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4. DARKMENTION: A Deployed System to Predict Enterprise-Targeted External Cyberattacks

Author

Almukaynizi, Mohammed, Marin, Ericsson, Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., Kapoor, Dipsy, and Siedlecki, Timothy

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

Recent incidents of data breaches call for organizations to proactively identify cyber attacks on their systems. Darkweb/Deepweb (D2web) forums and marketplaces provide environments where hackers anonymously discuss existing vulnerabilities and commercialize malicious software to exploit those vulnerabilities. These platforms offer security practitioners a threat intelligence environment that allows to mine for patterns related to organization-targeted cyber attacks. In this paper, we describe a system (called DARKMENTION) that learns association rules correlating indicators of attacks from D2web to real-world cyber incidents. Using the learned rules, DARKMENTION generates and submits warnings to a Security Operations Center (SOC) prior to attacks. Our goal was to design a system that automatically generates enterprise-targeted warnings that are timely, actionable, accurate, and transparent. We show that DARKMENTION meets our goal. In particular, we show that it outperforms baseline systems that attempt to generate warnings of cyber attacks related to two enterprises with an average increase in F1 score of about 45% and 57%. Additionally, DARKMENTION was deployed as part of a larger system that is built under a contract with the IARPA Cyber-attack Automated Unconventional Sensor Environment (CAUSE) program. It is actively producing warnings that precede attacks by an average of 3 days.

Published
2018

6. Cognitively-Inspired Inference for Malware Task Identification

Author

Nunes, Eric, Buto, Casey, Shakarian, Paulo, Lebiere, Christian, Bennati, Stefano, Thomson, Robert, Alhajj, Reda, Series Editor, Glässer, Uwe, Series Editor, Liu, Huan, Series Editor, Wittek, Rafael, Series Editor, Zeng, Daniel, Series Editor, Aggarwal, Charu C., Advisory Editor, Brantingham, Patricia L., Advisory Editor, Gross, Thilo, Advisory Editor, Han, Jiawei, Advisory Editor, Manásevich, Raúl, Advisory Editor, Masys, Anthony J., Advisory Editor, Morselli, Carlo, Advisory Editor, Tayebi, Mohammad A., editor, and Skillicorn, David B., editor

Published
2020
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8. Darknet and Deepnet Mining for Proactive Cybersecurity Threat Intelligence

Author

Nunes, Eric, Diab, Ahmad, Gunn, Andrew, Marin, Ericsson, Mishra, Vineet, Paliath, Vivin, Robertson, John, Shakarian, Jana, Thart, Amanda, and Shakarian, Paulo

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence, Computer Science - Computers and Society
Abstract

In this paper, we present an operational system for cyber threat intelligence gathering from various social platforms on the Internet particularly sites on the darknet and deepnet. We focus our attention to collecting information from hacker forum discussions and marketplaces offering products and services focusing on malicious hacking. We have developed an operational system for obtaining information from these sites for the purposes of identifying emerging cyber threats. Currently, this system collects on average 305 high-quality cyber threat warnings each week. These threat warnings include information on newly developed malware and exploits that have not yet been deployed in a cyber-attack. This provides a significant service to cyber-defenders. The system is significantly augmented through the use of various data mining and machine learning techniques. With the use of machine learning models, we are able to recall 92% of products in marketplaces and 80% of discussions on forums relating to malicious hacking with high precision. We perform preliminary analysis on the data collected, demonstrating its application to aid a security expert for better threat analysis., Comment: 6 page paper accepted to be presented at IEEE Intelligence and Security Informatics 2016 Tucson, Arizona USA September 27-30, 2016

Published
2016

9. Argumentation Models for Cyber Attribution

Author

Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., and Ruef, Andrew

Subjects
Computer Science - Artificial Intelligence
Abstract

A major challenge in cyber-threat analysis is combining information from different sources to find the person or the group responsible for the cyber-attack. It is one of the most important technical and policy challenges in cyber-security. The lack of ground truth for an individual responsible for an attack has limited previous studies. In this paper, we take a first step towards overcoming this limitation by building a dataset from the capture-the-flag event held at DEFCON, and propose an argumentation model based on a formal reasoning framework called DeLP (Defeasible Logic Programming) designed to aid an analyst in attributing a cyber-attack. We build models from latent variables to reduce the search space of culprits (attackers), and show that this reduction significantly improves the performance of classification-based approaches from 37% to 62% in identifying the attacker., Comment: 8 pages paper to be presented at International Symposium on Foundations of Open Source Intelligence and Security Informatics (FOSINT-SI) 2016 In conjunction with ASONAM 2016 San Francisco, CA, USA, August 19-20, 2016

Published
2016

10. Malware Task Identification: A Data Driven Approach

Author

Nunes, Eric, Buto, Casey, Shakarian, Paulo, Lebiere, Christian, Bennati, Stefano, Thomson, Robert, and Jaenisch, Holger

Subjects
Computer Science - Cryptography and Security
Abstract

Identifying the tasks a given piece of malware was designed to perform (e.g. logging keystrokes, recording video, establishing remote access, etc.) is a difficult and time-consuming operation that is largely human-driven in practice. In this paper, we present an automated method to identify malware tasks. Using two different malware collections, we explore various circumstances for each - including cases where the training data differs significantly from test; where the malware being evaluated employs packing to thwart analytical techniques; and conditions with sparse training data. We find that this approach consistently out-performs the current state-of-the art software for malware task identification as well as standard machine learning approaches - often achieving an unbiased F1 score of over 0.9. In the near future, we look to deploy our approach for use by analysts in an operational cyber-security environment., Comment: 8 pages full paper, accepted FOSINT-SI (2015)

Published
2015

11. Cyber-Deception and Attribution in Capture-the-Flag Exercises

Author

Nunes, Eric, Kulkarni, Nimish, Shakarian, Paulo, Ruef, Andrew, and Little, Jay

Subjects
Computer Science - Cryptography and Security
Abstract

Attributing the culprit of a cyber-attack is widely considered one of the major technical and policy challenges of cyber-security. The lack of ground truth for an individual responsible for a given attack has limited previous studies. Here, we overcome this limitation by leveraging DEFCON capture-the-flag (CTF) exercise data where the actual ground-truth is known. In this work, we use various classification techniques to identify the culprit in a cyberattack and find that deceptive activities account for the majority of misclassified samples. We also explore several heuristics to alleviate some of the misclassification caused by deception., Comment: 4 pages Short name accepted to FOSINT-SI 2015

Published
2015

12. Enhanced Data Collection for Cyber Attribution

Author

Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., Ruef, Andrew, Zdonik, Stan, Series editor, Shekhar, Shashi, Series editor, Wu, Xindong, Series editor, Jain, Lakhmi C., Series editor, Padua, David, Series editor, Shen, Xuemin Sherman, Series editor, Furht, Borko, Series editor, Subrahmanian, V.S., Series editor, Hebert, Martial, Series editor, Ikeuchi, Katsushi, Series editor, Siciliano, Bruno, Series editor, Jajodia, Sushil, Series editor, Lee, Newton, Series editor, Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., and Ruef, Andrew

Published
2018
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13. Belief Revision in DeLP3E

Author

Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., Ruef, Andrew, Zdonik, Stan, Series editor, Shekhar, Shashi, Series editor, Wu, Xindong, Series editor, Jain, Lakhmi C., Series editor, Padua, David, Series editor, Shen, Xuemin Sherman, Series editor, Furht, Borko, Series editor, Subrahmanian, V.S., Series editor, Hebert, Martial, Series editor, Ikeuchi, Katsushi, Series editor, Siciliano, Bruno, Series editor, Jajodia, Sushil, Series editor, Lee, Newton, Series editor, Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., and Ruef, Andrew

Published
2018
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14. Argumentation-Based Cyber Attribution: The DeLP3E Model

Author

Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., Ruef, Andrew, Zdonik, Stan, Series editor, Shekhar, Shashi, Series editor, Wu, Xindong, Series editor, Jain, Lakhmi C., Series editor, Padua, David, Series editor, Shen, Xuemin Sherman, Series editor, Furht, Borko, Series editor, Subrahmanian, V.S., Series editor, Hebert, Martial, Series editor, Ikeuchi, Katsushi, Series editor, Siciliano, Bruno, Series editor, Jajodia, Sushil, Series editor, Lee, Newton, Series editor, Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., and Ruef, Andrew

Published
2018
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15. Applying Argumentation Models for Cyber Attribution

Author

Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., Ruef, Andrew, Zdonik, Stan, Series editor, Shekhar, Shashi, Series editor, Wu, Xindong, Series editor, Jain, Lakhmi C., Series editor, Padua, David, Series editor, Shen, Xuemin Sherman, Series editor, Furht, Borko, Series editor, Subrahmanian, V.S., Series editor, Hebert, Martial, Series editor, Ikeuchi, Katsushi, Series editor, Siciliano, Bruno, Series editor, Jajodia, Sushil, Series editor, Lee, Newton, Series editor, Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., and Ruef, Andrew

Published
2018
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16. Baseline Cyber Attribution Models

Author

Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., Ruef, Andrew, Zdonik, Stan, Series editor, Shekhar, Shashi, Series editor, Wu, Xindong, Series editor, Jain, Lakhmi C., Series editor, Padua, David, Series editor, Shen, Xuemin Sherman, Series editor, Furht, Borko, Series editor, Subrahmanian, V.S., Series editor, Hebert, Martial, Series editor, Ikeuchi, Katsushi, Series editor, Siciliano, Bruno, Series editor, Jajodia, Sushil, Series editor, Lee, Newton, Series editor, Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., and Ruef, Andrew

Published
2018
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20. Introduction

Author

Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., Ruef, Andrew, Zdonik, Stan, Series editor, Shekhar, Shashi, Series editor, Wu, Xindong, Series editor, Jain, Lakhmi C., Series editor, Padua, David, Series editor, Shen, Xuemin Sherman, Series editor, Furht, Borko, Series editor, Subrahmanian, V.S., Series editor, Hebert, Martial, Series editor, Ikeuchi, Katsushi, Series editor, Siciliano, Bruno, Series editor, Jajodia, Sushil, Series editor, Lee, Newton, Series editor, Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., and Ruef, Andrew

Published
2018
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26. Conclusion

Author

Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., Ruef, Andrew, Zdonik, Stan, Series editor, Shekhar, Shashi, Series editor, Wu, Xindong, Series editor, Jain, Lakhmi C., Series editor, Padua, David, Series editor, Shen, Xuemin Sherman, Series editor, Furht, Borko, Series editor, Subrahmanian, V.S., Series editor, Hebert, Martial, Series editor, Ikeuchi, Katsushi, Series editor, Siciliano, Bruno, Series editor, Jajodia, Sushil, Series editor, Lee, Newton, Series editor, Nunes, Eric, Shakarian, Paulo, Simari, Gerardo I., and Ruef, Andrew

Published
2018
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34. Introduction

Author

Nunes, Eric, primary, Shakarian, Paulo, additional, Simari, Gerardo I., additional, and Ruef, Andrew, additional

Published
2018
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35. Conclusion

Author

Nunes, Eric, primary, Shakarian, Paulo, additional, Simari, Gerardo I., additional, and Ruef, Andrew, additional

Published
2018
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39. The Behavioral Pharmacology of Effort-Related Choice Behavior: Dopamine, Adenosine and beyond

Author

Salamone, John D., Correa, Merce, and Nunes, Eric J.

Abstract

For many years, it has been suggested that drugs that interfere with dopamine (DA) transmission alter the "rewarding" impact of primary reinforcers such as food. Research and theory related to the functions of mesolimbic DA are undergoing a substantial conceptual restructuring, with the traditional emphasis on hedonia and primary reward yielding to other concepts and lines of inquiry. The present review is focused upon the involvement of nucleus accumbens DA in effort-related choice behavior. Viewed from the framework of behavioral economics, the effects of accumbens DA depletions and antagonism on food-reinforced behavior are highly dependent upon the work requirements of the instrumental task, and DA depleted rats show a heightened sensitivity to response costs, especially ratio requirements. Moreover, interference with accumbens DA transmission exerts a powerful influence over effort-related choice behavior. Rats with accumbens DA depletions or antagonism reallocate their instrumental behavior away from food-reinforced tasks that have high response requirements, and show increased selection of low reinforcement/low cost options. Nucleus accumbens DA and adenosine interact in the regulation of effort-related functions, and other brain structures (anterior cingulate cortex, amygdala, ventral pallidum) also are involved. Studies of the brain systems regulating effort-based processes may have implications for understanding drug abuse, as well as symptoms such as psychomotor slowing, fatigue or anergia in depression and other neurological disorders. (Contains 1 table and 3 figures.)

Published
2012

48. Exploring Malicious Hacker Communities

Author

Marin, Ericsson, primary, Almukaynizi, Mohammed, additional, Sarkar, Soumajyoti, additional, Nunes, Eric, additional, Shakarian, Jana, additional, Shakarian, Paulo, additional, and Amoroso, Edward G., additional

Published
2021
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50. Evaluation of Flavor Effects on Oral Nicotine Liking and/or Disliking Using the Taste Reactivity Test in Rats.

Author

Bagdas, Deniz, Rupprecht, Laura E, Nunes, Eric J, Schillinger, Emma, Immanuel, Judah J, and Addy, Nii A

Subjects
NICOTINE, FLAVOR, TASTE, SPRAGUE Dawley rats, RATS
Abstract

Introduction: Tobacco product flavors may change the sensory properties of nicotine, such as taste and olfactory cues, which may alter nicotine reward and aversion and nicotine taking behavior. The hedonic or aversive value of a taste stimulus can be evaluated by examining affective orofacial movements in rodents.Aims and Methods: We characterized taste responses to various oral nicotine concentrations using the taste reactivity test in rats. We also evaluated the impact of menthol and benzaldehyde (cherry, almond) flavorants on both ingestive and aversive responses to oral nicotine. Adult Sprague-Dawley rats (n = 5-10 per sex per group) were implanted with intraoral catheters and received 20 infusions (200 µl/ea). Nicotine (1-100 µg/mL) was evaluated in taste reactivity test to determine taste responses to nicotine. Later, the effects of menthol (50 µg/mL) and benzaldehyde (100 µg/mL) on the taste responses to nicotine were determined.Results: Nicotine at low concentrations (3 µg/mL in males, 1 µg/mL in females) elicited significantly greater ingestive responses compared with water, whereas higher nicotine concentrations (≥30 µg/mL in males, ≥10 µg/mL in females) elicited significant aversive reactions. Thus, intraoral nicotine induced both hedonic and aversive responses in a concentration- and sex-dependent manner. Females were more sensitive to nicotine's concentration. The addition of menthol or benzaldehyde significantly increased the hedonic responses to nicotine, and significantly decreased the aversive nicotine responses.Conclusions: Oral nicotine induces both hedonic and aversive taste responses, which may represent liking and disliking. Menthol and benzaldehyde can alter the orosensory experience of nicotine, which may influence nicotine's abuse liability.Implications: Our work represents a model to study impact of flavors on oral nicotine liking and disliking responses in rats. Moreover, our findings show that menthol and benzaldehyde alter the orosensory experience of nicotine, suggesting that both could influence nicotine's abuse liability. [ABSTRACT FROM AUTHOR]

Published
2022
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