Computational Analysis of Terrorist Groups: Lashkar-e-Taiba

Subrahmanian, V. S.; Mannes, Aaron; Sliva, Amy; Shakarian, Jana; Dickerson, John P.

doi:10.1007/978-1-4614-4769-6

Cited by 36 publications

(27 citation statements)

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“…The ability to classify objects of interest from a training set, whether those objects are terrorists (1), machines that need maintenance (2), or emails containing a malicious link (3), represents the greatest success in the field. Typically, no single machine learning algorithm does everything well.…”

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confidence: 99%

Predicting human behavior: The next frontiers

Subrahmanian

Kumar

2017

Science

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Machine learning has provided researchers with new tools for understanding human behavior. In this article, we briefly describe some successes in predicting behaviors and describe the challenges over the next few years.A dvances in machine learning are revolutionizing how we understand offline and online human behavior. The ability to classify objects of interest from a training set, whether those objects are terrorists (1), machines that need maintenance (2), or emails containing a malicious link (3), represents the greatest success in the field. Typically, no single machine learning algorithm does everything well. Although accuracy is crucial, the acceptable accuracy varies with the problem being studied, and accuracy is not enough. All too often, researchers explain why their predictions are right but say nothing about why their predictions might be wrong. Knowing both enables decision-makers to make better decisions. Especially in high-risk situations, predictions must have accompanying explanations that provide deeper understanding of the situation being studied. A predictive model must also provide one or more prescriptions for potential future actions that enable decision-makers to make better decisions. Today's machine learning methods do not necessarily satisfy these three criteria. What constitutes an ideal predictive algorithm depends on the application. Oftentimes, stakeholders (e.g., social media platforms and search engines) will use varying definitions of accuracy that meet their particular needs. Moreover, domain experts may use extensive knowledge of the domain to suggest relevant independent variables to be included in a data set. Often, they will explain predictions using both the technical accuracy measures generated by a predictive model and stories from their discipline that are more understandable to their audiences. All of this suggests that in real-world systems, computer scientists need to team with stakeholders to generate high-impact results.In our opinion, the next generation of predictive models must deal with four major challenges.First, the maxim that more data lead to better predictive models is not always true, because noise in the data can overwhelm predictive models. The ability to deal with noisy, incomplete, and inconsistent data will be at the heart of nextgeneration predictive models. For instance, when identifying "bots" on Twitter (4) that are seeking to sway opinion to be positive about a political candidate, we needed to ignore the huge numbers of bots that were seeking to achieve other ends-such as spreading spam or seeking to influence opinions about other topics or to deceive users into clicking on links that generate revenue for the person who included that link in their tweet. Moreover, data about many Twitter handles are limited and, in some cases, intentionally misleading. Bot developers go to considerable effort to ensure that their bots elude detection.A second challenge is that of rare-event prediction. For instance, companies monitoring their internal networ...

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confidence: 99%

Predicting human behavior: The next frontiers

Subrahmanian

Kumar

2017

Science

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“…The first dataset is synthetic dataset created by the authors. The second and third datasets are Sageman's Al-Qaeda network Sageman (2004) and Lashkar-e-Taiba network Subrahmanian et al (2012) respectively. The results show that STONE-Predict algorithm achieves 80% accuracy of predicting who will replace the removed person in the network when tested on those datasets.…”

Section: Ranking Individuals and Relationships In Crime Networkmentioning

confidence: 99%

A Survey of Social Network Forensics

Karabiyik¹,

Canbaz²,

Aksoy³

et al. 2016

JDFSL

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Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models/techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks.

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“…Such rules can be extracted from the behavioral or spatial data previously mentioned (Khuller, Martinez, Nau, Simari, Sliva, & Subrahmanian, 2007). Figure 2 contains a sample of a probabilistic logic representation of some behaviors of a terror organization like LeT, indicating the probability of violent actions as a result of situational factors in the environment (Subrahmanian, Mannes, Sliva, Shakarian, & Dickerson, 2012). The first line says that if the group is experiencing conflict with another rival organization (i.e., interorganizational conflict), then the probability that it will use kidnapping as a strategy lies between 0.35 and 0.45.…”

Section: Abductive Reasoningmentioning

confidence: 99%