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David Merritt and Barry Mullins to the probability of cyber espionage, it is possible to use this score to measure the probability of the entire event as being cyber espionage. It is important to note that an individual event detected by itself may not express outright if a circumstance is cyber espionage-related or not. A targeted, socially-engineered intrusion might be a sophisticated spam or phishing attempt. New and undetected information-stealing malware could be a new variant of benign adware. A consistent transfer of significant amounts of encrypted data could end up being an authorized VPN (virtual private network) connection. The subtlety of a covert channel may be difficult to detect or declare with certainty its intentions, but it does serve as an impetus to investigate further to determine the context of the channel. The advantage behind this synthesis approach is that intrusion, malware, or exfiltration detection can be viewed within the context of the whole event. Not doing so could lead to incorrect conclusions being drawn from insufficient context. But each step of a cyber spy's attack methodology is not of equal value to the investigator. For instance, it is a challenge to judge the intent of malware simply by looking at its detectability and functionality. Many malicious programs have the same functionality but are used for different purposes. In fact, many legitimate programs are frequently used maliciously (e.g., Remote Administration Tools). On the contrary, a targeted intrusion that is industry-relevant hints at the intentions of the adversary—to quietly get to specific targets. Thus, the intrusion factor should be weighted more than the malware “factor”. Similarly, with data exfiltration detection, covertness of the channel and sensitivity of the data are significant factors affecting the characterization of espionage. These factors' weights should have more weight than the malware installation factor. This strategic weighting of indicators is integrated to establish the Espionage Probability Matrix (EPM) framework, shown in Table 1. The EPM is used to determine an EPM score based on varying degrees of espionage probability, as indicated by the three columns of High, Medium, and Low Probability. Each indicator is assigned a value associated with its column, with High, Medium, and Low indicators being assigned values of 3, 2, and 1, respectively. The values for the indicators (e.g., targeted and tailored network intrusion) within each factor (e.g., Intrusion) are averaged to provide an EPM score for that factor. For example, a network intrusion that is not targeted at a specific user/group but contains somewhat tailored content results in an Intrusion factor EPM score of 1.5. This is calculated by averaging the “Not targeted” indicator (i.e., 1) with the “Potentially tailored” indicator (i.e., 2). Table 1: Espionage Probability Matrix (EPM) Intrusion Malware Exfiltration High Probability Medium Probability Low Probability Targeted; specific victims Tailored; social engineering required Novel or unknown Advanced info/data- stealer Covert channel Custom encryption Significant data transfer Industry-specific Potentially targeted Potentially tailored; social engineering may be used Not well known; variant of known Info/data- stealer Attempts to hide channel Standard encryption Non-trivial data transfer Partially industry-specific Not targeted Well-known methods Well known Not info/data-stealer No attempt to hide channel Not encrypted Negligible data transfer Not industry-specific The Intrusion row has a α multiplier, where α > 1, to represent the relative importance of intrusion classification to the overall EPM score. The Exfiltration row has a β multiplier, where β > 1, to represent the relative importance of data exfiltration classification to the overall EPM score. This effectively assigns greater importance to the factors that deserve it, as discussed. These individual probabilities are brought into context of the entire event by calculating an overall EPM score using the following equation: EPM Score = α·Intrusion + Malware + β·Exfiltration 184
David Merritt and Barry Mullins Essentially, summing the individual weighted scores yields a “grade” for intrusion, malware, and exfiltration classification taken within the context of one another. For the purpose of this paper, a notional α multiplier of 2 and a β multiplier of 3 are used to illustrate the effectiveness and flexibility of this synthesis approach. Operationally, these values can be fine-tuned and adjusted as needed. However, this score has little value without a translation to what it could mean. The EPM score is used in the Espionage Threshold Matrix (ETM), shown in Table 2. Table 2: Espionage Threshold Matrix (ETM), assuming α=2 and β=3 Overall Probability of Cyber Espionage EPM Score High Probability ≥12 Medium Probability ≥9 Low Probability
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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. to see bo
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Mecealus Cronkrite et al. trivial.
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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
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Intelligence-Driven Computer Networ
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Eric Hutchins et al. of defensive a
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Eric Hutchins et al. Defenders can
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Eric Hutchins et al. Equally as imp
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Eric Hutchins et al. X-Mailer: Yaho
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Eric Hutchins et al. Received: (qma
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Eric Hutchins et al. U.S.-China Eco
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Saara Jantunen and Aki-Mauri Huhtin
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Page 179 and 180: Tree of Objectives Acknowledgements
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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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