Taxonomy Of Threats Research Articles

A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems

As the world moves towards being increasingly dependent on computers and automation, building secure applications, systems and networks are some of the main challenges faced in the current decade. The number of threats that individuals and businesses face is rising exponentially due to the increasing complexity of networks and services of modern networks. To alleviate the impact of these threats, researchers have proposed numerous solutions for anomaly detection; however, current tools often fail to adapt to ever-changing architectures, associated threats and zero-day attacks. This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats. To this end, this manuscript provides researchers with two key pieces of information; a survey of prominent datasets, analyzing their use and impact on the development of the past decade's Intrusion Detection Systems (IDS) and a taxonomy of network threats and associated tools to carry out these attacks. The manuscript highlights that current IDS research covers only 33.3% of our threat taxonomy. Current datasets demonstrate a clear lack of real-network threats, attack representation and include a large number of deprecated threats, which together limit the detection accuracy of current machine learning IDS approaches. The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data. As a result, this will improve the efficiency of the next generation IDS and reflect network threats more accurately within new datasets.

Characterizing and Evaluating Adversarial Examples for Offline Handwritten Signature Verification

The phenomenon of Adversarial Examples is attracting increasing interest from the Machine Learning community, due to its significant impact to the security of Machine Learning systems. Adversarial examples are similar (from a perceptual notion of similarity) to samples from the data distribution, that "fool" a machine learning classifier. For computer vision applications, these are images with carefully crafted but almost imperceptible changes, that are misclassified. In this work, we characterize this phenomenon under an existing taxonomy of threats to biometric systems, in particular identifying new attacks for Offline Handwritten Signature Verification systems. We conducted an extensive set of experiments on four widely used datasets: MCYT-75, CEDAR, GPDS-160 and the Brazilian PUC-PR, considering both a CNN-based system and a system using a handcrafted feature extractor (CLBP). We found that attacks that aim to get a genuine signature rejected are easy to generate, even in a limited knowledge scenario, where the attacker does not have access to the trained classifier nor the signatures used for training. Attacks that get a forgery to be accepted are harder to produce, and often require a higher level of noise - in most cases, no longer "imperceptible" as previous findings in object recognition. We also evaluated the impact of two countermeasures on the success rate of the attacks and the amount of noise required for generating successful attacks.

Playing With Danger: A Taxonomy and Evaluation of Threats to Smart Toys

Smart toys have captured an increasing share of the toy market, and are growing ubiquitous in households with children. Smart toys are a subset of Internet of Things (IoT) devices, containing sensors, actuators, and/or artificial intelligence capabilities. They frequently have internet connectivity, directly or indirectly through companion apps, and collect information about their users and environments. Recent studies have found security flaws in many smart toys that have led to serious privacy leaks, or allowed tracking a child's physical location. Some well-publicized discoveries of this nature have prompted actions from governments around the world to ban some of these toys. Compared to other IoT devices, smart toys pose unique risks because of their easily-vulnerable user base, and our work is intended to define these risks and assess a subset of toys against them. We provide a classification of threats specific to smart toys in order to unite and complement existing adhoc analyses, and help comprehensive evaluation of other smart toys. Our threat classification framework addresses the potential security and privacy flaws that can lead to leakage of private information or allow an adversary to control the toy to lure, harm, or distress a child. Using this framework, we perform a thorough experimental analysis of eleven smart toys and their companion apps. Our systematic analysis has uncovered that several current toys still expose children to multiple threats for attackers with physical, nearby, or remote access to the toy.

Towards a taxonomy of cyber threats against target applications

As the adoption of ubiquitous computing is growing with rapid pace, its security is becoming a major concern due to nimble and creative cyber attackers. The evolving threats are continuously challenging and reminding security professionals as well as corporations to come up with interconnected and integrated approach to security. In this paper, Taxonomy of Cyber Threats is prepared in terms of Industrial, OS, Mobile OS, Web Browser, Network and Social Network applications which will assist Information Security bodies such as CERT in describing and categorizing the attacks consistently. The proposed taxonomy consists of eight aspects which effectively comprehends the taxonomy in order to deal with the intrinsic issues in different target sectors. For better manifestation, the taxonomy is applied to some well known cyber threats. Finally, an analysis of Cyber Threats is conducted for that Symantec Threat database is used and some important findings are presented.

Privacy Concerns in Online Social Networks: A Users’ Perspective

Social networking has elevated the human life to the heights of interaction, response and content sharing. It has been offering state of the art facilities to its users for a long time. Though, over the period of time, the systems have become quite matured yet alongside the benefits, multiple concerns of the user with regard to the privacy and information security also exist. Multidimensional threat spectrum to the Internet has also been posed to social networking tools. A lot of work is being done to un-derstand privacy concerns in social networks. In this scenario, a survey of privacy concerns in online social networks is conducted. Risks, privacy issues, and threats have been highlighted that occurred in recent years, analyzing the targets of attackers, their methods of attack and measures taken to counter/manage these threats are the focus. A social network depends on the user, social network site/application and communication medium provider i.e. the Internet facility. Existing research contains domain specific re-search work regarding privacy issues in social networks; however, a comprehensive research work related to overall infrastructure of online social networks is missing. Development of a taxonomy of threats and categorization of frauds relevant to social networks is an important contribution of this survey. After completing a comprehensive research survey on privacy concerns in online social networks, a set of privacy guidelines is provided and open research challenges are highlighted.

Smart Grid Metering Networks: A Survey on Security, Privacy and Open Research Issues

Smart grid (SG) networks are newly upgraded networks of connected objects that greatly improve reliability, efficiency, and sustainability of the traditional energy infrastructure. In this respect, the smart metering infrastructure (SMI) plays an important role in controlling, monitoring, and managing multiple domains in the SG. Despite the salient features of SMI, security, and privacy issues have been under debate because of the large number of heterogeneous devices that are anticipated to be coordinated through public communication networks. This survey paper shows a brief overview of real cyber attack incidents in traditional energy networks and those targeting the smart metering network. Specifically, we present a threat taxonomy considering: 1) threats in system-level security; 2) threats and/or theft of services; and 3) threats to privacy. Based on the presented threats, we derive a set of security and privacy requirements for SG metering networks. Furthermore, we discuss various schemes that have been proposed to address these threats, considering the pros and cons of each. Finally, we investigate the open research issues to shed new light on future research directions in SG metering networks.

Applying the Taxonomy of Validity Threats from Mainstream Research Design to Single-Case Experiments in Applied Behavior Analysis.

Mainstream research design in the social and behavioral sciences has often been conceptualized using a taxonomy of threats to experimental validity first articulated by Campbell and his colleagues (Campbell & Stanley, 1966; Cook & Campbell, 1979). The most recent update of this framework was published by Shadish, Cook, and Campbell (2002), in which the authors describe different types of validity and numerous threats to each primarily in terms of group-design experiments. In the present article, we apply Shadish et al.'s analysis of threats to internal, external, statistical conclusion, and construct validity to single-case experimental research as it is typically conducted in applied behavior analysis. In doing so, we hope to provide researchers and educators in the field with a translation of the validity-threats taxonomy into terms and considerations relevant to the design and interpretation of applied behavior-analytic research for the purposes of more careful research design and the ability to communicate our designs to individuals outside of behavior analysis, using their own vocabulary.

NFV Security Survey: From Use Case Driven Threat Analysis to State-of-the-Art Countermeasures

Network functions virtualization (NFV), along with software-defined networking (SDN), drives a new change in networking infrastructure with respect to designing, deploying, and managing various network services. In particular, NFV has potential to significantly reduce the hardware cost, greatly improve operational efficiency, and dramatically shorten the development lifecycle of network service. It also makes network functions and services much more adaptive and scalable. Despite the promising advantages of NFV, security remains to be one of the vital concerns and potential hurdle, as attack surface becomes unclear and defense line turns to be blurred in the virtualization environment. This survey is therefore devoted to analyzing NFV from a security perspective. We first analyze security threats of five well-defined NFV use cases, with an objective to establishing a comprehensive layer-specific threat taxonomy. Second, we conduct in-depth comparative studies on several security mechanisms that are applied in traditional scenarios and in NFV environments. The purpose is to analyze their implicit relationships with NFV performance objectives in terms of feasibility, agility, effectiveness, and so on. Third, based on the established threat taxonomy and the analyzed security mechanisms, we provide a set of recommendations on securing NFV based services, along with the analysis on the state-of-the-art security countermeasures. A resulting holistic security framework is intended to lay a foundation for NFV service providers to deploy adaptive, scalable, and cost-effective security hardening based on their particular needs. Some future research directions are finally discussed.

A new taxonomy of insider threats: an initial step in understanding authorised attack

Insider threat represents one of the greatest challenges in the cyber security world. Insider attackers have more privileged and legitimate access to the information and facilities, compared to the outsider attackers. In fact, insider attacker has more accessibilities and higher potential to bring huge damage to the organisation. However, the behaviour of the insider attacker generates many questions to ponder before a new taxonomy is created. Therefore, the main objective of this paper is two-fold: a) to classify the insider threat for better understanding; b) propose a new taxonomy for insider threat with terminologies. To obtain the objective, the process starts with collecting and classifying the evident. Then, this study presents a hybrid insider threat classification based on combining insider threat access, motivation, indicator, types and actions, profile categorisation, methods, and detection techniques. With the insights afforded by looking more closely at conceptual understanding, we describe how classification of insider threat may effectively be used in insider threat detection.

Cyber-Security Incidents: A Review Cases in Cyber-Physical Systems

Cyber-Physical Systems refer to systems that have an interaction between computers, communication channels and physical devices to solve a real-world problem. Towards industry 4.0 revolution, Cyber-Physical Systems currently become one of the main targets of hackers and any damage to them lead to high losses to a nation. According to valid resources, several cases reported involved security breaches on Cyber-Physical Systems. Understanding fundamental and theoretical concept of security in the digital world was discussed worldwide. Yet, security cases in regard to the cyber-physical system are still remaining less explored. In addition, limited tools were introduced to overcome security problems in Cyber-Physical System. To improve understanding and introduce a lot more security solutions for the cyber-physical system, the study on this matter is highly on demand. In this paper, we investigate the current threats on Cyber-Physical Systems and propose a classification and matrix for these threats, and conduct a simple statistical analysis of the collected data using a quantitative approach. We confirmed four components i.e., (the type of attack, impact, intention and incident categories) main contributor to threat taxonomy of Cyber-Physical Systems.

Cyber-Physical Systems Security—A Survey

With the exponential growth of cyber-physical systems (CPS), new security challenges have emerged. Various vulnerabilities, threats, attacks, and controls have been introduced for the new generation of CPS. However, there lack a systematic study of CPS security issues. In particular, the heterogeneity of CPS components and the diversity of CPS systems have made it very difficult to study the problem with one generalized model. In this paper, we capture and systematize existing research on CPS security under a unified framework. The framework consists of three orthogonal coordinates: (1) from the \emph{security} perspective, we follow the well-known taxonomy of threats, vulnerabilities, attacks and controls; (2)from the \emph{CPS components} perspective, we focus on cyber, physical, and cyber-physical components; and (3) from the \emph{CPS systems} perspective, we explore general CPS features as well as representative systems (e.g., smart grids, medical CPS and smart cars). The model can be both abstract to show general interactions of a CPS application and specific to capture any details when needed. By doing so, we aim to build a model that is abstract enough to be applicable to various heterogeneous CPS applications; and to gain a modular view of the tightly coupled CPS components. Such abstract decoupling makes it possible to gain a systematic understanding of CPS security, and to highlight the potential sources of attacks and ways of protection.

Toward reassessing data-deficient species.

One in 6 species (13,465 species) on the International Union for Conservation of Nature (IUCN) Red List is classified as data deficient due to lack of information on their taxonomy, population status, or impact of threats. Despite the chance that many are at high risk of extinction, data-deficient species are typically excluded from global and local conservation priorities, as well as funding schemes. The number of data-deficient species will greatly increase as the IUCN Red List becomes more inclusive of poorly known and speciose groups. A strategic approach is urgently needed to enhance the conservation value of data-deficient assessments. To develop this, we reviewed 2879 data-deficient assessments in 6 animal groups and identified 8 main justifications for assigning data-deficient status (type series, few records, old records, uncertain provenance, uncertain population status or distribution, uncertain threats, taxonomic uncertainty, and new species). Assigning a consistent set of justification tags (i.e., consistent assignment to assessment justifications) to species classified as data deficient is a simple way to achieve more strategic assessments. Such tags would clarify the causes of data deficiency; facilitate the prediction of extinction risk; facilitate comparisons of data deficiency among taxonomic groups; and help prioritize species for reassessment. With renewed efforts, it could be straightforward to prevent thousands of data-deficient species slipping unnoticed toward extinction.

How to Lose a Constitutional Democracy

Is the United States at risk of democratic backsliding? And would the Constitution prevent such decay? To many, the 2016 election campaign may be the immediate catalyst for these questions. But it is structural changes to the socio-economic environment and geopolitical shifts that make the question a truly pressing one. This Article develops a taxonomy of different threats of democratic backsliding, the mechanisms whereby they unfold, and the comparative risk of each threat in the contemporary moment. By drawing on comparative law and politics experience, we demonstrate that there are two modal paths of democratic decay, which we call authoritarian reversion and constitutional retrogression. A reversion is a rapid and near-complete collapse of democratic institutions. Retrogression is a more subtle, incremental erosion that happens simultaneously to three institutional predicates of democracy: competitive elections; rights of political speech and association; and the administrative and adjudicative rule of law. Over the past quartercentury, we show that the risk of reversion has declined, while the risk of retrogression has spiked. The United States is not exceptional. We evaluate the danger of retrogression as clear and present, whereas we think reversion is much less likely. We further demonstrate that the constitutional safeguards against retrogression are weak. The near-term prospects of constitutional liberal democracy hence depend less on our institutions than on the qualities of political leadership and popular resistance.

TrustCall: A Trust Computation Model for Web Conversational Services

Web conversational services are exposed to several threats in which the social context between communicating participants is manipulated. Cybercrimes based on identity misrepresentation to obtain sensitive information are on the rise. Various scams and frauds are conducted by distributing malicious content, viruses, and spam over established communication sessions. In order to maintain overall security and enhance privacy, methods of estimating trustworthiness and reputation should be built into Web calling services. In this paper, we propose “TrustCall” a reputation-based trust model for real-time Web conversational services. In our approach, the reputation of a caller is evaluated using Authenticity Trust and Behavioral Trust . Authenticity Trust describes the legitimacy of a caller by collecting recommendations from other members of the network, whereas Behavioral Trust determines a caller’s popularity based on its communication behavior. Other contributions include a threat taxonomy for Web calling services, including social threats, that directly target users. A set of experiments are conducted in order to prove the feasibility and effectiveness of our model.

A Multidimension Taxonomy of Insider Threats in Cloud Computing

Security is considered a significant deficiency in cloud computing, and insider threats problem exacerbate security concerns in the cloud. In addition to that, cloud computing is very complex by itself, because it encompasses numerous technologies and concepts. Apparently, overcoming these challenges requires substantial efforts from information security researchers to develop powerful mitigation solutions for this emerging problem. This entails developing a taxonomy of insider threats in cloud environments encompassing all potential abnormal activities in the cloud, and can be useful for conducting security assessment. This paper describes the first phase of an ongoing research to develop a framework for mitigating insider threats in cloud computing environments. Primarily, it presents a multidimensional taxonomy of insider threats in cloud computing, and demonstrates its viability. The taxonomy provides a fundamental understanding for this complicated problem by identifying five dimensions, it also supports security engineers in identifying hidden paths, thus determining proper countermeasures, and presents a guidance covers all bounders of insiders threats issue in clouds, hence it facilitates researchers’ endeavours in tackling this problem. For instance, according to the hierarchical taxonomy, clearly many significant issues exist in public cloud, while conventional insider mitigation solutions can be used in private clouds. Finally, the taxonomy assists in identifying future research directions in this emerging area.

A Systematic Approach for Cyber Security in Vehicular Networks

Vehicular Networks (VANET) are the largest real-life paradigm of ad hoc networks which aim to ensure road safety and enhance drivers’ comfort. In VANET, the vehicles communicate or collaborate with each other and with adjacent infrastructure by exchanging significant messages, such as road accident warnings, steep-curve ahead warnings or traffic jam warnings. However, this communication and other assets involved are subject to major threats and provide numerous opportunities for attackers to launch several attacks and compromise security and privacy of vehicular users. This paper reviews the cyber security in VANET and proposes an asset-based approach for VANET security. Firstly, it identifies relevant assets in VANET. Secondly, it provides a detailed taxonomy of vulnerabilities and threats on these assets, and, lastly, it classifies the possible attacks in VANET and critically evaluates them.

The Role of Mobile Phone Technology in Understanding and Preventing Suicidal Behavior.

In this editorial, we discuss how mobile phone technology has the potential to move the field forward in terms of understanding suicide risk as well as laying foundations for the development of effective treatments/interventions. We have focused on mobile health technology given the rapid growth of mobile health approaches in suicide prevention (De Jaegere & Portzky, 2014; Mishara & Kerkhof, 2013) and psychological research more generally (Myin-Germeys et al., 2009; Nock, Prinstein, & Sterba, 2009; Palmier-Claus et al., 2011) and because mobile phone use is ubiquitous, with 75% of the world having access to a mobile phone (Kay, 2011). (aut. ref.)

Schematizing a historical demographic collapse on a large time span using local, secondary and grey data: The case of Italian roe deer Capreolus capreolus italicus in Central Italy

In this work we attempt to reconstruct the dynamic collapse over time of a regional population of Italian roe deer in central Italy (Latium and Abruzzi) from 15th Century to the present day. To meet this aim, both historical and grey literature (both primary and secondary) has been investigated, and the DPSIR model and a standardized threat taxonomy have been applied. Historically, different driving forces and threat pressures changed the status of roe deer and different conservation responses were promoted. Biological resource use (5.1—“Hunting and collecting terrestrial animals” and 5.3—“Logging and wood harvesting”), Natural system modification (7.1—“Fire” and 7.3—“Other ecosystem modifications, as land reclamation) and Invasive and other problematic species (8.3 “Introduced genetic material and genes”) were the main threats (IUCN standardized taxonomy). We recognized three historical phases and one sub-phase of demographic reduction and collapse: “Aristocratic hunting” phase (15th Century), “Fire weapons and people hunting” phase (from 16th to 18th Century), “Habitat collapse” phase (19th and 20th Century), with a sub-phase of “genetic introgression” beginning in the 1940s. Our analysis highlighted: (i) the role in conservation biology of primary and secondary grey sources of data; and (ii) the use of the DPSIR approach to schematically reconstruct the local factors and processes acting on the demographic collapse of a below-species taxon.

PRM240 - Avoiding and Identifying Errors and Other Threats To the Credibility of Health Economic Models

Health economic models have become the primary vehicle for undertaking economic evaluation and are used in various healthcare jurisdictions across the world to inform decisions about the use of new and existing health technologies. Models are required because a single source of evidence, such as a randomised controlled trial, is rarely sufficient to provide all relevant information about the expected costs and health consequences of all competing decision alternatives. Whilst models are used to synthesise all relevant evidence, they also contain assumptions, abstractions and simplifications. By their very nature, all models are therefore ‘wrong’. As such, the interpretation of estimates of the cost effectiveness of health technologies requires careful judgements about the degree of confidence that can be placed in the models from which they are drawn. The presence of a single error or inappropriate judgement within a model may lead to inappropriate decisions, an inefficient allocation of healthcare resources and ultimately suboptimal outcomes for patients. This paper sets out a taxonomy of threats to the credibility of health economic models. The taxonomy segregates threats to model credibility into three broad categories: (i) unequivocal errors, (ii) violations, and (iii) matters of judgement; and maps these across the main elements of the model development process. These three categories are defined according to the existence of criteria for judging correctness, the degree of force with which such criteria can be applied, and the means by which these credibility threats can be handled. A range of suggested processes and techniques for avoiding and identifying these threats is put forward with the intention of prospectively improving the credibility of models.

Avoiding and identifying errors and other threats to the credibility of health economic models.

Health economic models have become the primary vehicle for undertaking economic evaluation and are used in various healthcare jurisdictions across the world to inform decisions about the use of new and existing health technologies. Models are required because a single source of evidence, such as a randomised controlled trial, is rarely sufficient to provide all relevant information about the expected costs and health consequences of all competing decision alternatives. Whilst models are used to synthesise all relevant evidence, they also contain assumptions, abstractions and simplifications. By their very nature, all models are therefore 'wrong'. As such, the interpretation of estimates of the cost effectiveness of health technologies requires careful judgements about the degree of confidence that can be placed in the models from which they are drawn. The presence of a single error or inappropriate judgement within a model may lead to inappropriate decisions, an inefficient allocation of healthcare resources and ultimately suboptimal outcomes for patients. This paper sets out a taxonomy of threats to the credibility of health economic models. The taxonomy segregates threats to model credibility into three broad categories: (i) unequivocal errors, (ii) violations, and (iii) matters of judgement; and maps these across the main elements of the model development process. These three categories are defined according to the existence of criteria for judging correctness, the degree of force with which such criteria can be applied, and the means by which these credibility threats can be handled. A range of suggested processes and techniques for avoiding and identifying these threats is put forward with the intention of prospectively improving the credibility of models.