Biblio

Spoofing is a serious threat to the widespread use of Global Navigation Satellite Systems (GNSSs) such as GPS and can be expected to play an important role in the security of many future IoT systems that rely on time, location, or navigation information. In this paper, we focus on the technique of multi-receiver GPS spoofing detection, so far only proposed theoretically. This technique promises to detect malicious spoofing signals by making use of the reported positions of several GPS receivers deployed in a fixed constellation. We scrutinize the assumptions of prior work, in particular the error models, and investigate how these models and their results can be improved due to the correlation of errors at co-located receiver positions. We show that by leveraging spatial noise correlations, the false acceptance rate of the countermeasure can be improved while preserving the sensitivity to attacks. As a result, receivers can be placed significantly closer together than previously expected, which broadens the applicability of the countermeasure. Based on theoretical and practical investigations, we build the first realization of a multi-receiver countermeasure and experimentally evaluate its performance both in authentic and in spoofing scenarios.

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.

In last twenty years, use of internet applications, web hacking activities have exaggerated speedily. Organizations facing very significant challenges in securing their web applications from rising cyber threats, as compromise with the protection issues don't seem to be reasonable. Vulnerability Assessment and Penetration Testing (VAPT) techniques help them to go looking out security loopholes. These security loopholes could also be utilized by attackers to launch attacks on technical assets. Thus it is necessary ascertain these vulnerabilities and install security patches. VAPT helps organization to determine whether their security arrangements are working properly. This paper aims to elucidate overview and various techniques used in vulnerability assessment and penetration testing (VAPT). Also focuses on making cyber security awareness and its importance at various level of an organization for adoption of required up to date security measures by the organization to stay protected from various cyber-attacks.

In this work we present a study that evaluates and compares two block ciphers, AES and PRESENT, in the context of lightweight cryptography for smartphones security applications. To the best of our knowledge, this is the first comparison between these ciphers using a smartphone as computing platform. AES is the standard for symmetric encryption and PRESENT is one of the first ultra-lightweight ciphers proposed in the literature and included in the ISO/IEC 29192-2. In our study, we consider execution time, voltage consumption and memory usage as metrics for comparison purposes. The two block ciphers were evaluated through several experiments in a low-cost smartphone using Android built in tools. From the results we conclude that, for general purpose encryption AES performs statistically better although block-to-block PRESENT delivers better results.

IP tracking and cloaking are practices for identifying users which are used legitimately by websites to provide services and content tailored to particular users. However, it is believed that these practices are also used by malicious websites to avoid detection by anti-virus companies crawling the web to find malware. In addition, malicious websites are also believed to use IP tracking in order to deliver targeted malware based upon a history of previous visits by users. In this paper we empirically investigate these beliefs and collect a large dataset of suspicious URLs in order to identify at what level IP tracking takes place that is at the level of an individual address or at the level of their network provider or organisation (Network tracking). Our results illustrate that IP tracking is used in a small subset of domains within our dataset while no strong indication of network tracking was observed.

This study was conducted to determine whether monitoring moderated the impact of trust on the project performance of 57 virtual teams. Two sources of monitoring were examined: internal monitoring done by team members and external monitoring done by someone outside of the team. Two types of trust were also examined: affective-based trust, or trust based on emotion; and cognitive trust, or trust based on competency. Results indicate that when internal monitoring was high, affective trust was associated with increases in performance. However, affective trust was associated with decreases in performance when external monitoring was high. Both types of monitoring reduced the strong positive relationship between cognitive trust and the performance of virtual teams. Results of this study provide new insights about monitoring and trust in virtual teams and inform both theory and design.

In this work, we introduce a new Markov operator associated with a digraph, which we refer to as a nonlinear Laplacian. Unlike previous Laplacians for digraphs, the nonlinear Laplacian does not rely on the stationary distribution of the random walk process and is well defined on digraphs that are not strongly connected. We show that the nonlinear Laplacian has nontrivial eigenvalues and give a Cheeger-like inequality, which relates the conductance of a digraph and the smallest non-zero eigenvalue of its nonlinear Laplacian. Finally, we apply the nonlinear Laplacian to the analysis of real-world networks and obtain encouraging results.

In this special session, members of the ACM Joint Task Force on Cyber Education to Develop Undergraduate Curricular Guidance will provide an overview of the task force mission, objectives, and work plan. After the overview, task force members will engage session participants in the curricular development process.

We initiate the study of a quantity that we call coordination complexity. In a distributed optimization problem, the information defining a problem instance is distributed among n parties, who need to each choose an action, which jointly will form a solution to the optimization problem. The coordination complexity represents the minimal amount of information that a centralized coordinator, who has full knowledge of the problem instance, needs to broadcast in order to coordinate the n parties to play a nearly optimal solution. We show that upper bounds on the coordination complexity of a problem imply the existence of good jointly differentially private algorithms for solving that problem, which in turn are known to upper bound the price of anarchy in certain games with dynamically changing populations. We show several results. We fully characterize the coordination complexity for the problem of computing a many-to-one matching in a bipartite graph. Our upper bound in fact extends much more generally to the problem of solving a linearly separable convex program. We also give a different upper bound technique, which we use to bound the coordination complexity of coordinating a Nash equilibrium in a routing game, and of computing a stable matching.

Users’ online behaviors such as ratings and examination of items are recognized as one of the most valuable sources of information for learning users’ preferences in order to make personalized recommendations. But most previous works focus on modeling only one type of users’ behaviors such as numerical ratings or browsing records, which are referred to as explicit feedback and implicit feedback, respectively. In this article, we study a Semisupervised Collaborative Recommendation (SSCR) problem with labeled feedback (for explicit feedback) and unlabeled feedback (for implicit feedback), in analogy to the well-known Semisupervised Learning (SSL) setting with labeled instances and unlabeled instances. SSCR is associated with two fundamental challenges, that is, heterogeneity of two types of users’ feedback and uncertainty of the unlabeled feedback. As a response, we design a novel Self-Transfer Learning (sTL) algorithm to iteratively identify and integrate likely positive unlabeled feedback, which is inspired by the general forward/backward process in machine learning. The merit of sTL is its ability to learn users’ preferences from heterogeneous behaviors in a joint and selective manner. We conduct extensive empirical studies of sTL and several very competitive baselines on three large datasets. The experimental results show that our sTL is significantly better than the state-of-the-art methods.

The rise of sensor-equipped smart phones has enabled a variety of classification based applications that provide personalized services based on user data extracted from sensor readings. However, malicious applications aggressively collect sensitive information from inherent user data without permissions. Furthermore, they can mine sensitive information from user data just in the classification process. These privacy threats raise serious privacy concerns. In this paper, we introduce two new privacy concerns which are inherent-data privacy and latent-data privacy. We propose a framework that enables a data-obfuscation mechanism to be developed easily. It preserves latent-data privacy while guaranteeing satisfactory service quality. The proposed framework preserves privacy against powerful adversaries who have knowledge of users' access pattern and the data-obfuscation mechanism. We validate our framework towards a real classification-orientated dataset. The experiment results confirm that our framework is superior to the basic obfuscation mechanism.

Many applications of mobile computing require the computation of dot-product of two vectors. For examples, the dot-product of an individual's genome data and the gene biomarkers of a health center can help detect diseases in m-Health, and that of the interests of two persons can facilitate friend discovery in mobile social networks. Nevertheless, exposing the inputs of dot-product computation discloses sensitive information about the two participants, leading to severe privacy violations. In this paper, we tackle the problem of privacy-preserving dot-product computation targeting mobile computing applications in which secure channels are hardly established, and the computational efficiency is highly desirable. We first propose two basic schemes and then present the corresponding advanced versions to improve efficiency and enhance privacy-protection strength. Furthermore, we theoretically prove that our proposed schemes can simultaneously achieve privacy-preservation, non-repudiation, and accountability. Our numerical results verify the performance of the proposed schemes in terms of communication and computational overheads.

Standardization and harmonization efforts have reached a consensus towards using a special-purpose Vehicular Public-Key Infrastructure (VPKI) in upcoming Vehicular Communication (VC) systems. However, there are still several technical challenges with no conclusive answers; one such an important yet open challenge is the acquisition of short-term credentials, pseudonym: how should each vehicle interact with the VPKI, e.g., how frequently and for how long? Should each vehicle itself determine the pseudonym lifetime? Answering these questions is far from trivial. Each choice can affect both the user privacy and the system performance and possibly, as a result, its security. In this paper, we make a novel systematic effort to address this multifaceted question. We craft three generally applicable policies and experimentally evaluate the VPKI system performance, leveraging two large-scale mobility datasets. We consider the most promising, in terms of efficiency, pseudonym acquisition policies; we find that within this class of policies, the most promising policy in terms of privacy protection can be supported with moderate overhead. Moreover, in all cases, this work is the first to provide tangible evidence that the state-of-the-art VPKI can serve sizable areas or domain with modest computing resources.

Evolutionary Computation (EC) has been used with great success on various real-world problems. One domain abundant with numerous difficult problems is cryptology. Cryptology can be divided into cryptography, that informally speaking considers methods how to ensure secrecy (but also authenticity, privacy, etc.), and cryptanalysis, that deals with methods how to break cryptographic systems. Although not always in an obvious way, EC can be applied to problems from both domains. This tutorial will first give a brief introduction to cryptology intended for general audience (therefore, omitting proofs and mathematics behind many concepts). Afterwards, we concentrate on several topics from cryptography that are successfully tackled up to now with EC and discuss why those topics are suitable to apply EC. However, care must be taken since there exists a number of problems that seem to be impossible to solve with EC and one needs to realize the limitations of the heuristics. We will discuss the choice of appropriate EC techniques (GA, GP, CGP, ES, multi-objective optimization, etc) for various problems and evaluate on the importance of that choice. Furthermore, we will discuss the gap between the cryptographic community and EC community and what does that mean for the results. By doing that, we will give a special emphasis on the perspective that cryptography presents a source of benchmark problems for the EC community. To conclude, we will present a number of topics we consider to be a strong research choice that can have a real-world impact. In that part, we give a special attention to cryptographic problems where cryptographic community successfully applied EC, but where those problems remained out of the focus of EC community. This tutorial will also present some live demos of EC in action when dealing with cryptographic problems. We will present several problems, ways of encoding solutions, impact of the algorithms choice and finally, we will run some experiments to show the results and discuss how to assess them from cryptographic perspective.

The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to the public crowd equipped with various mobile devices. A fundamental issue in such systems is to effectively incentivize worker participation. However, instead of being an isolated module, the incentive mechanism usually interacts with other components which may affect its performance, such as data aggregation component that aggregates workers' data and data perturbation component that protects workers' privacy. Therefore, different from past literature, we capture such interactive effect, and propose INCEPTION, a novel MCS system framework that integrates an incentive, a data aggregation, and a data perturbation mechanism. Specifically, its incentive mechanism selects workers who are more likely to provide reliable data, and compensates their costs for both sensing and privacy leakage. Its data aggregation mechanism also incorporates workers' reliability to generate highly accurate aggregated results, and its data perturbation mechanism ensures satisfactory protection for workers' privacy and desirable accuracy for the final perturbed results. We validate the desirable properties of INCEPTION through theoretical analysis, as well as extensive simulations.

Contactless communications have become omnipresent in our daily lives, from simple access cards to electronic passports. Such systems are particularly vulnerable to relay attacks, in which an adversary relays the messages from a prover to a verifier. Distance-bounding protocols were introduced to counter such attacks. Lately, there has been a very active research trend on improving the security of these protocols, but also on ensuring strong privacy properties with respect to active adversaries and malicious verifiers. In particular, a difficult threat to address is the terrorist fraud, in which a far-away prover cooperates with a nearby accomplice to fool a verifier. The usual defence against this attack is to make it impossible for the accomplice to succeed unless the prover provides him with enough information to recover his secret key and impersonate him later on. However, the mere existence of a long-term secret key is problematic with respect to privacy. In this paper, we propose a novel approach in which the prover does not leak his secret key but a reusable session key along with a group signature on it. This allows the adversary to impersonate him even without knowing his signature key. Based on this approach, we give the first distance-bounding protocol, called SPADE, integrating anonymity, revocability and provable resistance to standard threat models.

The ever increasing interest in semantic technologies and the availability of several open knowledge sources have fueled recent progress in the field of recommender systems. In this paper we feed recommender systems with features coming from the Linked Open Data (LOD) cloud - a huge amount of machine-readable knowledge encoded as RDF statements - with the aim of improving recommender systems effectiveness. In order to exploit the natural graph-based structure of RDF data, we study the impact of the knowledge coming from the LOD cloud on the overall performance of a graph-based recommendation algorithm. In more detail, we investigate whether the integration of LOD-based features improves the effectiveness of the algorithm and to what extent the choice of different feature selection techniques influences its performance in terms of accuracy and diversity. The experimental evaluation on two state of the art datasets shows a clear correlation between the feature selection technique and the ability of the algorithm to maximize a specific evaluation metric. Moreover, the graph-based algorithm leveraging LOD-based features is able to overcome several state of the art baselines, such as collaborative filtering and matrix factorization, thus confirming the effectiveness of the proposed approach.

Bitcoin, a decentralized cryptographic currency that has experienced proliferating popularity over the past few years, is the common denominator in a wide variety of cybercrime. We perform a measurement analysis of CryptoLocker, a family of ransomware that encrypts a victim's files until a ransom is paid, within the Bitcoin ecosystem from September 5, 2013 through January 31, 2014. Using information collected from online fora, such as reddit and BitcoinTalk, as an initial starting point, we generate a cluster of 968 Bitcoin addresses belonging to CryptoLocker. We provide a lower bound for CryptoLocker's economy in Bitcoin and identify 795 ransom payments totalling 1,128.40 BTC (\$310,472.38), but show that the proceeds could have been worth upwards of \$1.1 million at peak valuation. By analyzing ransom payment timestamps both longitudinally across CryptoLocker's operating period and transversely across times of day, we detect changes in distributions and form conjectures on CryptoLocker that corroborate information from previous efforts. Additionally, we construct a network topology to detail CryptoLocker's financial infrastructure and obtain auxiliary information on the CryptoLocker operation. Most notably, we find evidence that suggests connections to popular Bitcoin services, such as Bitcoin Fog and BTC-e, and subtle links to other cybercrimes surrounding Bitcoin, such as the Sheep Marketplace scam of 2013. We use our study to underscore the value of measurement analyses and threat intelligence in understanding the erratic cybercrime landscape.

Along with the ever increasing growth in data collection and its mining, there is an increasing fear of compromising individual and population privacy. Several techniques have been proposed in literature to preserve privacy of collected data while storing and processing. In this paper, we propose a privacy-aware architecture for storing and processing data in a Big Data warehouse. In particular, we propose a flexible, extendable, and adaptable architecture that enforces user specified privacy requirements in the form of Embedded Privacy Agreements. The paper discusses the details of the architecture with some implementation details.

Adaptivity is an important feature of data analysis - the choice of questions to ask about a dataset often depends on previous interactions with the same dataset. However, statistical validity is typically studied in a nonadaptive model, where all questions are specified before the dataset is drawn. Recent work by Dwork et al. (STOC, 2015) and Hardt and Ullman (FOCS, 2014) initiated a general formal study of this problem, and gave the first upper and lower bounds on the achievable generalization error for adaptive data analysis. Specifically, suppose there is an unknown distribution P and a set of n independent samples x is drawn from P. We seek an algorithm that, given x as input, accurately answers a sequence of adaptively chosen ``queries'' about the unknown distribution P. How many samples n must we draw from the distribution, as a function of the type of queries, the number of queries, and the desired level of accuracy? In this work we make two new contributions towards resolving this question: We give upper bounds on the number of samples n that are needed to answer statistical queries. The bounds improve and simplify the work of Dwork et al. (STOC, 2015), and have been applied in subsequent work by those authors (Science, 2015; NIPS, 2015). We prove the first upper bounds on the number of samples required to answer more general families of queries. These include arbitrary low-sensitivity queries and an important class of optimization queries (alternatively, risk minimization queries). As in Dwork et al., our algorithms are based on a connection with algorithmic stability in the form of differential privacy. We extend their work by giving a quantitatively optimal, more general, and simpler proof of their main theorem that the stability notion guaranteed by differential privacy implies low generalization error. We also show that weaker stability guarantees such as bounded KL divergence and total variation distance lead to correspondingly weaker generalization guarantees.

Crowdsourcing is an unique and practical approach to obtain personalized data and content. Its impact is especially significant in providing commentary, reviews and metadata, on a variety of location based services. In this study, we examine reliability of the Waze mapping service, and its vulnerability to a variety of location-based attacks. Our goals are to understand the severity of the problem, shed light on the general problem of location and device authentication, and explore the efficacy of potential defenses. Our preliminary results already show that a single attacker with limited resources can cause havoc on Waze, producing ``virtual'' congestion and accidents, automatically re-routing user traffic, and compromising user privacy by tracking users' precise movements via software while staying undetected. To defend against these attacks, we propose a proximity-based Sybil detection method to filter out malicious devices.

Hardware support for isolated execution (such as Intel SGX) enables development of applications that keep their code and data confidential even while running in a hostile or compromised host. However, automatically verifying that such applications satisfy confidentiality remains challenging. We present a methodology for designing such applications in a way that enables certifying their confidentiality. Our methodology consists of forcing the application to communicate with the external world through a narrow interface, compiling it with runtime checks that aid verification, and linking it with a small runtime that implements the narrow interface. The runtime includes services such as secure communication channels and memory management. We formalize this restriction on the application as Information Release Confinement (IRC), and we show that it allows us to decompose the task of proving confidentiality into (a) one-time, human-assisted functional verification of the runtime to ensure that it does not leak secrets, (b) automatic verification of the application's machine code to ensure that it satisfies IRC and does not directly read or corrupt the runtime's internal state. We present /CONFIDENTIAL: a verifier for IRC that is modular, automatic, and keeps our compiler out of the trusted computing base. Our evaluation suggests that the methodology scales to real-world applications.

The domain name system (DNS) offers an ideal distributed database for big data mining related to different cyber security questions. Besides infrastructural problems, scalability issues, and security challenges related to the protocol itself, information from DNS is often required also for more nuanced cyber security questions. Against this backdrop, this paper discusses the fundamental characteristics of DNS in relation to cyber security and different research prototypes designed for passive but continuous DNS-based monitoring of domains and addresses. With this discussion, the paper also illustrates a few general software design aspects.

While email plays a growingly important role on the Internet, we are faced with more severe challenges brought by compromised email accounts, especially for the administrators of institutional email service providers. Inspired by the previous experience on spam filtering and compromised accounts detection, we propose several criteria, like Success Outdegree Proportion, Reverse Pagerank, Recipient Clustering Coefficient and Legitimate Recipient Proportion, for compromised email accounts detection from the perspective of graph topology in this paper. Specifically, several widely used social network analysis metrics are used and adapted according to the characteristics of mail log analysis. We evaluate our methods on a dataset constructed by mining the one month (30 days) mail log from an university with 118,617 local users and 11,460,399 mail log entries. The experimental results demonstrate that our methods achieve very positive performance, and we also prove that these methods can be efficiently applied on even larger datasets.

Information Systems curricula require on-going and frequent review [2] [11]. Furthermore, such curricula must be flexible because of the fast-paced, dynamic nature of the workplace. Such flexibility can be maintained through modernizing course content or, inclusively, exchanging hardware or software for newer versions. Alternatively, flexibility can arise from incorporating new information into curricula from other disciplines. One field where the pace of change is extremely high is cybersecurity [3]. Students are left with outdated skills when curricula lag behind the pace of change in industry. For example, cryptography is a required learning objective in the DHS/NSA Center of Academic Excellence (CAE) knowledge criteria [1]. However, the overarching curriculum associated with basic ciphers has gone unchanged for decades. Indeed, a general problem in cybersecurity education is that students lack fundamental knowledge in areas such as ciphers [5]. In response, researchers have developed a variety of interactive classroom visualization tools [5] [8] [9]. Such tools visualize the standard approach to frequency analysis of simple substitution ciphers that includes review of most common, single letters in ciphertext. While fundamental ciphers such as the monoalphabetic substitution cipher have not been updated (these are historical ciphers), collective understanding of how humans interact with language has changed. Updated understanding in both English language pedagogy [10] [12] and automated cryptanalysis of substitution ciphers [4] potentially renders the interactive classroom visualization tools incomplete or outdated. Classroom visualization tools are powerful teaching aids, particularly for abstract concepts. Existing research has established that such tools promote an active learning environment that translates to not only effective learning conditions but also higher student retention rates [7]. However, visualization tools require extensive planning and design when used to actively engage students with detailed, specific knowledge units such as ciphers [7] [8]. Accordingly, we propose a heatmap-based frequency analysis visualization solution that (a) incorporates digraph and trigraph language processing norms; (b) and enhances the active learning pedagogy inherent in visualization tools. Preliminary results indicate that study participants take approximately 15% longer to learn the heatmap-based frequency analysis technique compared to traditional frequency analysis but demonstrate a 50% increase in efficacy when tasked with solving simple substitution ciphers. Further, a heatmap-based solution contributes positively to the field insofar as educators have an additional tool to use in the classroom. As well, the heatmap visualization tool may allow researchers to comparatively examine efficacy of visualization tools in the cryptanalysis of mono-alphabetic substitution ciphers.