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Figure 3: REN-JIN forensic model (Pelaez 2006) Jose Mas y Rubi et al. The Honeynets are highly controlled type of network architecture, one in which you can monitor all activity that occurs. By placing real victims (which can be any type of system, service or information) inside the network like an attack target, it creates an environment where you can observe everything that happens on it, allowing the attacking intruders interact with the Honeynet while information from that attack is being collected. This happens because Honeynets are high interaction real networks which implement traps to detect, deviate, or in some cases, to counteract non-authorize uses of the information system; where no service neither traffic is generated. Therefore, any interaction with the Honeynet implies malicious or non-authorize activities. Any connection initiated to a Honeynet implies that someone has compromised a system and has initiated a suspicious activity. This makes much easier the activity analysis, because all the captured information can be assumed as non-authorize or malicious one (Honeynet 2006). 4. Comparative analysis One of the objectives of our work is to discuss the structure of REN-JIN and CALEA models, so that at the end, we could affirm if one of these models can be applicable for a VoIP traffic forensic analysis, and also proposing possible improvement to the selected model in this analysis process. The methodology to follow implies that the mentioned models are analyzed in the DFRWS general model structure, to identify if all the elements functions of each individual model meet the requirements of the chosen general model. In conclusion, the elements that integrate the forensic models CALEA and REN-JIN will be located in the corresponding step of the DFRWS model structure, to identify if the functions that those elements provide can cover one of the general model’s important steps. 4.1 Discussion and analysis Table 2 shows the main function of each of the analyzed models, and then compared to the general model functions: 4.2 REN-JIN and CALEA operation differences The main functions of CALEA are focused on a single component, LEA, which will depend on the traffic mirror used by the forensic agents to collect the required information. This makes the model easily adaptable to the rules governing the legal interception in the countries where these type of tools are used. However, it is the duty of each country to lay down rules for the use of this type of system, so the collected evidence could have full legality in the judicial environment. 164
Jose Mas y Rubi et al. Table 2: Comparative analysis between REN-JIN and CALEA models In contrast, the main functions of REN-JIN are distributed between different components of the model, which are mainly controlled by the Network Forensic Server, which has autonomous power to determine what type of traffic should be captured and analyzed. This allow the tool to collect evidence in sequential steps, being able to obtain more precise and adequate information, regarding to the requirements of the judicial entities. Due to the characteristics of CALEA model, forensic investigators should have action freedom over the analyzed networks. However, because those networks can be public, there is a potential risk that interceptions could involve innocent users, violating their privacy rights. REN-JIN, as CALEA model, requires that forensic investigators have action freedom over the analyzed network. However, the traffic to be analyze is canalize to a Honeynet used by the model, preserving the privacy rights of all the user that are not involve in this study. It can be considered that CALEA performance is reactive, due to the fact that forensic investigators should identify the suspect and after it, they have to implement the analysis and capture platform proposed by the model. On the other hand, REN-JIN performance is also of the reactive type, but instead of previously identify the suspect, you need to identify the attacked network, which will become our decoy network (Honeynet), based in the analysis and capture platform proposed by the model. 4.3 Model election Based on our analysis and after doing a balance between advantages and limitations of the two studied models, we observed that REN-JIN model has a more adequate architecture that possess the majority of the functions of the DFRWS general model, and when its limitations are overcome, this model can be validated as a network forensic model. Also, while REN-JIN is a theoretical model, we believe that it could be properly implemented. 4.4 Improvements in the chosen model Considering REN-JIN as the chosen model, we observed that it presents several flaws, so we propose to correct them through the insertion of new elements that allow us to strengthen the architecture for a good VoIP forensic analysis. The identification function could be implemented in a convergence network through technologies like MEGACO H.248 protocol (ITU 2005) and ENUM (IETF 2004). 165
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The Proceedings of the 6th Internat
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Contents Paper Title Author(s) Page
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Preface These Proceedings are the w
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Biographies of contributing authors
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Department of Computer Science, IQR
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Using the Longest Common Substring
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Jaime Acosta Some techniques that u
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Jaime Acosta assigned by an anti-vi
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Jaime Acosta Cormen, T.H., Leiserso
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Hind Al Falasi and Liren Zhang tran
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Hind Al Falasi and Liren Zhang ther
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Hind Al Falasi and Liren Zhang the
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Edwin Leigh Armistead and Thomas Mu
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Deception Operations Security (OPS
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The Uses and Limits of Game Theory
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3. Limits to using game theory 3.1
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Merritt Baer Effective cyberintrusi
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Merritt Baer 1.5), there seems to b
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Merritt Baer Report of the Defense
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Ivan Burke and Renier van Heerden F
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Ivan Burke and Renier van Heerden a
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Marco Carvalho et al. systems start
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Marco Carvalho et al. Benatallah, 2
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Marco Carvalho et al. management la
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Marco Carvalho et al. Figure 4: Sta
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Marco Carvalho et al. Sorrels, D.,
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Manoj Cherukuri and Srinivas Mukkam
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Mecealus Cronkrite et al. socially
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Mecealus Cronkrite et al. The views
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Vincent Garramone and Daniel Likari
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Stephen Groat et al. Sections 4 and
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Stephen Groat et al. probability fo
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Stephen Groat et al. host. In this
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Stephen Groat et al. changing addre
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Marthie Grobler et al. leadership,
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Marthie Grobler et al. apply to sta
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Marthie Grobler et al. 6. Working t
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Cyber Strategy and the Law of Armed
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Ulf Haeussler Alliance and Allies r
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Ulf Haeussler following the invocat
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Ulf Haeussler NCSA (2009) NCSA Supp
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Karim Hamza and Van Dalen of respon
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Karim Hamza and Van Dalen From a mi
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Karim Hamza and Van Dalen productiv
Page 123 and 124: Intelligence-Driven Computer Networ
Page 125 and 126: Eric Hutchins et al. of defensive a
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Page 149 and 150: 4. Evaluation Brian Jewell and Just
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Page 171 and 172: Jose Mas y Rubi et al. As we can se
Page 173: Figure 2: CALEA forensic model (Pel
Page 177 and 178: Jose Mas y Rubi et al. Another pend
Page 179 and 180: Tree of Objectives Acknowledgements
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Page 183 and 184: Ruchika Mehresh et al. implementing
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Page 199 and 200: Muhammad Naveed Pakistan Computer E
Page 201 and 202: Muhammad Naveed response could also
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Page 219 and 220: Christopher Perr attacks again help
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David Rohret and Michael Kraft reve
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David Rohret and Michael Kraft sary
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Data (Evidence) Removal Shield Davi
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Neutrality in the Context of Cyberw
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Julie Ryan and Daniel Ryan 18th cen
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Julie Ryan and Daniel Ryan “Decla
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Julie Ryan and Daniel Ryan von Glah
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Harm Schotanus et al. In the remain
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Harm Schotanus et al. 2.3.1 Secure
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Harm Schotanus et al. In this setup
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Harm Schotanus et al. the label (by
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Harm Schotanus et al. these aspects
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Maria Semmelrock-Picej et al. they
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Maria Semmelrock-Picej et al. User
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Maria Semmelrock-Picej et al. SPIKE
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Maria Semmelrock-Picej et al. A cl
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Maria Semmelrock-Picej et al. In th
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Maria Semmelrock-Picej et al. Fuchs
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Madhu Shankarapani and Srinivas Muk
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Figure 1: UPX packed Trojan Figure
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Trojan.Zb ot- 1342.mal Trojan.Sp y.
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Madhu Shankarapani and Srinivas Muk
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Namosha Veerasamy and Marthie Grobl
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4. Conclusion Namosha Veerasamy and
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Tanya Zlateva et al. Computer Infor
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Tanya Zlateva et al. security and v
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Tanya Zlateva et al. court opinions
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Tanya Zlateva et al. 5. Pedagogy, e
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PhD Research Papers 277
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Shada Alsalamah et al. Level 3 all
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Shada Alsalamah et al. assure the a
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Shada Alsalamah et al. for Health I
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3. Hematology Laboratory System Who
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Shada Alsalamah et al. Pirnejad, H.
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Michael Bilzor a diverse base of U.
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Michael Bilzor In our current exper
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5. Execution monitor theory Michael
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Michael Bilzor design was run in si
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Michael Bilzor over testbench metho
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Evan Dembskey and Elmarie Biermann
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Theoretical Offensive Cyber Militia
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Rain Ottis Last, but not least, it
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Rain Ottis an infantry battalion, w
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Rain Ottis Ottis, R. (2008) “Anal
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Work in Progress Papers 315
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Large-Scale Analysis of Continuous
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References William Acosta Abadi, D.
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Natarajan Vijayarangan top box unit
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Natarajan Vijayarangan The proposed
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6th European Conference - Academic Conferences

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