor Attacks in the Frequency Domain [80.24811082454367]
  We show the advantages of utilizing the frequency domain for establishing undetectable and powerful backdoor attacks. We also show two possible defences that succeed against frequency-based backdoor attacks and possible ways for the attacker to bypass them.
  arXiv  Detail & Related papers  (2021-09-12T12:44:52Z)
• Backdoor Attack in the Physical World [49.64799477792172]
  Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs) Most existing backdoor attacks adopted the setting of static trigger, $i.e.,$ triggers across the training and testing images. We demonstrate that this attack paradigm is vulnerable when the trigger in testing images is not consistent with the one used for training.
  arXiv  Detail & Related papers  (2021-04-06T08:37:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.