Avatar captcha : telling computers and humans apart via face classification and mouse dynamics.

Bots are malicious, automated computer programs that execute malicious scripts and predefined functions on an affected computer. They pose cybersecurity threats and are one of the most sophisticated and common types of cybercrime tools today. They spread viruses, generate spam, steal personal sensitive information, rig online polls and commit other types of online crime and fraud. They sneak into unprotected systems through the Internet by seeking vulnerable entry points. They access the system’s resources like a human user does. Now the question arises how do we counter this? How do we prevent bots and on the other hand allow human users to access the system resources? One solution is by designing a CAPTCHA (Completely Automated Public Turing Tests to tell Computers and Humans Apart), a program that can generate and grade tests that most humans can pass but computers cannot. It is used as a tool to distinguish humans from malicious bots. They are a class of Human Interactive Proofs (HIPs) meant to be easily solvable by humans and economically infeasible for computers. Text CAPTCHAs are very popular and commonly used. For each challenge, they generate a sequence of alphabets by distorting standard fonts, requesting users to identify them and type them out. However, they are vulnerable to character segmentation attacks by bots, English language dependent and are increasingly becoming too complex for people to solve. A solution to this is to design Image CAPTCHAs that use images instead of text and require users to identify certain images to solve the challenges. They are user-friendly and convenient for human users and a much more challenging problem for bots to solve. In today’s Internet world the role of user profiling or user identification has gained a lot of significance. Identity thefts, etc. can be prevented by providing authorized access to resources. To achieve timely response to a security breach frequent user verification is needed. However, this process must be passive, transparent and non-obtrusive. In order for such a system to be practical it must be accurate, efficient and difficult to forge. Behavioral biometric systems are usually less prominent however, they provide numerous and significant advantages over traditional biometric systems. Collection of behavior data is non-obtrusive and cost-effective as it requires no special hardware. While these systems are not unique enough to provide reliable human identification, they have shown to be highly accurate in identity verification. In accomplishing everyday tasks, human beings use different styles, strategies, apply unique skills and knowledge, etc. These define the behavioral traits of the user. Behavioral biometrics attempts to quantify these traits to profile users and establish their identity. Human computer interaction (HCI)-based biometrics comprise of interaction strategies and styles between a human and a computer. These unique user traits are quantified to build profiles for identification. A specific category of HCI-based biometrics is based on recording human interactions with mouse as the input device and is known as Mouse Dynamics. By monitoring the mouse usage activities produced by a user during interaction with the GUI, a unique profile can be created for that user that can help identify him/her. Mouse-based verification approaches do not record sensitive user credentials like usernames and passwords. Thus, they avoid privacy issues. An image CAPTCHA is proposed that incorporates Mouse Dynamics to help fortify it. It displays random images obtained from Yahoo’s Flickr. To solve the challenge the user must identify and select a certain class of images. Two theme-based challenges have been designed. They are Avatar CAPTCHA and Zoo CAPTCHA. The former displays human and avatar faces whereas the latter displays different animal species. In addition to the dynamically selected images, while attempting to solve the CAPTCHA, the way each user interacts with the mouse i.e. mouse clicks, mouse movements, mouse cursor screen co-ordinates, etc. are recorded nonobtrusively at regular time intervals. These recorded mouse movements constitute the Mouse Dynamics Signature (MDS) of the user. This MDS provides an additional secure technique to segregate humans from bots. The security of the CAPTCHA is tested by an adversary executing a mouse bot attempting to solve the CAPTCHA challenges

THE STRATEGIC UTILITY OF SOF IN GREAT POWER COMPETITION: A NATO PERSPECTIVE

NATO needs to discuss whether, why, and how Special Operations Forces (SOF) contribute to the “fight” in Great Power Competition. NATO’s security strategy traditionally relies on a deterrence posture with conventional and nuclear capabilities. The new NATO 2022 Strategic Concept validates the necessity to research the question: What is the strategic utility of SOF for NATO in Great Power Competition, and how can this strategic utility be enhanced? This study uses a qualitative methodology. At the core is a comparative analysis of two scenarios in the Black Sea and Arctic regions, both developed through a systematic process and enriched with imagination to contain useful vignettes. The analysis suggests that SOF have strategic utility, albeit in changing manifestations in different phases of the conflict continuum, in Great Power Competition. SOF expands the strategic options available to political and military leaders—expansion of choice—to anticipate and respond, especially in an early stage of a crises below the threshold of armed conflict. SOF also achieve significant results with limited forces—economy of force—when conventional formations are not available or capable. It is not about what SOF can and should do; the heart of the matter is what makes the strategic difference—expansion of choice and economy of force—that defines the future of SOF.Majoor, Royal Netherlands ArmyOberstleutnant, German ArmyApproved for public release. Distribution is unlimited

Air Force Institute of Technology Research Report 2016

This Research Report presents the FY16 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs)

A Holistic Analysis of Internet of Things (IoT) Security : Principles, Practices, and New Perspectives

Peer reviewedPublisher PD

Lessons Learned from Topic Modeling Analysis of COVID-19 News to Enrich Statistics Education in Korea

This study aimed to investigate how mass media in Korea dealt with various issues arising from COVID-19 and the implications of this on statistics education in South Korea during the recent pandemic. We extracted news articles with the keywords “Corona” and “Statistics” from 18 February to 20 May 2020. We employed word frequency analysis, topic modeling, semantic network analysis, hierarchical clustering, and simple linear regression analysis. The main results of this study are as follows. First, the topic modeling analysis revealed four topics, namely “macroeconomy”, “domestic outbreak”, “international outbreak”, and “real estate and stocks”. Second, a simple linear regression analysis displayed two rising topics, “macroeconomy” and “real estate and stocks” and two falling topics, “domestic outbreak” and “international outbreak” regarding the statistics related to COVID-19 as time passed. Based on these findings, we suggest that the high school mathematics curriculum of Korea should be revised to use real-life context to enable integrated education, social justice for statistics education, and simple linear regression analysis

Security awareness of computer users: A game based learning approach

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The research reported in this thesis focuses on developing a framework for game design to protect computer users against phishing attacks. A comprehensive literature review was conducted to understand the research domain, support the proposed research work and identify the research gap to fulfil the contribution to knowledge. Two studies and one theoretical design were carried out to achieve the aim of this research reported in this thesis. A quantitative approach was used in the first study while engaging both quantitative and qualitative approaches in the second study. The first study reported in this thesis was focused to investigate the key elements that should be addressed in the game design framework to avoid phishing attacks. The proposed game design framework was aimed to enhance the user avoidance behaviour through motivation to thwart phishing attack. The results of this study revealed that perceived threat, safeguard effectiveness, safeguard cost, self-efficacy, perceived severity and perceived susceptibility elements should be incorporated into the game design framework for computer users to avoid phishing attacks through their motivation. The theoretical design approach was focused on designing a mobile game to educate computer users against phishing attacks. The elements of the framework were addressed in the mobile game design context. The main objective of the proposed mobile game design was to teach users how to identify phishing website addresses (URLs), which is one of many ways of identifying a phishing attack. The mobile game prototype was developed using MIT App inventor emulator. In the second study, the formulated game design framework was evaluated through the deployed mobile game prototype on a HTC One X touch screen smart phone. Then a discussion is reported in this thesis investigating the effectiveness of the developed mobile game prototype compared to traditional online learning to thwart phishing threats. Finally, the research reported in this thesis found that the mobile game is somewhat effective in enhancing the user’s phishing awareness. It also revealed that the participants who played the mobile game were better able to identify fraudulent websites compared to the participants who read the website without any training. Therefore, the research reported in this thesis determined that perceived threat, safeguard effectiveness, safeguard cost, self-efficacy, perceived threat and perceived susceptibility elements have a significant impact on avoidance behaviour through motivation to thwart phishing attacks as addressed in the game design framework

A Comprehensive Review on Adaptability of Network Forensics Frameworks for Mobile Cloud Computing

Network forensics enables investigation and identification of network attacks through the retrieved digital content. The proliferation of smartphones and the cost-effective universal data access through cloud has made Mobile Cloud Computing (MCC) a congenital target for network attacks. However, confines in carrying out forensics in MCC is interrelated with the autonomous cloud hosting companies and their policies for restricted access to the digital content in the back-end cloud platforms. It implies that existing Network Forensic Frameworks (NFFs) have limited impact in the MCC paradigm. To this end, we qualitatively analyze the adaptability of existing NFFs when applied to the MCC. Explicitly, the fundamental mechanisms of NFFs are highlighted and then analyzed using the most relevant parameters. A classification is proposed to help understand the anatomy of existing NFFs. Subsequently, a comparison is given that explores the functional similarities and deviations among NFFs. The paper concludes by discussing research challenges for progressive network forensics in MCC

A Comprehensive Review on Adaptability of Network Forensics Frameworks for Mobile Cloud Computing

Network forensics enables investigation and identification of network attacks through the retrieved digital content. The proliferation of smartphones and the cost-effective universal data access through cloud has made Mobile Cloud Computing (MCC) a congenital target for network attacks. However, confines in carrying out forensics in MCC is interrelated with the autonomous cloud hosting companies and their policies for restricted access to the digital content in the back-end cloud platforms. It implies that existing Network Forensic Frameworks (NFFs) have limited impact in the MCC paradigm. To this end, we qualitatively analyze the adaptability of existing NFFs when applied to the MCC. Explicitly, the fundamental mechanisms of NFFs are highlighted and then analyzed using the most relevant parameters. A classification is proposed to help understand the anatomy of existing NFFs. Subsequently, a comparison is given that explores the functional similarities and deviations among NFFs. The paper concludes by discussing research challenges for progressive network forensics in MCC

Cyber Security and Critical Infrastructures 2nd Volume

The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems

Establishing cyber situational awareness in industrial control systems

The cyber threat to industrial control systems is an acknowledged security issue, but a qualified dataset to quantify the risk remains largely unavailable. Senior executives of facilities that operate these systems face competing requirements for investment budgets, but without an understanding of the nature of the threat cyber security may not be a high priority. Operational managers and cyber incident responders at these facilities face a similarly complex situation. They must plan for the defence of critical systems, often unfamiliar to IT security professionals, from potentially capable, adaptable and covert antagonists who will actively attempt to evade detection. The scope of the challenge requires a coherent, enterprise-level awareness of the threat, such that organisations can assess their operational priorities, plan their defensive posture, and rehearse their responses prior to such an attack. This thesis proposes a novel combination of concepts found in risk assessment, intrusion detection, education, exercising, safety and process models, fused with experiential learning through serious games. It progressively builds a common set of shared mental models across an ICS operation to frame the nature of the adversary and establish enterprise situational awareness that permeates through all levels of teams involved in addressing the threat. This is underpinned by a set of coping strategies that identifies probable targets for advanced threat actors, proactively determining antagonistic courses of actions to derive an appropriate response strategy