Automated attacks allow adversaries to exploit vulnerabilities in enterprise IT systems at short notice. To identify such attacks as well as new cybersecurity threats, defenders use honeypot systems; these monitored decoy resources mimic legitimate devices to entice adversaries. The domain of enterprise IT honeypots has been an active area of development and research, especially in the open-source community. In this work, we survey open-source honeypots, honeypot frameworks, and tools that help to develop or discover honeypot deployments. In contrast to existing surveys, our work provides a detailed discussion of the honeypots’ system architecture, software architecture, and cloud-native deployment options. In addition, we cover the most recent academic research in honeypot detection and evasion techniques, and discuss how these advances impact current open-source honeypots. This work helps the reader to make an educated choice when selecting a honeypot for deployment or further development. 

Survey of contemporary open-source honeypots, frameworks, and tools

An integrated framework based deep learning for cancer classification using microarray datasets

The rise of the Industrial Internet of Things (IIoT) plays a crucial role in the era of hyper-connected digital economies. Despite the valuable benefits, such as increased resiliency, self-monitoring and pervasive control, IIoT raises severe cybersecurity and privacy risks, allowing cyberattackers to exploit a plethora of vulnerabilities and weaknesses that can lead to disastrous consequences. Although the Intrusion Detection and Prevention Systems (IDPS) constitute valuable solutions, they suffer from several gaps, such as zero-day attacks, unknown anomalies and false positives. Therefore, the presence of supporting mechanisms is necessary. To this end, honeypots can protect the real assets and trap the cyberattackers. In this paper, we provide a web-based platform called TRUSTY , which is capable of aggregating, storing and analysing the detection results of multiple industrial honeypots related to Modbus/Transmission Control Protocol (TCP), IEC 60870-5-104, BACnet, Message Queuing Telemetry Transport (MQTT) and EtherNet/IP. Based on this analysis, we provide a dataset related to honeypot security events. Moreover, this paper provides a Reinforcement Learning (RL) method, which decides about the number of honeypots that can be deployed in an industrial environment in a strategic way. In particular, this decision is converted into a Multi-Armed Bandit (MAB), which is solved with the Thompson Sampling (TS) method. The evaluation analysis demonstrates the efficiency of the proposed method.

TRUSTY: A Solution for Threat Hunting Using Data Analysis in Critical Infrastructures

—The progression of Software Defined Networking (SDN) and the virtualisation technologies lead to the beyond 5G era, providing multiple benefits in the smart economies. However, despite the advantages, security issues still remain. In particular, SDN/NFV and cloud/edge computing are related to various security issues. Moreover, due to the wireless nature of the entities, they are prone to a wide range of cyberthreats. Therefore, the presence of appropriate intrusion detection mechanisms is critical. Although both Machine Learning (ML) and Deep Learning (DL) have optimised the typical rule-based detection systems, the use of ML and DL requires labelled pre-existing datasets. However, this kind of data varies based on the nature of the respective environment. Another smart solution for detecting intrusions is to use honeypots. A honeypot acts as a decoy with the goal to mislead the cyberatatcker and protect the real assets. In this paper, we focus on Wireless Honeypots (WHs) in ultra-dense networks. In particular, we introduce a strategic honeypot deployment method, using two Reinforcement Learning (RL) techniques: (a) e − Greedy and (b) Q − Learning . Both methods aim to identify the optimal number of honeypots that can be deployed for protecting the actual entities. The experimental results demonstrate the efficacy of both methods.

https://ieeexplore.ieee.org/ielx7/6245516/6558478/09805462.pdf

Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach

Contract Net Protocol (CNP) is a high level communication protocol. It is one of the most widely used in multi-agent system (MAS) to resolve decentralized task allocation problem. The main aim of the protocol is to facilitate contract negotiation between a manager agent and many contractor agents. A lot of works have been done for the verification of the protocol and its extensions, but there still lacks a formalism for representing temporal interaction aspects which are an essential parameter in the protocol modeling. This paper proposes to use Timed Colored Petri Nets (TCPN) to model correctly and formally this temporal dimension often defined as interaction duration and message deadlines. We will verify by means of simulation techniques and state space analysis important properties namely model correctness, deadline respect, absence of deadlocks and livelocks, absence of dead code, agent terminal states consistency, concurrency and validity. Keywords-Negotiation protocols; Contract net protocol; Multiagent systems; Timed Colored Petri Nets.

A Timed Colored Petri-Net-based Modelling for Contract Net Protocol with Temporal Aspects

Technologies used in ICS and Smart Grid are overlapping. The most discussed attacks on ICSs are Stuxnet and Black energy malware. The anatomy of these attacks not only pointed out that the security of ICS is of prime concern but also demanded to execute a proactive approach in practicing ICS security. Honeypot is used to implement defensive measures for security. The Honeynet group released Honeypot for ICS labelled as Conpot in 2013. Though the Conpot is low interactive Honeypot, it emulates processes of different cyber-physical systems, typically Smart Grid. In the literature, the effectiveness of Honeypot operations was studied by challenging limitations of the existing setup or proposing new variants. Similar approaches are followed for Conpot evaluation. However, none of the work addressed a formal verification method to verify the engagement of Honeypot, and this makes the presented work unique. For proposed work, Coloured Petri Net (CPN) tool is used for formal verification of Conpot. The variants of Conpot are modelled, including initial state model, deadlock state model and livelock model. Further evaluation of these models based on state space analysis results confirmed that Conpot could lure an attacker by engaging him in an infinite loop and thereby limiting the scope of the attacker from exploring and damaging the real-time systems or services. However, in the deadlock state, the attacker’s activity in the conpot will be restricted and will be unable to proceed further as the conpot model incorporates deadlock loop.

/pdf/industrial-control-systems-honeypot-a-formal-analysis-of-2pfw2cauy0.pdf

Industrial Control Systems Honeypot: A Formal Analysis of Conpot

Identification of Alzheimer associated differentially expressed gene through microarray data and transfer learning-based image analysis.

: Honeypot is an active defence mechanism intended to mimic a computer system concealing its identity to misguide attackers. The mechanism traps an attacker and collects intrusion information as they trespass a network environment and cause a menace for their interest. The paper proposes a honeypot tool with deadlock and livelock states to strengthen the defence mechanism and engage the attacker for a longer period. The proposed work aims to present the formal analysis of a honeypot using coloured petri nets tool. The three core components of the honeypot, such as data capture, control and collection, are included in the formal modelling to study the behavioural properties of a honeypot in a deadlock and livelock state. The main objective is to emphasise the working of high interaction honeypot in deadlock or livelock states under an attack surface. The honeypot’s formal model verification using a state-space tool in coloured petri net determines that an attacker wedged in a deadlock or livelock state fails to navigate further to fulfil malicious intent, thereby deceiving an attacker for a longer period.

Formal modelling and verification of high interactive honeypot using coloured Petri nets

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Sheetal Gokhale

Author Tools

Chat about Author

Papers

Industrial Control Systems Honeypot: A Formal Analysis of Conpot

Identification of Alzheimer associated differentially expressed gene through microarray data and transfer learning-based image analysis.

Formal modelling and verification of high interactive honeypot using coloured Petri nets