Dockerize torbot (#167)

* Dockerize torbot

* Cleanup apt lists

Dockerfile

@@ -0,0 +1,19 @@
+FROM python:3.6-stretch
+LABEL maintainer="v1shwa"
+
+# Install PyQt5
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends python3-pyqt5 \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY . .
+
+RUN pip install -r requirements.txt
+
+RUN chmod +x install.sh
+RUN bash install.sh
+
+ENTRYPOINT ["./torBot", "--ip", "tor"]

README.md

@@ -6,8 +6,8 @@
                               ██║   ██║   ██║██╔══██╗    ██╔══██╗████╔╝██║   ██║
                               ██║   ╚██████╔╝██║  ██║    ██████╔╝╚██████╔╝   ██║
                               ╚═╝    ╚═════╝ ╚═╝  ╚═╝    ╚═════╝  ╚═════╝    ╚═╝
-		       
-	           	        Open Source Intelligence Tool for the Dark Web
+               
+                        Open Source Intelligence Tool for the Dark Web
 
 </pre>
 [![Build Status](https://travis-ci.org/DedSecInside/TorBot.svg?branch=dev)](https://travis-ci.org/DedSecInside/TorBoT)
@@ -22,13 +22,13 @@ the following steps:
  <pre>
 URLs = input(url)
 while(URLs is not empty) do
-	dequeue url
-	request page
-	parse for Links
-	for(link in Links) do 
-		if (link islive && link is not visited) then 
-			add link to URLs
-	store page content
+    dequeue url
+    request page
+    parse for Links
+    for(link in Links) do 
+        if (link islive && link is not visited) then 
+            add link to URLs
+    store page content
  </pre>
  </code>
 
@@ -111,6 +111,16 @@ optional arguments:
 
 Read more about torrc here : [Torrc](https://github.com/DedSecInside/TorBoT/blob/master/Tor.md)
 
+#### Using Docker
+
+- Ensure than you have a tor container running on port 9050.
+- Build the image using following command:
+
+    `docker build -t dedsecinside/torbot .`
+- Run the container (make sure to link the tor container as `tor`):
+
+    `docker run --link tor:tor --rm -ti dedsecinside/torbot`
+
 ## TO-DO
 - [ ] Visualization Module
 - [x] Implement BFS Search for webcrawler
@@ -128,27 +138,27 @@ If the idea is worth implementing, congratz, you are now a contributor.
 
 ### References
 
-	1.	M. Glassman and M. J. Kang, “Intelligence in the internet age: The emergence and evolution of Open Source Intelligence (OSINT),” Comput. Human Behav., vol. 28, no. 2, pp. 673–682, 2012.
-	2.	D. Bradbury, “In plain view: open source intelligence,” Comput. Fraud Secur., vol. 2011, no. 4, pp. 5–9, 2011.
-	3.	B. Butler, B. Wardman, and N. Pratt, “REAPER: an automated, scalable solution for mass credential harvesting and OSINT,” 2016 APWG Symp. Electron. Crime Res., pp. 1–10, 2016.
-	4.	B. Zantout and R. A. Haraty, “I2P Data Communication System I2P Data Communication System,” no. April 2002, 2014.
-	5.	J. Qin, Y. Zhou, G. Lai, E. Reid, M. Sageman, and H. Chen, “The dark web portal project: collecting and analyzing the presence of terrorist groups on the web,” in Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics, 2005, pp. 623–624.
-	6.	D. Moore, T. Rid, D. Moore, and T. Rid, “Cryptopolitik and the Darknet Cryptopolitik and the Darknet,” vol. 6338, 2016.
-	7.	G. Weimann, “Going dark: Terrorism on the dark Web,” Stud. Confl. Terror., vol. 39, no. 3, pp. 195–206, 2016.
-	8.	A. T. Zulkarnine, R. Frank, B. Monk, J. Mitchell, and G. Davies, “Surfacing collaborated networks in dark web to find illicit and criminal content,” in Intelligence and Security Informatics (ISI), 2016 IEEE Conference on, 2016, pp. 109–114.
-	9.	T. Minárik and A.-M. Osula, “Tor does not stink: Use and abuse of the Tor anonymity network from the perspective of law,” Comput. Law Secur. Rev., vol. 32, no. 1, pp. 111–127, 2016.
-	10.	K. Loesing, S. J. Murdoch, and R. Dingledine, “A Case Study on Measuring Statistical Data in the {T}or Anonymity Network,” in Proceedings of the Workshop on Ethics in Computer Security Research (WECSR 2010), 2010.
-	11.	B. Nafziger, “Data Mining in the Dark : Darknet Intelligence Automation,” 2017.
-	12.	I. Sanchez-Rola, D. Balzarotti, and I. Santos, “The onions have eyes: A comprehensive structure and privacy analysis of tor hidden services,” in Proceedings of the 26th International Conference on World Wide Web, 2017, pp. 1251–1260.
-	13.	Mouli VR, Jevitha KP. “Web Services Attacks and Security-A Systematic Literature Review.”, Procedia Computer Science. 2016 Jan 1;93:870-7.
-	14.	Cova M, Felmetsger V, Vigna G. "Vulnerability analysis of web-based applications. InTest and Analysis of Web Services" 2007 (pp. 363-394). Springer, Berlin, Heidelberg.
-	15.	B. R. Holland, “Enabling Open Source Intelligence (OSINT) in private social networks,” 2012.
-	16.	S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” Cryptogr. Mail. List https//metzdowd.com, 2009.
-	17.	M. Wesam, A. Nabki, E. Fidalgo, E. Alegre, and I. De Paz, “Classifying Illegal Activities on Tor Network Based on Web Textual Contents”, vol. 1, pp. 35–43, 2017.
-	18.	Sathyadevan S, Gangadharan S.“Crime analysis and prediction using data mining”. In Networks & Soft Computing (ICNSC), 2014 First International Conference on 2014 Aug 19 (pp. 406-412). IEEE.
-	19.	Chau M, Chen H. "A machine learning approach to web page filtering using content and structure analysis. Decision Support Systems." 2008 Jan 1;44(2):482-94.
-	20.	Ani R, Jose J, Wilson M, Deepa OS. “Modified Rotation Forest Ensemble Classifier for Medical Diagnosis in Decision Support Systems”, In Progress in Advanced Computing and Intelligent Engineering 2018 (pp. 137-146). Springer, Singapore.
-	21.	Ani R, Augustine A, Akhil N.C. and Deepa O.S., 2016. “Random Forest Ensemble Classifier to Predict the Coronary Heart Disease Using Risk Factors”, In Proceedings of the International Conference on Soft Computing Systems (pp. 701-710). Springer, New Delhi.
+    1.  M. Glassman and M. J. Kang, “Intelligence in the internet age: The emergence and evolution of Open Source Intelligence (OSINT),” Comput. Human Behav., vol. 28, no. 2, pp. 673–682, 2012.
+    2.  D. Bradbury, “In plain view: open source intelligence,” Comput. Fraud Secur., vol. 2011, no. 4, pp. 5–9, 2011.
+    3.  B. Butler, B. Wardman, and N. Pratt, “REAPER: an automated, scalable solution for mass credential harvesting and OSINT,” 2016 APWG Symp. Electron. Crime Res., pp. 1–10, 2016.
+    4.  B. Zantout and R. A. Haraty, “I2P Data Communication System I2P Data Communication System,” no. April 2002, 2014.
+    5.  J. Qin, Y. Zhou, G. Lai, E. Reid, M. Sageman, and H. Chen, “The dark web portal project: collecting and analyzing the presence of terrorist groups on the web,” in Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics, 2005, pp. 623–624.
+    6.  D. Moore, T. Rid, D. Moore, and T. Rid, “Cryptopolitik and the Darknet Cryptopolitik and the Darknet,” vol. 6338, 2016.
+    7.  G. Weimann, “Going dark: Terrorism on the dark Web,” Stud. Confl. Terror., vol. 39, no. 3, pp. 195–206, 2016.
+    8.  A. T. Zulkarnine, R. Frank, B. Monk, J. Mitchell, and G. Davies, “Surfacing collaborated networks in dark web to find illicit and criminal content,” in Intelligence and Security Informatics (ISI), 2016 IEEE Conference on, 2016, pp. 109–114.
+    9.  T. Minárik and A.-M. Osula, “Tor does not stink: Use and abuse of the Tor anonymity network from the perspective of law,” Comput. Law Secur. Rev., vol. 32, no. 1, pp. 111–127, 2016.
+    10. K. Loesing, S. J. Murdoch, and R. Dingledine, “A Case Study on Measuring Statistical Data in the {T}or Anonymity Network,” in Proceedings of the Workshop on Ethics in Computer Security Research (WECSR 2010), 2010.
+    11. B. Nafziger, “Data Mining in the Dark : Darknet Intelligence Automation,” 2017.
+    12. I. Sanchez-Rola, D. Balzarotti, and I. Santos, “The onions have eyes: A comprehensive structure and privacy analysis of tor hidden services,” in Proceedings of the 26th International Conference on World Wide Web, 2017, pp. 1251–1260.
+    13. Mouli VR, Jevitha KP. “Web Services Attacks and Security-A Systematic Literature Review.”, Procedia Computer Science. 2016 Jan 1;93:870-7.
+    14. Cova M, Felmetsger V, Vigna G. "Vulnerability analysis of web-based applications. InTest and Analysis of Web Services" 2007 (pp. 363-394). Springer, Berlin, Heidelberg.
+    15. B. R. Holland, “Enabling Open Source Intelligence (OSINT) in private social networks,” 2012.
+    16. S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” Cryptogr. Mail. List https//metzdowd.com, 2009.
+    17. M. Wesam, A. Nabki, E. Fidalgo, E. Alegre, and I. De Paz, “Classifying Illegal Activities on Tor Network Based on Web Textual Contents”, vol. 1, pp. 35–43, 2017.
+    18. Sathyadevan S, Gangadharan S.“Crime analysis and prediction using data mining”. In Networks & Soft Computing (ICNSC), 2014 First International Conference on 2014 Aug 19 (pp. 406-412). IEEE.
+    19. Chau M, Chen H. "A machine learning approach to web page filtering using content and structure analysis. Decision Support Systems." 2008 Jan 1;44(2):482-94.
+    20. Ani R, Jose J, Wilson M, Deepa OS. “Modified Rotation Forest Ensemble Classifier for Medical Diagnosis in Decision Support Systems”, In Progress in Advanced Computing and Intelligent Engineering 2018 (pp. 137-146). Springer, Singapore.
+    21. Ani R, Augustine A, Akhil N.C. and Deepa O.S., 2016. “Random Forest Ensemble Classifier to Predict the Coronary Heart Disease Using Risk Factors”, In Proceedings of the International Conference on Soft Computing Systems (pp. 701-710). Springer, New Delhi.