There is limited research examining community and neighborhood influences on prosociality in children and youth. In this chapter we outline three relevant theories that address how neighborhood and community processes influence prosocial behavior and review the empirical literature on the topic. Our review suggests that measures of neighborhood socioeconomic status, demography, and disorder have little direct association with prosociality in children and youth but that adolescent prosocial behavior is linked to social capital and collective efficacy. The community intervention evidence shows that providing increased opportunities for prosocial involvement may support greater prosocial behavior of adolescents, possibly by boosting community social capital. Further development of more specific theoretical models and further empirical research is required to better understand the complex neighborhood and community mechanisms across neighborhoods, cities, nations, and cultures.

Despite the large investments in information security technologies and research over the past decades, the information security industry is still immature when it comes to vulnerability management. In particular, the prioritization of remediation efforts within vulnerability management programs predominantly relies on a mixture of subjective expert opinion and severity scores. Compounding the need for prioritization is the increase in the number of vulnerabilities the average enterprise has to remediate. This article describes the first open, data-driven framework for assessing vulnerability threat, that is, the probability that a vulnerability will be exploited in the wild within the first 12 months after public disclosure. This scoring system has been designed to be simple enough to be implemented by practitioners without specialized tools or software yet provides accurate estimates (ROC AUC = 0.838) of exploitation. Moreover, the implementation is flexible enough that it can be updated as more, and better, data becomes available. We call this system the Exploit Prediction Scoring System (EPSS).

While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern. In particular, ML models are often trained on data from potentially untrustworthy sources, providing adversaries with the opportunity to manipulate them by inserting carefully crafted samples into the training set. Recent work has shown that this type of attack, called a poisoning attack, allows adversaries to insert backdoors or trojans into the model, enabling malicious behavior with simple external backdoor triggers at inference time and only a blackbox perspective of the model itself. Detecting this type of attack is challenging because the unexpected behavior occurs only when a backdoor trigger, which is known only to the adversary, is present. Model users, either direct users of training data or users of pre-trained model from a catalog, may not guarantee the safe operation of their ML-based system. In this paper, we propose a novel approach to backdoor detection and removal for neural networks. Through extensive experimental results, we demonstrate its effectiveness for neural networks classifying text and images. To the best of our knowledge, this is the first methodology capable of detecting poisonous data crafted to insert backdoors and repairing the model that does not require a verified and trusted dataset.

Internet topology reflects economic and political constraints that change over time. Although autonomous systems (AS) topology has been measured and modeled for many years, focusing primarily on economic relationships, earlier studies have not quantified how topology is changing with respect to nation-state boundaries. National boundaries are natural points of control for surveillance, censorship, tariffs and data localization. This paper introduces a measure, national chokepoint potential (NCP), to characterize how a country's AS topology is organized in terms of BGP paths that can carry traffic across international borders. To study country-level chokepoints, we developed BGP-SAS, an open source, cross platform, efficient set of tools for simulating BGP routing and calculating national chokepoint measures. We use these tools to assess how AS topologies have changed over a ten-year span, finding significant variability among countries, with some increasing their chokepoint potential and others remaining constant, fluctuating, and in some cases declining. Overall, however, most national Internet boundaries have either become more pronounced or remained constant, despite new infrastructure buildouts and increased Internet usage. When compared to independent measures of Internet freedom, we find statistically significant relationships between NCP and Internet freedom.

Deep neural networks (DNNs) have been widely applied in various applications, including autonomous driving and surveillance systems. However, DNNs are found to be vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead a learner to make incorrect predictions. While several defense and detection approaches are proposed for static image classification, many security-critical tasks use videos as their input and require efficient processing. In this paper, we propose an efficient and effective method advIT to detect adversarial frames within videos against different types of attacks based on temporal consistency property of videos. In particular, we apply optical flow estimation to the target and previous frames to generate pseudo frames and evaluate the consistency of the learner output between these pseudo frames and target. High inconsistency indicates that the target frame is adversarial. We conduct extensive experiments on various learning tasks including video semantic segmentation, human pose estimation, object detection, and action recognition, and demonstrate that we can achieve above 95% adversarial frame detection rate. To consider adaptive attackers, we show that even if an adversary has access to the detector and performs a strong adaptive attack based on the state of the art expectation of transformation method, the detection rate stays almost the same. We also tested the transferability among different optical flow estimators and show that it is hard for attackers to attack one and transfer the perturbation to others. In addition, as efficiency is important in video analysis, we show that advIT can achieve real-time detection in about 0.03--0.4 seconds.

Machine learning architectures are readily available, but obtaining the high quality labeled data for training is costly.Pre-trained models available as cloud services can be used to generate this costly labeled data, and would allow an attacker to replicate trained models, effectively stealing them.Limiting the information provided by cloud based models by omitting class probabilities has been proposed as a means of protection but significantly impacts the utility of the models.In this work, we illustrate how cloud based models can still provide useful class probability information for users, while significantly limiting the ability of an adversary to steal the model.Our defense perturbs the model's final activation layer, slightly altering the output probabilities.This forces the adversary to discard the class probabilities, requiring significantly more queries before they can train a model with comparable performance.We evaluate our defense under diverse scenarios and defense aware attacks.Our evaluation shows our defense can degrade the accuracy of the stolen model at least 20%, or increase the number of queries required by an adversary 64 fold, all with a negligible decrease in the protected model accuracy.

Security practices in large organizations are notoriously difficult to assess. The challenge only increases when organizations turn to third parties to provide technology and business services, which typically require tight network integration and sharing of confidential data, potentially increasing the organization's attack surface. The security maturity of an organization describes how well it mitigates known risks and responds to new threats. Today, maturity is typically assessed with audits and questionnaires, which are difficult to quantify, lack objectivity, and may not reflect current threats. This paper demonstrates how external measurement of an organization can be used to assess the relative quality of security among organizations. Using a large dataset from BitSight(www.bitsight.com), a cybersecurity ratings company, containing 3.2 billion measurements spanning nearly 37,000 organizations collected during calendar year 2015, we show how per-organizational "risk vectors" can be constructed that may be related to an organization's overall security posture, or maturity. Using statistical analysis, we then study the correlation between the risk vectors and botnet infections. For example, we find that misconfigured TLS services, publicly available unsecured protocols, and the use of peer-to-peer file sharing correlate with organizations that have increased rates of botnet infections. We argue that the methodology used to identify these correlations can easily be applied to other data to provide a growing picture of organizational security using external measurement.

Adversarial examples have become an indisputable threat to the security of modern AI systems based on deep neural networks (DNNs). The Adversarial Robustness Toolbox (ART) is a Python library designed to support researchers and developers in creating novel defence techniques, as well as in deploying practical defences of real-world AI systems. Researchers can use ART to benchmark novel defences against the state-of-the-art. For developers, the library provides interfaces which support the composition of comprehensive defence systems using individual methods as building blocks. The Adversarial Robustness Toolbox supports machine learning models (and deep neural networks (DNNs) specifically) implemented in any of the most popular deep learning frameworks (TensorFlow, Keras, PyTorch). Currently, the library is primarily intended to improve the adversarial robustness of visual recognition systems, however, future releases that will comprise adaptations to other data modes (such as speech, text or time series) are envisioned. The ART source code is released (this https URL) under an MIT license. The release includes code examples and extensive documentation (this http URL) to help researchers and developers get quickly started.

Machine learning models are vulnerable to simple model stealing attacks if the adversary can obtain output labels for chosen inputs. To protect against these attacks, it has been proposed to limit the information provided to the adversary by omitting probability scores, significantly impacting the utility of the provided service. In this work, we illustrate how a service provider can still provide useful, albeit misleading, class probability information, while significantly limiting the success of the attack. Our defense forces the adversary to discard the class probabilities, requiring significantly more queries before they can train a model with comparable performance. We evaluate several attack strategies, model architectures, and hyperparameters under varying adversarial models, and evaluate the efficacy of our defense against the strongest adversary. Finally, we quantify the amount of noise injected into the class probabilities to mesure the loss in utility, e.g., adding 1.26 nats per query on CIFAR-10 and 3.27 on MNIST. Our evaluation shows our defense can degrade the accuracy of the stolen model at least 20%, or require up to 64 times more queries while keeping the accuracy of the protected model almost intact.

Cyber conflict is now a common and potentially dangerous occurrence. The target typically faces a strategic choice based on its ability to attribute the attack to a specific perpetrator and whether it has a viable punishment at its disposal. We present a game-theoretic model, in which the best strategic choice for the victim depends on the vulnerability of the attacker, the knowledge level of the victim, payoffs for different outcomes, and the beliefs of each player about their opponent. The resulting blame game allows analysis of four policy-relevant questions: the conditions under which peace (i.e., no attacks) is stable, when attacks should be tolerated, the consequences of asymmetric technical attribution capabilities, and when a mischievous third party or an accident can undermine peace. Numerous historical examples illustrate how the theory applies to cases of cyber or kinetic conflict involving the United States, Russia, China, Japan, North Korea, Estonia, Israel, Iran, and Syria.

Metabolic rate in animals and power consumption in computers are analogous quantities that scale similarly with size. We analyse vascular systems of mammals and on-chip networks of microprocessors, where natural selection and human engineering, respectively, have produced systems that minimize both energy dissipation and delivery times. Using a simple network model that simultaneously minimizes energy and time, our analysis explains empirically observed trends in the scaling of metabolic rate in mammals and power consumption and performance in microprocessors across several orders of magnitude in size. Just as the evolutionary transitions from unicellular to multicellular animals in biology are associated with shifts in metabolic scaling, our model suggests that the scaling of power and performance will change as computer designs transition to decentralized multi-core and distributed cyber-physical systems. More generally, a single energy–time minimization principle may govern the design of many complex systems that process energy, materials and information. This article is part of the themed issue ‘The major synthetic evolutionary transitions’.

Recent widely publicized data breaches have exposed thepersonal information of hundreds of millions of people. Somereports point to alarming increases in both the size and fre-quency of data breaches, spurring institutions around theworld to address what appears to be a worsening situation.But, is the problem actually growing worse? In this paper,we study a popular public dataset and develop BayesianGeneralized Linear Models to investigate trends in databreaches. Analysis of the model shows that neither sizenor frequency of data breaches has increased over the pastdecade. We nd that the increases that have attracted at-tention can be explained by the heavy-tailed statistical dis-tributions underlying the dataset. Speci cally, we nd thatdata breach size is log-normally distributed and that thedaily frequency of breaches is described by a negative bi-nomial distribution. These distributions may provide cluesto the generative mechanisms that are responsible for thebreaches. Additionally, our model predicts the likelihood ofbreaches of a particular size in the future. For example, we nd that in the next year there is only a 31% chance of abreach of 10 million records or more in the US. Regardlessof any trend, data breaches are costly, and we combine themodel with two di erent cost models to project that in thenext three years breaches could cost up to $55 billion.

Many cybersecurity problems occur on a worldwide scale, but we lack rigorous methods for determining how best to intervene and mitigate damage globally, both short- and long-term. Analysis of longitudinal security data can provide insight into the effectiveness and differential impacts of security interventions on a global level. In this paper we consider the example of spam, studying a large high-resolution data set of messages sent from 260 ISPs in 60 countries over the course of a decade. The statistical analysis is designed to avoid common pitfalls that could lead to erroneous conclusions. We show how factors such as geography, national economics, Internet connectivity and traffic flow impact can affect local spam concentrations. Additionally, we present a statistical model to study temporal transitions in the dataset, and we use a simple extension of the model to investigate the effect of historical botnet takedowns on spam levels. We find that in aggregate most historical takedowns are beneficial in the short-term, but few have long-term impact. Further, even when takedowns are effective globally, they can be detrimental in specific geographic regions or countries. The analysis and modeling described here are based on a single data set. However, the techniques are general and could be adapted to other data sets to help improve decision making about when and how to deploy security interventions.

A panel at the New Security Paradigms Workshop (2014) discussed the topic of regulation and licensing of software developers and information security professionals. This included topics of the current state of certification, future possibilities, and challenges associated with new forms of regulation. This paper presents a brief background on the subject, three opinions presented by the panelists, and finally a summary of the discussion which occurred at the workshop, including input from both the panelists and the workshop attendees.

Participatory sensing applications rely on individuals to share personal data to produce aggregated models and knowledge. In this setting, privacy concerns can discourage widespread adoption of new applications. We present a privacy-preserving participatory sensing scheme based on negative surveys for both continuous and multivariate categorical data. Without relying on encryption, our algorithms enhance the privacy of sensed data in an energy and computation efficient manner. Simulations and implementation on Android smart phones illustrate how multidimensional data can be aggregated in a useful and privacy-enhancing manner.

Malware spread among websites and between websites and clients is an increasing problem. Search engines play an important role in directing users to websites and are a natural control point for intervening using mechanisms such as blacklisting. The paper presents a simple Markov model of malware spread through large populations of websites and studies the effect of two interventions that might be deployed by a search provider: blacklisting infected web pages by removing them from search results entirely and a generalization of blacklisting, called depreferencing, in which a website's ranking is decreased by a fixed percentage each time period the site remains infected. We analyze and study the trade-offs between infection exposure and traffic loss due to false positives (the cost to a website that is incorrectly blacklisted) for different interventions. As expected, we find that interventions are most effective when websites are slow to remove infections. Surprisingly, we also find that low infection or recovery rates can increase traffic loss due to false positives. Our analysis also shows that heavy-tailed distributions of website popularity, as documented in many studies, leads to high sample variance of all measured outcomes. This result implies that it will be difficult to determine empirically whether certain website interventions are effective, and it suggests that theoretical models such as the one described in this paper have an important role to play in improving web security.

Malware spread among websites and between websites and clients is an increasing problem. Search engines play an important role in directing users to websites and are a natural control point for intervening using mechanisms such as blacklisting. The paper presents a simple Markov model of malware spread through large populations of websites and studies the effect of two interventions that might be deployed by a search provider: blacklisting infected web pages by removing them from search results entirely and a generalization of blacklisting, called depreferencing, in which a website's ranking is decreased by a fixed percentage each time period the site remains infected. We analyze and study the trade-offs between infection exposure and traffic loss due to false positives (the cost to a website that is incorrectly blacklisted) for different interventions. As expected, we find that interventions are most effective when websites are slow to remove infections. Surprisingly, we also find that low infection or recovery rates can increase traffic loss due to false positives. Our analysis also shows that heavy-tailed distributions of website popularity, as documented in many studies, leads to high sample variance of all measured outcomes. This result implies that it will be difficult to determine empirically whether certain website interventions are effective, and it suggests that theoretical models such as the one described in this paper have an important role to play in improving web security.

There are few studies that look closely at how the topology of the Internet evolves over time; most focus on snapshots taken at a particular point in time. In this paper, we investigate the evolution of the topology of the Autonomous Systems graph of the Internet, examining how eight commonly-used topological measures change from January 2002 to January 2010. We find that the distributions of most of the measures remain unchanged, except for average path length and clustering coefficient. The average path length has slowly and steadily increased since 2005 and the average clustering coefficient has steadily declined. We hypothesize that these changes are due to changes in peering policies as the Internet evolves. We also investigate a surprising feature, namely that the maximum degree has changed little, an aspect that cannot be captured without modeling link deletion. Our results suggest that evaluating models of the Internet graph by comparing steady-state generated topologies to snapshots of the real data is reasonable for many measures. However, accurately matching time-variant properties is more difficult, as we demonstrate by evaluating ten well-known models against the 2010 data.

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