Biblio

Ubiquitous WiFi infrastructure and smart phones offer a great opportunity to study physical activities. In this paper, we present MobiCamp, a large-scale testbed for studying mobility-related activities of residents on a campus. MobiCamp consists of \textasciitilde2,700 APs, \textasciitilde95,000 smart phones, and an App with \textasciitilde2,300 opt-in volunteer users. More specifically, we capture how mobile users interact with different types of buildings, with other users, and with classroom courses, etc. To achieve this goal, we first obtain a relatively complete coverage of the users' mobility traces by utilizing four types of information from SNMP and by relaxing the location granularity to roughly at the room level. Then the popular App provides user attributes (grade, gender, etc.) and fine-grained behavior information (phone usages, course timetables, etc.) of the sampled population. These detailed mobile data is then correlated with the mobility traces from the SNMP to estimate the entire campus population's physical activities. We use two applications to show the power of MobiCamp.

We study the trade-off between the benefits obtained by communication, vs. the risks due to exposure of the location of the transmitter. To study this problem, we introduce a game between two teams of mobile agents, the P-bots team and the E-bots team. The E-bots attempt to eavesdrop and collect information, while evading the P-bots; the P-bots attempt to prevent this by performing patrol and pursuit. The game models a typical use-case of micro-robots, i.e., their use for (industrial) espionage. We evaluate strategies for both teams, using analysis and simulations.

With its high penetration rate and relatively good clock accuracy, smartphones are replacing watches in several market segments. Modern smartphones have more than one clock source to complement each other: NITZ (Network Identity and Time Zone), NTP (Network Time Protocol), and GNSS (Global Navigation Satellite System) including GPS. NITZ information is delivered by the cellular core network, indicating the network name and clock information. NTP provides a facility to synchronize the clock with a time server. Among these clock sources, only NITZ and NTP are updated without user interaction, as location services require manual activation. In this paper, we analyze security aspects of these clock sources and their impact on security features of modern smartphones. In particular, we investigate NITZ and NTP procedures over cellular networks (2G, 3G and 4G) and Wi-Fi communication respectively. Furthermore, we analyze several European, Asian, and American cellular networks from NITZ perspective. We identify three classes of vulnerabilities: specification issues in a cellular protocol, configurational issues in cellular network deployments, and implementation issues in different mobile OS's. We demonstrate how an attacker with low cost setup can spoof NITZ and NTP messages to cause Denial of Service attacks. Finally, we propose methods for securely synchronizing the clock on smartphones.

Future transportation systems highly rely on the integrity of spatial information provided by their means of transportation such as vehicles and planes. In critical applications (e.g. collision avoidance), tampering with this data can result in life-threatening situations. It is therefore essential for the safety of these systems to securely verify this information. While there is a considerable body of work on the secure verification of locations, movement of nodes has only received little attention in the literature. This paper proposes a new method to securely verify spatial movement of a mobile sender in all dimensions, i.e., position, speed, and direction. Our scheme uses Doppler shift measurements from different locations to verify a prover's motion. We provide formal proof for the security of the scheme and demonstrate its applicability to air traffic communications. Our results indicate that it is possible to reliably verify the motion of aircraft in currently operational systems with an equal error rate of zero.

Radio network information is leaked well beyond the perimeter in which the radio network is deployed. We investigate attacks where person location can be inferred using the radio characteristics of wireless links (e.g., the received signal strength). An attacker can deploy a network of receivers which measure the received signal strength of the radio signals transmitted by the legitimate wireless devices inside a perimeter, allowing the attacker to learn the locations of people moving in the vicinity of the devices inside the perimeter. In this paper, we develop the first solution to this location privacy problem where neither the attacker nodes nor the tracked moving object transmit any RF signals. We first model the radio network leakage attack using a Stackelberg game. Next, we define utility and cost functions related to the defender and attacker actions. Last, using our utility and cost functions, we find the optimal strategy for the defender by applying a greedy method. We evaluate our game theoretic framework using experiments and find that our approach significantly reduces the chance of an attacker determining the location of people inside a perimeter.

Today, mobile data owners lack consent and control over the release and utilization of their location data. Third party applications continuously process and access location data without data owners granular control and without knowledge of how location data is being used. The proliferation of GPS enabled IoT devices will lead to larger scale abuses of trust. In this paper we present the first design and implementation of a privacy module built into the GPSD daemon. The GPSD daemon is a low-level GPS interface that runs on GPS enabled devices. The integration of the privacy module ensures that data owners have granular control over the release of their GPS location. We describe the design of our privacy module integration into the GPSD daemon.

With an immense number of threats pouring in from nation states and hacktivists as well as terrorists and cybercriminals, the requirement of a globally secure infrastructure becomes a major obligation. Most critical infrastructures were primarily designed to work isolated from the normal communication network, but due to the advent of the "Smart Grid" that uses advanced and intelligent approaches to control critical infrastructure, it is necessary for these cyber-physical systems to have access to the communication system. Consequently, such critical systems have become prime targets; hence security of critical infrastructure is currently one of the most challenging research problems. Performing an extensive security analysis involving experiments with cyber-attacks on a live industrial control system (ICS) is not possible. Therefore, researchers generally resort to test beds and complex simulations to answer questions related to SCADA systems. Since all conclusions are drawn from the test bed, it is necessary to perform validation against a physical model. This paper examines the fidelity of a virtual SCADA testbed to a physical test bed and allows for the study of the effects of cyber- attacks on both of the systems.

Security and privacy are crucial for all IT systems and services. The diversity of applications places high demands on the knowledge and experience of software developers and IT professionals. Besides programming skills, security and privacy aspects are required as well and must be considered during development. If developers have not been trained in these topics, it is especially difficult for them to prevent problematic security issues such as vulnerabilities. In this work we present an interactive e-learning platform focusing on solving real-world cybersecurity tasks in a sandboxed web environment. With our platform students can learn and understand how security vulnerabilities can be exploited in different scenarios. The platform has been evaluated in four university IT security courses with around 1100 participants over three years.

Nowadays, both the amount of cyberattacks and their sophistication have considerably increased, and their prevention concerns many organizations. Cooperation by means of information sharing is a promising strategy to address this problem, but unfortunately it poses many challenges. Indeed, looking for a win-win environment is not straightforward and organizations are not properly motivated to share information. This work presents a model to analyse the benefits and drawbacks of information sharing among organizations that present a certain level of dependency. The proposed model applies functional dependency network analysis to emulate attacks propagation and game theory for information sharing management. We present a simulation framework implementing the model that allows for testing different sharing strategies under several network and attack settings. Experiments using simulated environments show how the proposed model provides insights on which conditions and scenarios are beneficial for information sharing.

Cyber ranges are well-defined controlled virtual environments used in cybersecurity training as an efficient way for trainees to gain practical knowledge through hands-on activities. However, creating an environment that contains all the necessary features and settings, such as virtual machines, network topology and security-related content, is not an easy task, especially for a large number of participants. Therefore, we propose CyRIS (Cyber Range Instantiation System) as a solution towards this problem. CyRIS provides a mechanism to automatically prepare and manage cyber ranges for cybersecurity education and training based on specifications defined by the instructors. In this paper, we first describe the design and implementation of CyRIS, as well as its utilization. We then present an evaluation of CyRIS in terms of feature coverage compared to the Technical Guide to Information Security Testing and Assessment of the U.S National Institute of Standards and Technology, and in terms of functionality compared to other similar tools. We also discuss the execution performance of CyRIS for several representative scenarios.

Fast and accurate identification of active recursive domain name servers (RDNS) is a fundamental step to evaluate security risk degrees of DNS systems. Much identification work have been proposed based on network traffic measurement technology. Even though identifying RDNS accurately, they waste huge network resources, and fail to obtain host activity and distinguish between direct and indirect RDNS. In this paper, we proposed an approach to identify direct and forward RDNS based on our three key insights on their request-response behaviors, and proposed an approach to identify indirect RDNS based on CNAME redirect behaviors. To work in high-speed backbone networks, we further proposed an online connectivity estimation algorithm to obtain estimated values used in our identification approaches. According to our experiments, we can identify RDNS with a high accuracy by selecting the reasonable thresholds. The accuracy of identifying direct and forward RDNS can reach 89%.The accuracy of identifying indirect RDNS can reach 90%.Moreover, our work is capable of real-time analyzing high speed backbone traffics.

This paper presents a multi-year undergraduate computing capstone project that holistically contributes to the development of cybersecurity knowledge and skills in non-computing high school and college students. We describe the student-built Vulnerable Web Server application, which is a system that packages instructional materials and pre-built virtual machines to provide lessons on cybersecurity to non-technical students. The Vulnerable Web Server learning materials have been piloted at several high schools and are now integrated into multiple security lessons in an intermediate, general education information technology course at the United States Military Academy. Our paper interweaves a description of the Vulnerable Web Server materials with the senior capstone design process that allowed it to be built by undergraduate information technology and computer science students, resulting in a valuable capstone learning experience. Throughout the paper, a call is made for greater emphasis on educating the non-technical user.

Botnets are accountable for numerous cybersecurity threats. A lot of efforts have been dedicated to botnet intelligence, but botnets versatility and rapid adaptation make them particularly difficult to outwit. Prompt countermeasures require effective tools to monitor the evolution of botnets. Therefore, in this paper we analyze 5 months of traffic from different botnet families, and propose an unsupervised clustering technique to identify the different roles assigned to C&C servers. This technique allows us to classify servers with similar behavior and effectively identify bots contacting several servers. We also present a temporal analysis method that uncovers synchronously activated servers. Our results characterize 6 C&C server roles that are common to various botnet families. In the monitored traffic we found that servers are usually involved in a specific role, and we observed a significant number of C&C servers scanning the Internet.

As information systems become increasingly interdependent, there is an increased need to share cybersecurity data across government agencies and companies, and within and across industrial sectors. This sharing includes threat, vulnerability and incident reporting data, among other data. For cyberattacks that include sociotechnical vectors, such as phishing or watering hole attacks, this increased sharing could expose customer and employee personal data to increased privacy risk. In the US, privacy risk arises when the government voluntarily receives data from companies without meaningful consent from individuals, or without a lawful procedure that protects an individual's right to due process. In this paper, we describe a study to examine the trade-off between the need for potentially sensitive data, which we call incident data usage, and the perceived privacy risk of sharing that data with the government. The study is comprised of two parts: a data usage estimate built from a survey of 76 security professionals with mean eight years' experience; and a privacy risk estimate that measures privacy risk using an ordinal likelihood scale and nominal data types in factorial vignettes. The privacy risk estimate also factors in data purposes with different levels of societal benefit, including terrorism, imminent threat of death, economic harm, and loss of intellectual property. The results show which data types are high-usage, low-risk versus those that are low-usage, high-risk. We discuss the implications of these results and recommend future work to improve privacy when data must be shared despite the increased risk to privacy.

College students as digital natives suffer from cyberattacks that include social engineering and phishing attacks. Moreover, students as college computer users and as future employees may inadvertently commit cybercrimes as insiders. Cybersecurity awareness programs and training have been found to be effective in reducing the risk of successful cyberattacks related to human users. In this outline, we propose a three dimensional (3D) approach to cybersecurity awareness and training for college students.

This paper presents a supervisory control and data acquisition (SCADA) testbed recently built at the University of New Orleans. The testbed consists of models of three industrial physical processes: a gas pipeline, a power transmission and distribution system, and a wastewater treatment plant–these systems are fully-functional and implemented at small-scale. It utilizes real-world industrial equipment such as transformers, programmable logic controllers (PLC), aerators, etc., bringing it closer to modeling real-world SCADA systems. Sensors, actuators, and PLCs are deployed at each physical process system for local control and monitoring, and the PLCs are also connected to a computer running human-machine interface (HMI) software for monitoring the status of the physical processes. The testbed is a useful resource for cybersecurity research, forensic research, and education on different aspects of SCADA systems such as PLC programming, protocol analysis, and demonstration of cyber attacks.

Software security is an important concern in the world moving towards Information Technology. Detecting software vulnerabilities is a difficult and resource consuming task. Therefore, automatic vulnerability prediction would help development teams to predict vulnerability-prone components and prioritize security inspection efforts. Software source code metrics and data mining techniques have been recently used to predict vulnerability-prone components. Some of previous studies used a set of unit complexity and coupling metrics to predict vulnerabilities. In this study, first, we compare the predictability power of these two groups of metrics in cross-project vulnerability prediction. In cross-project vulnerability prediction we create the prediction model based on datasets of completely different projects and try to detect vulnerabilities in another project. The experimental results show that unit complexity metrics are stronger vulnerability predictors than coupling metrics. Then, we propose a new set of coupling metrics which are called Included Vulnerable Header (IVH) metrics. These new coupling metrics, which consider interaction of application modules with outside of the application, predict vulnerabilities highly better than regular coupling metrics. Furthermore, adding IVH metrics to the set of complexity metrics improves Recall of the best predictor from 60.9% to 87.4% and shows the best set of metrics for cross-project vulnerability prediction.

After decades of cyber warfare, it is well-known that the static and predictable behavior of cyber configuration provides a great advantage to adversaries to plan and launch their attack successfully. At the same time, as cyber attacks are getting highly stealthy and more sophisticated, their detection and mitigation become much harder and expensive. We developed a new foundation for moving target defense (MTD) based on cyber mutation, as a new concept in cybersecurity to reverse this asymmetry in cyber warfare by embedding agility into cyber systems. Cyber mutation enables cyber systems to automatically change its configuration parameters in unpredictable, safe and adaptive manner in order to proactively achieve one or more of the following MTD goals: (1) deceiving attackers from reaching their goals, (2) disrupting their plans via changing adversarial behaviors, and (3) deterring adversaries by prohibitively increasing the attack effort and cost. In this talk, we will present the formal foundations, metrics and framework for developing effective cyber mutation techniques. The talk will also review several examples of developed techniques including Random Host Mutation, Random Rout Mutation, fingerprinting mutation, and mutable virtual networks. The talk will also address the evaluation and lessons learned for advancing the future research in this area.

This paper presents a comprehensive review of state-of-the-art research works in knowledge-based user authentication, covering the security and usability aspects of the most prominent user authentication schemes; text-, pin- and graphical-based. From the security perspective, we analyze current threats from a user and service provider perspective. Furthermore, based on current practices in authentication policies, we summarize and discuss their security strengths based on widely applied security metrics. From the usability point of view, we present and discuss the usability of each authentication scheme in regards with task performance and user experience. The analysis reveals that although a plethora of alternative user authentication schemes have been proposed in the literature and users interact differently with the various alternatives, online service providers do not yet adopt alternatives to text-based solutions. We further discuss and identify areas for further research and improved methodology with the aim to drive this research towards the design of sustainable, secure and usable authentication approaches.

As the use of social media technologies proliferates in organizations, it is important to understand the nefarious behaviors, such as cyberbullying, that may accompany such technology use and how to discourage these behaviors. We draw from neutralization theory and the criminological theory of general deterrence to develop and empirically test a research model to explain why cyberbullying may occur and how the behavior may be discouraged. We created a research model of three second-order formative constructs to examine their predictive influence on intentions to cyberbully. We used PLS- SEM to analyze the responses of 174 Facebook users in two different cyberbullying scenarios. Our model suggests that neutralization techniques enable cyberbullying behavior and while sanction certainty is an important deterrent, sanction severity appears ineffective. We discuss the theoretical and practical implications of our model and results.

With the boom of ride-sharing platforms, there has been a growing debate on ride-sharing regulations. In particular, allegations of rape against ride-sharing drivers put sexual assault at the center of this debate. However, there is no systematic and society-wide evidence regarding ride-sharing and sexual assault. Building on a theory of crime victimization, this study examines the effect of ride-sharing on sexual assault incidents using comprehensive data on Uber transactions and crime incidents in New York City over the period from January to March 2015. Our findings demonstrate that the Uber availability is negatively associated with the likelihood of rape, after controlling for endogeneity. Moreover, the deterrent effect of Uber on sexual assault is entirely driven by the taxi-sparse areas, namely outside Manhattan. This study sheds light on the potential of ride-sharing platforms and sharing economy to improve social welfare beyond economic gains.

Content-based routing (CBR) is a powerful model that supports scalable asynchronous communication among large sets of geographically distributed nodes. Yet, preserving privacy represents a major limitation for the wide adoption of CBR, notably when the routers are located in public clouds. Indeed, a CBR router must see the content of the messages sent by data producers, as well as the filters (or subscriptions) registered by data consumers. This represents a major deterrent for companies for which data is a key asset, as for instance in the case of financial markets or to conduct sensitive business-to-business transactions. While there exists some techniques for privacy-preserving computation, they are either prohibitively slow or too limited to be usable in real systems. In this paper, we follow a different strategy by taking advantage of trusted hardware extensions that have just been introduced in off-the-shelf processors and provide a trusted execution environment. We exploit Intel's new software guard extensions (SGX) to implement a CBR engine in a secure enclave. Thanks to the hardware-based trusted execution environment (TEE), the compute-intensive CBR operations can operate on decrypted data shielded by the enclave and leverage efficient matching algorithms. Extensive experimental evaluation shows that SGX adds only limited overhead to insecure plaintext matching outside secure enclaves while providing much better performance and more powerful filtering capabilities than alternative software-only solutions. To the best of our knowledge, this work is the first to demonstrate the practical benefits of SGX for privacy-preserving CBR.

Security is concerned with protecting assets. The aspects of security can be applied to any situation- defense, detection and deterrence. Network security plays important role of protecting information, hardware and software on a computer network. Denial of service (DOS) attacks causes great impacts on the internet world. These attacks attempt to disrupt legitimate user's access to services. By exploiting computer's vulnerabilities, attackers easily consume victim's resources. Many special techniques have been developed to protest against DOS attacks. Some organizations constitute several defense mechanism tools to tackle the security problems. This paper has proposed various types of attacks and solutions associated with each layers of OSI model. These attacks and solutions have different impacts on the different environment. Thus the rapid growth of new technologies may constitute still worse impacts of attacks in the future.

Organisers of large-scale crowdsourcing initiatives need to consider how to produce outcomes with their projects, but also how to build volunteer capacity. The initial project experience of contributors plays an important role in this, particularly when the contribution process requires some degree of expertise. We propose three analytical dimensions to assess first-time contributor engagement based on readily available public data: cohort analysis, task analysis, and observation of contributor performance. We apply these to a large-scale study of remote mapping activities coordinated by the Humanitarian OpenStreetMap Team, a global volunteer effort with thousands of contributors. Our study shows that different coordination practices can have a marked impact on contributor retention, and that complex task designs can be a deterrent for certain contributor groups. We close by providing recommendations about how to build and sustain volunteer capacity in these and comparable crowdsourcing systems.

DDoS-for-hire services, also known as booters, have commoditized DDoS attacks and enabled abusive subscribers of these services to cheaply extort, harass and intimidate businesses and people by taking them offline. However, due to the underground nature of these booters, little is known about their underlying technical and business structure. In this paper, we empirically measure many facets of their technical and payment infrastructure. We also perform an analysis of leaked and scraped data from three major booters–-Asylum Stresser, Lizard Stresser and VDO–-which provides us with an in-depth view of their customers and victims. Finally, we conduct a large-scale payment intervention in collaboration with PayPal and evaluate its effectiveness as a deterrent to their operations. Based on our analysis, we show that these booters are responsible for hundreds of thousands of DDoS attacks and identify potentially promising methods to undermine these services by increasing their costs of operation.