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Robert E. Crossler | Allen C. Johnston | Paul Benjamin Lowry | Qing Hu | Merrill Warkentin | Richard Baskerville
Information Security (InfoSec) research is far reaching and includes many approaches to deal with protecting and mitigating threats to the information assets and technical resources available within computer based systems. Although a predominant weakness in properly securing information assets is the individual user within an organization, much of the focus of extant security research is on technical issues. The purpose of this paper is to highlight future directions for Behavioral InfoSec research, which is a newer, growing area of research. The ensuing paper presents information about challenges currently faced and future directions that Behavioral InfoSec researchers should explore. These areas include separating insider deviant behavior from insider misbehavior, approaches to understanding hackers, improving information security compliance, cross-cultural Behavioral InfoSec research, and data collection and measurement issues in Behavioral InfoSec research. © 2012 Elsevier Ltd. All rights reserved.
This research investigated information systems security policy (ISSP) compliance by drawing upon two relevant theories i.e. the theory of planned behavior (TPB) and the protection motivation theory (PMT). A research model that fused constituents of the aforementioned theories was proposed and validated. Relevant hypotheses were developed to test the research conceptualization. Data analysis was performed using the partial least squares (PLS) technique. Using a survey of 124 business managers and IS professionals, this study showed that factors such as self-efficacy, attitude toward compliance, subjective norms, response efficacy and perceived vulnerability positively influence ISSP behavioral compliance intentions of employees. The data analysis did not support perceived severity and response cost as being predictors of ISSP behavioral compliance intentions. The study’s implications for research and practice are discussed. © 2011 Elsevier Ltd. All rights reserved.
Ali Shiravi | Hadi Shiravi | Mahbod Tavallaee | Ali A. Ghorbani
In network intrusion detection, anomaly-based approaches in particular suffer from accurate evaluation, comparison, and deployment which originates from the scarcity of adequate datasets. Many such datasets are internal and cannot be shared due to privacy issues, others are heavily anonymized and do not reflect current trends, or they lack certain statistical characteristics. These deficiencies are primarily the reasons why a perfect dataset is yet to exist. Thus, researchers must resort to datasets that are often suboptimal. As network behaviors and patterns change and intrusions evolve, it has very much become necessary to move away from static and one-time datasets toward more dynamically generated datasets which not only reflect the traffic compositions and intrusions of that time, but are also modifiable, extensible, and reproducible. In this paper, a systematic approach to generate the required datasets is introduced to address this need. The underlying notion is based on the concept of profiles which contain detailed descriptions of intrusions and abstract distribution models for applications, protocols, or lower level network entities. Real traces are analyzed to create profiles for agents that generate real traffic for HTTP, SMTP, SSH, IMAP, POP3, and FTP. In this regard, a set of guidelines is established to outline valid datasets, which set the basis for generating profiles. These guidelines are vital for the effectiveness of the dataset in terms of realism, evaluation capabilities, total capture, completeness, and malicious activity. The profiles are then employed in an experiment to generate the desirable dataset in a testbed environment. Various multi-stage attacks scenarios were subsequently carried out to supply the anomalous portion of the dataset. The intent for this dataset is to assist various researchers in acquiring datasets of this kind for testing, evaluation, and comparison purposes, through sharing the generated datasets and profiles. © 2011 Elsevier Ltd. All rights reserved.
A. Nicholson | S. Webber | S. Dyer | T. Patel | H. Janicke
Supervisory Control and Data Acquisition (SCADA) systems are deployed worldwide in many critical infrastructures ranging from power generation, over public transport to industrial manufacturing systems. Whilst contemporary research has identified the need for protecting SCADA systems, these information are disparate and do not provide a coherent view of the threats and the risks resulting from the tendency to integrate these once isolated systems into corporate networks that are prone to cyber attacks. This paper surveys ongoing research and provides a coherent overview of the threats, risks and mitigation strategies in the area of SCADA security. © 2012 Elsevier Ltd. All rights reserved.
Alexios Mylonas | Anastasia Kastania | Dimitris Gritzalis
Smartphone users increasingly download and install third-party applications from official application repositories. Attackers may use this centralized application delivery architecture as a security and privacy attack vector. This risk increases since application vetting mechanisms are often not in place and the user is delegated to authorize which functionality and protected resources are accessible by third-party applications. In this paper, we mount a survey to explore the security awareness of smartphone users who download applications from official application repositories (e.g. Google Play, Apple’s App Store, etc.). The survey findings suggest a security complacency, as the majority of users trust the app repository, security controls are not enabled or not added, and users disregard security during application selection and installation. As a response to this security complacency we built a prediction model to identify users who trust the app repository. The model is assessed, evaluated and proved to be statistically significant and efficient. © 2012 Elsevier Ltd. All rights reserved.
David Zhao | Issa Traore | Bassam Sayed | Wei Lu | Sherif Saad | Ali Ghorbani | Dan Garant
Botnets represent one of the most serious cybersecurity threats faced by organizations today. Botnets have been used as the main vector in carrying many cyber crimes reported in the recent news. While a significant amount of research has been accomplished on botnet analysis and detection, several challenges remain unaddressed, such as the ability to design detectors which can cope with new forms of botnets. In this paper, we propose a new approach to detect botnet activity based on traffic behavior analysis by classifying network traffic behavior using machine learning. Traffic behavior analysis methods do not depend on the packets payload, which means that they can work with encrypted network communication protocols. Network traffic information can usually be easily retrieved from various network devices without affecting significantly network performance or service availability. We study the feasibility of detecting botnet activity without having seen a complete network flow by classifying behavior based on time intervals. Using existing datasets, we show experimentally that it is possible to identify the presence of existing and unknown botnets activity with high accuracy even with very small time windows. © 2013 Elsevier Ltd. All rights reserved.
Rossouw Von Solms | Johan Van Niekerk
The term cyber security is often used interchangeably with the term information security. This paper argues that, although there is a substantial overlap between cyber security and information security, these two concepts are not totally analogous. Moreover, the paper posits that cyber security goes beyond the boundaries of traditional information security to include not only the protection of information resources, but also that of other assets, including the person him/herself. In information security, reference to the human factor usually relates to the role(s) of humans in the security process. In cyber security this factor has an additional dimension, namely, the humans as potential targets of cyber attacks or even unknowingly participating in a cyber attack. This additional dimension has ethical implications for society as a whole, since the protection of certain vulnerable groups, for example children, could be seen as a societal responsibility. © 2013 Elsevier Ltd.
Shari Lawrence Pfleeger | Deanna D. Caputo
Most efforts to improve cyber security focus primarily on incorporating new technological approaches in products and processes. However, a key element of improvement involves acknowledging the importance of human behavior when designing, building and using cyber security technology. In this survey paper, we describe why incorporating an understanding of human behavior into cyber security products and processes can lead to more effective technology. We present two examples: the first demonstrates how leveraging behavioral science leads to clear improvements, and the other illustrates how behavioral science offers the potential for significant increases in the effectiveness of cyber security. Based on feedback collected from practitioners in preliminary interviews, we narrow our focus to two important behavioral aspects: cognitive load and bias. Next, we identify proven and potential behavioral science findings that have cyber security relevance, not only related to cognitive load and bias but also to heuristics and behavioral science models. We conclude by suggesting several next steps for incorporating behavioral science findings in our technological design, development and use. © 2012 Elsevier Ltd. All rights reserved.
Zheran Fang | Weili Han | Yingjiu Li
Android security has been a hot spot recently in both academic research and public concerns due to numerous instances of security attacks and privacy leakage on Android platform. Android security has been built upon a permission based mechanism which restricts accesses of third-party Android applications to critical resources on an Android device. Such permission based mechanism is widely criticized for its coarse-grained control of application permissions and difficult management of permissions by developers, marketers, and end-users. In this paper, we investigate the arising issues in Android security, including coarse granularity of permissions, incompetent permission administration, insufficient permission documentation, over-claim of permissions, permission escalation attack, and TOCTOU (Time of Check to Time of Use) attack. We illustrate the relationships among these issues, and investigate the existing countermeasures to address these issues. In particular, we provide a systematic review on the development of these countermeasures, and compare them according to their technical features. Finally, we propose several methods to further mitigate the risk in Android security. © 2014 Elsevier Ltd. All rights reserved.
Ulrik Franke | Joel Brynielsson
Cyber situational awareness is attracting much attention. It features prominently in the national cyber strategies of many countries, and there is a considerable body of research dealing with it. However, until now, there has been no systematic and up-to-date review of the scientific literature on cyber situational awareness. This article presents a review of cyber situational awareness, based on systematic queries in four leading scientific databases. 102 articles were read, clustered, and are succinctly described in the paper. The findings are discussed from the perspective of both national cyber strategies and science, and some directions for future research are examined. © 2014 Elsevier Ltd. All rights reserved.
A. Shabtai | L. Tenenboim-Chekina | D. Mimran | L. Rokach | B. Shapira | Y. Elovici
In this paper we present a new behavior-based anomaly detection system for detecting meaningful deviations in a mobile application’s network behavior. The main goal of the proposed system is to protect mobile device users and cellular infrastructure companies from malicious applications by: (1) identification of malicious attacks or masquerading applications installed on a mobile device, and (2) identification of republished popular applications injected with a malicious code (i.e., repackaging). More specifically, we attempt to detect a new type of mobile malware with self-updating capabilities that were recently found on the official Google Android marketplace. Malware of this type cannot be detected using the standard signatures approach or by applying regular static or dynamic analysis methods. The detection is performed based on the application’s network traffic patterns only. For each application, a model representing its specific traffic pattern is learned locally (i.e., on the device). Semi-supervised machine-learning methods are used for learning the normal behavioral patterns and for detecting deviations from the application’s expected behavior. These methods were implemented and evaluated on Android devices. The evaluation experiments demonstrate that: (1) various applications have specific network traffic patterns and certain application categories can be distinguished by their network patterns; (2) different levels of deviation from normal behavior can be detected accurately; (3) in the case of self-updating malware, original (benign) and infected versions of an application have different and distinguishable network traffic patterns that in most cases, can be detected within a few minutes after the malware is executed while presenting very low false alarms rate; and (4) local learning is feasible and has a low performance overhead on mobile devices. © 2014 Elsevier Ltd. All rights reserved.
This paper aims at surveying the extrinsic and intrinsic motivations that influence the propensity toward compliant information security behavior. Information security behavior refers to a set of core information security activities that have to be adhered to by end-users to maintain information security as defined by information security policies. The intention is to classify the research done on compliant information security behavior from an end-user perspective and arrange it as a taxonomy predicated on Self-Determination Theory (SDT). In addition, the relative significance of factors that contribute to compliant information security behavior is evaluated on the basis of empirical studies. The taxonomy will be valuable in providing a comprehensive overview of the factors that influence compliant information security behavior and in identifying areas that require further research. © 2012 Elsevier Ltd. All rights reserved.
Shuzhen Wang | Zonghua Zhang | Youki Kadobayashi
The increasing complexity of today’s computer systems, together with the rapid emergence of novel vulnerabilities, make security hardening a formidable challenge for security administrators. Although a large variety of tools and techniques are available for vulnerability analysis, the majority work at system or network level without explicit association with human and organizational factors. This article presents a middleware approach to bridge the gap between system-level vulnerabilities and organization-level security metrics, ultimately contributing to cost-benefit security hardening. In particular, our approach systematically integrates attack graph, a commonly used effective approach to representing and analyzing network vulnerabilities, and Hidden Markov Model (HMM) together, for exploring the probabilistic relation between system observations and states. More specifically, we modify and apply dependency attack graph to represent network assets and vulnerabilities (observations), which are then fed to HMM for estimating attack states, whereas their transitions are driven by a set of predefined cost factors associated with potential attacks and countermeasures. A heuristic searching algorithm is employed to automatically infer the optimal security hardening through cost-benefit analysis. We use a synthetic network scenario to illustrate our approach and evaluate its performance through a set of simulations. © 2012 Elsevier Ltd. All rights reserved.
Atif Ahmad | Justin Hadgkiss | A. B. Ruighaver
Incident response is a critical security function in organisations that aims to manage incidents in a timely and cost-effective manner. This research was motivated by previous case studies that suggested that the practice of incident response frequently did not result in the improvement of strategic security processes such as policy development and risk assessment. An exploratory in-depth case study was performed at a large global financial institution to examine shortcomings in the practice of incident response. The case study revealed the practice of incident response, in accordance with detailed best-practice guidelines, tended to adopt a narrow technical focus aimed at maintaining business continuity whilst neglecting strategic security concerns. The case study also revealed that the (limited) post-incident review process focused on ‘high-impact’ incidents rather than ‘high-learning’ (i.e. potentially useful incidents from a learning perspective) incidents and ‘near misses’. In response to this case study, we propose a new double-loop model for incident learning to address potential systemic corrective action in such areas as the risk assessment and policy development processes. © 2012 Elsevier Ltd. All rights reserved.
S. García | M. Grill | J. Stiborek | A. Zunino
The results of botnet detection methods are usually presented without any comparison. Although it is generally accepted that more comparisons with third-party methods may help to improve the area, few papers could do it. Among the factors that prevent a comparison are the difficulties to share a dataset, the lack of a good dataset, the absence of a proper description of the methods and the lack of a comparison methodology. This paper compares the output of three different botnet detection methods by executing them over a new, real, labeled and large botnet dataset. This dataset includes botnet, normal and background traffic. The results of our two methods (BClus and CAMNEP) and BotHunter were compared using a methodology and a novel error metric designed for botnet detections methods. We conclude that comparing methods indeed helps to better estimate how good the methods are, to improve the algorithms, to build better datasets and to build a comparison methodology. © 2014 Elsevier Ltd. All rights reserved.
Saman Zonouz | Amir Houmansadra | Robin Berthiera | Nikita Borisova | William Sanders
As smartphones are becoming more complex and powerful to provide better functionalities, concerns are increasing regarding security threats against their users. Since smart-phones use a software architecture similar to PCs, they are vulnerable to the same classes of security risks. Unfortunately, smartphones are constrained by their limited resources that prevent the integration of advanced security monitoring solutions that work with traditional PCs. We propose Secloud, a cloud-based security solution for smartphone devices. Secloud emulates a registered smartphone device inside a designated cloud and keeps it synchronized by continuously passing the device inputs and network connections to the cloud. This allows Secloud to perform a resource-intensive security analysis on the emulated replica that would otherwise be infeasible to run on the device itself. We demonstrate the practical feasibility of Secloud through a prototype for Android devices and illustrate its resource effectiveness by comparing it with on-device solutions. © 2013 Elsevier Ltd. All rights reserved.
Nurul Hidayah Ab Rahman | Kim Kwang Raymond Choo
© 2014 Elsevier Ltd. Incident handling strategy is one key strategy to mitigate risks to the confidentiality, integrity and availability (CIA) of organisation assets, as well as minimising loss (e.g. financial, reputational and legal) particularly as organisations move to the cloud. In this paper, we surveyed existing incident handling and digital forensic literature with the aims of contributing to the knowledge gap(s) in handling incidents in the cloud environment. 139 English language publications between January 2009 and May 2014 were located by searching various sources including the websites of standard bodies (e.g. National Institute of Standards and Technology) and academic databases (e.g. Google Scholar, IEEEXplore, ACM Digital Library, Springer and ScienceDirect). We then propose a conceptual cloud incident handling model that brings together incident handling, digital forensic and the Capability Maturity Model for Services to more effectively handle incidents for organisations using the cloud. A discussion of open research issues concludes this survey.
Kathryn Parsons | Agata McCormac | Marcus Butavicius | Malcolm Pattinson | Cate Jerram
It is increasingly acknowledged that many threats to an organisation’s computer systems can be attributed to the behaviour of computer users. To quantify these human-based information security vulnerabilities, we are developing the Human Aspects of Information Security Questionnaire (HAIS-Q). The aim of this paper was twofold. The first aim was to outline the conceptual development of the HAIS-Q, including validity and reliability testing. The second aim was to examine the relationship between knowledge of policy and procedures, attitude towards policy and procedures and behaviour when using a work computer. Results from 500 Australian employees indicate that knowledge of policy and procedures had a stronger influence on attitude towards policy and procedure than self-reported behaviour. This finding suggests that training and education will be more effective if it outlines not only what is expected (knowledge) but also provides an understanding of why this is important (attitude). Plans for future research to further develop and test the HAIS-Q are outlined. Crown Copyright © 2014 Published by Elsevier Ltd. All rights reserved.
Steven Furnell | Nathan Clarke
It is perhaps unsurprising to find much of the focus in IT and computer security being drawn towards the technical aspects of the discipline. However, it is increasingly recognised that technology alone cannot deliver a complete solution, and there is also a tangible need to address human aspects. At the core, people must understand the threats they face and be able to use the protection available to them, and although this has not been entirely ignored, it has not received the level of attention that it merits either. Indeed, security surveys commonly reveal that the more directly user-facing aspects such as policy, training and education are prone to receiving significantly less attention than technical controls such as firewalls, antivirus and intrusion detection. The underlying reason for such disparity is that the human aspects are in many ways a more challenging problem to approach, not least because they cannot be easily targeted with a product-based solution. There is also a direct overlap into the technical area, with issues such as the usability and acceptability of technology solutions having a direct impact upon the actual protection that they are able to deliver. This paper explores these themes, highlighting the need for human aspects to form part of a holistic security strategy alongside the necessary technologies. Taking the specific examples of security awareness and two user-facing technical controls (user authentication and antivirus), the discussion examines how things have evolved to the present day and considers how they need to be positioned for the future. © 2012 Elsevier Ltd. All rights reserved.
Yosra Ben Saied | Alexis Olivereau | Djamal Zeghlache | Maryline Laurent
This work proposes a new trust management system (TMS) for the Internet of Things (IoT). The wide majority of these systems are today bound to the assessment of trustworthiness with respect to a single function. As such, they cannot use past experiences related to other functions. Even those that support multiple functions hide this heterogeneity by regrouping all past experiences into a single metric. These restrictions are detrimental to the adaptation of TMSs to today’s emerging M2M and IoT architectures, which are characterized with heterogeneity in nodes, capabilities and services. To overcome these limitations, we design a context-aware and multi-service trust management system fitting the new requirements of the IoT. Simulation results show the good performance of the proposed system and especially highlight its ability to deter a class of common attacks designed to target trust management systems. © 2012 Elsevier B.V. All rights reserved.
Sergio Martínez | David Sánchez | Aida Valls
In the context of Statistical Disclosure Control, microaggregation is a privacy-preserving method aimed to mask sensitive microdata prior to publication. It iteratively creates clusters of, at least, k elements, and replaces them by their prototype so that they become k-indistinguishable (anonymous). This data transformation produces a loss of information with regards to the original dataset which affects the utility of masked data, so, the aim of microaggregation algorithms is to find the partition that minimises the information loss while ensuring a certain level of privacy. Most microaggregation methods, such as the MDAV algorithm, which is the focus of this paper, have been designed for numerical data. Extending them to support non-numerical (categorical) attributes is not straightforward because of the limitations on defining appropriate aggregation operators. Concretely, related works focused on the MDAV algorithm propose grouping data into groups with constrained size (or even fixed) and/or incorporate a basic categorical treatment of non-numerical data. This approach affects negatively the utility of the protected dataset because neither the distributional characteristics of data nor their underlying semantics are properly considered. In this paper, we propose a set of modifications to the MDAV algorithm focused on categorical microdata. Our approach has been evaluated and compared with related works when protecting real datasets with textual attribute values. Results show that our method produces masked datasets that better minimises the information loss resulting from the data transformation. © 2012 Elsevier Ltd. All rights reserved.
Ken H. Guo
Security-related behavior in the workplace has recently drawn much attention from scholars in the information systems literature. Many studies, however, have reported inconsistent and sometimes contradictory results about the effects of some key factors such as sanctions. We argue that one of the reasons causing the inconsistent findings is the divergent conceptualizations of security-related behavior. In this paper, we conducted an extensive review of the divergent concepts. Many of the concepts overlap with each other on some dimensions and yet are different on others. By delineating and synthesizing the differences, we proposed a framework for conceptualizing security-related behavior. The framework can facilitate the development of consistent and comparable terms and concepts in future studies. Implications for research are also discussed. © 2012 Elsevier Ltd. All rights reserved.
Shu Yuan Shen | Li Hong Huang
© 2014 Elsevier Ltd. The fundamental requirements of information hiding systems are good visual quality, high hiding capacity, robustness and steganographic security. In this paper, we propose a new data hiding method which can increase the steganographic security of a data hiding scheme because it is less detectable by RS detection attack and the steganalytic histogram attack of pixel-value difference. In our method, a cover image is first mapped into a 1D pixels sequence by Hilbert filling curve and then divided into non-overlapping embedding units with two consecutive pixels. Because the human eyes tolerate more changes in edge and texture areas than in smooth areas, and pixel pairs in these areas often possess larger differences, the method exploits pixel value differences (PVD) to estimate the base of digits to be embedded into pixel pairs. Pixel pairs with larger differences are embedded with digits in larger base than those pixel pairs with smaller differences to maximize the payload and image quality. By using an optimization problem to solve the overflow/underflow problem, minimal distortion of the pixel ternaries cause by data embedding can be obtained. The experimental results show our method not only to enhance the embedding rate and good embedding capacity but also to keep stego-image quality.
M. D. Kohn | M. M. Eloff | J. H P Eloff
Digital forensics is an established research and application field. Various process models exist describing the steps and processes to follow during digital forensic investigations. During such investigations, it is not only the digital evidence itself that needs to prevail in a court of law; the process followed and terminology used should also be rigorous and generally accepted within the digital forensic community. Different investigators have been refining their own investigative methods, resulting in a variety of digital forensic process models. This paper proposes a standardized Digital Forensic Process Model to aid investigators in following a uniform approach in digital forensic investigations. © 2013 Elsevier Ltd.
Quang Do | Ben Martini | Kim Kwang Raymond Choo
© 2014 Elsevier Ltd. All rights reserved. Modern mobile devices have security capabilities built into the native operating system, which are generally designed to ensure the security of personal or corporate data stored on the device, both at rest and in transit. In recent times, there has been interest from researchers and governments in securing as well as exfiltrating data stored on such devices (e.g. the high profile PRISM program involving the US Government). In this paper, we propose an adversary model for Android covert data exfiltration, and demonstrate how it can be used to construct a mobile data exfiltration technique (MDET) to covertly exfiltrate data from Android devices. Two proof-of-concepts were implemented to demonstrate the feasibility of exfiltrating data via SMS and inaudible audio transmission using standard mobile devices.