Biblio

Eight hundred eighty-seven phishing emails from Arizona State University, Brown University, and Cornell University were assessed by two reviewers for Cialdini’s six principles of persuasion: authority, social proof, liking/similarity, commitment/consistency, scarcity, and reciprocation. A correlational analysis of email characteristics by year revealed that the persuasion principles of commitment/consistency and scarcity have increased over time, while the principles of reciprocation and social proof have decreased over time. Authority and liking/similarity revealed mixed results with certain characteristics increasing and others decreasing. Results from this study can inform user training of phishing emails and help cybersecurity software to become more effective. 

Cyber-Physical Systems (CPS) are systems with seamless integration of physical, computational and networking components. These systems can potentially have an impact on the physical components, hence it is critical to safeguard them against a wide range of attacks. In this paper, it is argued that an effective approach to achieve this goal is to systematically identify the potential threats at the design phase of building such systems, commonly achieved via threat modeling. In this context, a tool to perform systematic analysis of threat modeling for CPS is proposed. A real-world wireless railway temperature monitoring system is used as a case study to validate the proposed approach. The threats identified in the system are subsequently mitigated using National Institute of Standards and Technology (NIST) standards.

The diverse views of science of security have opened up several alleys towards applying the methods of science to security. We pursue a different kind of connection between science and security. This paper explores the idea that security is not just a suitable subject for science,. but that the process of security is also similar to the process of science. This similarity arises from the fact that both science and security depend on the methods of inductive inference. Because of this dependency, a scientific theory can never be definitely proved, but can only be disproved by new evidence, and improved into a better theory. Because of the same dependency, every security claim and method has a lifetime, and always eventually needs to be improved.

In this general framework of security-as-science, we explore the ways to apply the methods of scientific induction in the process of trust. The process of trust building and updating is viewed as hypothesis testing. We propose to formulate the trust hypotheses by the methods of algorithmic learning, and to build more robust trust testing and vetting methodologies on the solid foundations of statistical inference.

In this paper, we formulate a three-player three-stage Colonel Blotto game, in which two players fight against a common adversary. We assume that the game is one of complete information, that is, the players have complete and consistent information on the underlying model of the game; further, each player observes the actions taken by all players up to the previous stage.  The setting  under  consideration is similar  to the one considered in our recent  work [1], but with a different  information structure  during  the  second  stage  of the  game;  this  leads  to  a  significantly different  solution.

In the first stage, players can add additional battlefields. In the second stage, the players (except the adversary) are allowed to transfer resources among  each  other  if it  improves their  expected payoffs, and simultaneously, the adversary decides  on the amount  of resource it allocates  to the battle with each player subject to its resource constraint. At the third stage, the players and the adversary fight against each other with updated resource levels and battlefields. We compute the subgame-perfect Nash equilibrium for this game. Further, we show that when playing according to the equilibrium, there are parameter regions  in which (i) there  is a net  positive transfer, (ii)  there  is absolutely no transfer, (iii) the  adversary fights  with  only  one player, and  (iv)  adding  battlefields is beneficial to a player. In doing so, we also exhibit a counter-intuitive property of Nash equilibrium in games: extra information to a player in the game does not necessarily lead to a better performance for that player.  The result finds application in resource allocation problems for securing cyber-physical systems.

We consider a three-step three-player complete information Colonel Blotto game in this paper, in which the first two players fight against a common adversary. Each player is endowed with a certain amount of resources at the beginning of the game, and the number of battlefields on which a player and the adversary fights is specified. The first two players are allowed to form a coalition if it improves their payoffs. In the first stage, the first two players may add battlefields and incur costs. In the second stage, the first two players may transfer resources among each other. The adversary observes this transfer, and decides on the allocation of its resources to the two battles with the players. At the third step, the adversary and the other two players fight on the updated number of battlefields and receive payoffs. We characterize the subgame-perfect Nash equilibrium (SPNE) of the game in various parameter regions. In particular, we show that there are certain parameter regions in which if the players act according to the SPNE strategies, then (i) one of the first two players add battlefields and transfer resources to the other player (a coalition is formed), (ii) there is no addition of battlefields and no transfer of resources (no coalition is formed). We discuss the implications of the results on resource allocation for securing cyberphysical systems.

Knowing inputs that cover a specific branch or statement in a program is useful for debugging and regression testing. Symbolic backward execution (SBE) is a natural approach to find such targeted inputs. However, SBE struggles with complicated arithmetic, external method calls, and data-dependent loops that occur in many real-world programs. We propose symcretic execution, a novel combination of SBE and concrete forward execution that can efficiently find targeted inputs despite these challenges. An evaluation of our approach on a range of test cases shows that symcretic execution finds inputs in more cases than concolic testing tools while exploring fewer path segments. Integration of our approach will allow test generation tools to fill coverage gaps and static bug detectors to verify candidate bugs with concrete test cases. This is the full version of an extended abstract that was presented at the 29th IEEE/ACM International Conference on Automated Software Engineering (ASE 2014), September 15–19, 2014, Västerås, Sweden.

Knowing inputs that cover a specific branch or statement in a program is useful for debugging and regression testing. Symbolic backward execution (SBE) is a natural approach to find such targeted inputs. However, SBE struggles with complicated arithmetic, external method calls, and data- dependent loops that occur in many real-world programs. We propose symcretic execution, a novel combination of SBE and concrete forward execution that can efficiently find targeted inputs despite these challenges. An evaluation of our approach on a range of test cases shows that symcretic execution finds inputs in more cases than concolic testing tools while exploring fewer path segments. Integration of our approach will allow test generation tools to fill coverage gaps and static bug detectors to verify candidate bugs with concrete test cases.

Recent advances in the automated analysis of cryptographic protocols have aroused new interest in the practical application of unification modulo theories, especially theories that describe the algebraic properties of cryptosystems. However, this application requires unification algorithms that can be easily implemented and easily extended to combinations of different theories of interest. In practice this has meant that most tools use a version of a technique known as variant unification. This requires, among other things, that the theory be decomposable into a set of axioms B and a set of rewrite rules R such that R has the finite variant property with respect to B. Most theories that arise in cryptographic protocols have decompositions suitable for variant unification, but there is one major exception: the theory that describes encryption that is homomorphic over an Abelian group.

In this paper we address this problem by studying various approximations of homomorphic encryption over an Abelian group. We construct a hierarchy of increasingly richer theories, taking advantage of new results that allow us to automatically verify that their decompositions have the finite variant property. This new verification procedure also allows us to construct a rough metric of the complexity of a theory with respect to variant unification, or variant complexity. We specify different versions of protocols using the different theories, and analyze them in the Maude-NPA cryptographic protocol analysis tool to assess their behavior. This gives us greater understanding of how the theories behave in actual application, and suggests possible techniques for improving performance.

This paper presents the architecture of an end-to-end secu- rity testbed and security analytics framework, which aims to: i) understand real-world exploitation of known security vulnerabilities and ii) preemptively detect multi-stage at- tacks, i.e., before the system misuse. With the increasing number of security vulnerabilities, it is necessary for secu- rity researchers and practitioners to understand: i) system and network behaviors under attacks and ii) potential ef- fects of attacks to the target infrastructure. To safely em- ulate and instrument exploits of known vulnerabilities, we use virtualization techniques to isolate attacks in contain- ers, e.g., Linux-based containers or Virtual Machines, and to deploy monitors, e.g., kernel probes or network packet captures, across a system and network stack. To infer the evolution of attack stages from monitoring data, we use a probabilistic graphical model, namely AttackTagger, that represents learned knowledge of simulated attacks in our se- curity testbed and real-world attacks. Experiments are be- ing run on a real-world deployment of the framework at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign.

While computer programs and logical theories begin by declaring the concepts of interest, be it as data types or as predicates, network computation does not allow such global declarations, and requires concept mining and concept analysis to extract shared semantics for different network nodes. Powerful semantic analysis systems have been the drivers of nearly all paradigm shifts on the web. In categorical terms, most of them can be described as bicompletions of enriched matrices, generalizing the Dedekind-MacNeille-style completions from posets to suitably enriched categories. Yet it has been well known for more than 40 years that ordinary categories themselves in general do not permit such completions. Armed with this new semantical view of Dedekind-MacNeille completions, and of matrix bicompletions, we take another look at this ancient mystery. It turns out that simple categorical versions of the limit superior and limit inferior operations characterize a general notion of Dedekind-MacNeille completion, that seems to be appropriate for ordinary categories, and boils down to the more familiar enriched versions when the limits inferior and superior coincide. This explains away the apparent gap among the completions of ordinary categories, and broadens the path towards categorical concept mining and analysis, opened in previous work.

A fundamental drawback of current anomaly detection systems (ADSs) is the ability of a skilled attacker to evade detection. This is due to the flawed assumption that an attacker does not have any information about an ADS. Advanced persistent threats that are capable of monitoring network behavior can always estimate some information about ADSs which makes these ADSs susceptible to evasion attacks. Hence in this paper, we first assume the role of an attacker to launch evasion attacks on anomaly detection systems. We show that the ADSs can be completely paralyzed by parameter estimation attacks. We then present a mathematical model to measure evasion margin with the aim to understand the science of evasion due to ADS design. Finally, to minimize the evasion margin, we propose a key-based randomization scheme for existing ADSs and discuss its robustness against evasion attacks. Case studies are presented to illustrate the design methodology and extensive experimentation is performed to corroborate the results.
 

With the rapid development of Wireless Sensor Networks (WSNs), besides the energy efficient, Quality of Service (QoS) supported and the validity of packet transmission should be considered under some circumstances. In this paper, according to summing up LEACH protocol's advantages and defects, combining with trust evaluation mechanism, energy and QoS control, a trust-based QoS routing algorithm is put forward. Firstly, energy control and coverage scale are adopted to keep load balance in the phase of cluster head selection. Secondly, trust evaluation mechanism is designed to increase the credibility of the network in the stage of node clusting. Finally, in the period of information transmission, verification and ACK mechanism also put to guarantee validity of data transmission. In this paper, it proposes the improved protocol. The improved protocol can not only prolong nodes' life expectancy, but also increase the credibility of information transmission and reduce the packet loss. Compared to typical routing algorithms in sensor networks, this new algorithm has better performance.

Wireless security has been an active research area since the last decade. A lot of studies of wireless security use cryptographic tools, but traditional cryptographic tools are normally based on computational assumptions, which may turn out to be invalid in the future. Consequently, it is very desirable to build cryptographic tools that do not rely on computational assumptions. In this paper, we focus on a crucial cryptographic tool, namely 1-out-of-2 oblivious transfer. This tool plays a central role in cryptography because we can build a cryptographic protocol for any polynomial-time computable function using this tool. We present a novel 1-out-of-2 oblivious transfer protocol based on wireless channel characteristics, which does not rely on any computational assumption. We also illustrate the potential broad applications of this protocol by giving two applications, one on private communications and the other on privacy preserving password verification. We have fully implemented this protocol on wireless devices and conducted experiments in real environments to evaluate the protocol. Our experimental results demonstrate that it has reasonable efficiency.
 

In a modern software system, when a program fails, a crash report which contains an execution trace would be sent to the software vendor for diagnosis. A crash report which corresponds to a failure could be caused by multiple types of faults simultaneously. Many large companies such as Baidu organize a team to analyze these failures, and classify them into multiple labels (i.e., multiple types of faults). However, it would be time-consuming and difficult for developers to manually analyze these failures and come out with appropriate fault labels. In this paper, we automatically classify a failure into multiple types of faults, using a composite algorithm named MLL-GA, which combines various multi-label learning algorithms by leveraging genetic algorithm (GA). To evaluate the effectiveness of MLL-GA, we perform experiments on 6 open source programs and show that MLL-GA could achieve average F-measures of 0.6078 to 0.8665. We also compare our algorithm with Ml.KNN and show that on average across the 6 datasets, MLL-GA improves the average F-measure of MI.KNN by 14.43%.
 

Applications such as fleet management and logistics, emergency response, public security and surveillance or mobile workforce management use geo-positioning and mobile networks as means of enabling real-time monitoring, communication and collaboration among a possibly large set of mobile nodes. The majority of those systems require real-time tracking of mobile nodes (e.g. vehicles, people or mobile robots), reliable communication to/from the nodes, as well as group communication among the mobile nodes. In this paper we describe a distributed middleware with focus on management of context-defined groups of mobile nodes, and group communication with large sets of nodes. We also present a prototype Fleet Tracking and Management system based on our middleware, give an example of how context-specific group communication can enhance the node's mutual awareness, and show initial performance results that indicate small overhead and latency of the group communication and management.

Due to the development of cloud computing and NoSQL database, more and more sensitive information are stored in NoSQL databases, which exposes quite a lot security vulnerabilities. This paper discusses security features of MongoDB database and proposes a transparent middleware implementation. The analysis of experiment results show that this transparent middleware can efficiently encrypt sensitive data specified by users on a dataset level. Existing application systems do not need too many modifications in order to apply this middleware.

The term Cloud Computing is not something that appeared overnight, it may come from the time when computer system remotely accessed the applications and services. Cloud computing is Ubiquitous technology and receiving a huge attention in the scientific and industrial community. Cloud computing is ubiquitous, next generation's in-formation technology architecture which offers on-demand access to the network. It is dynamic, virtualized, scalable and pay per use model over internet. In a cloud computing environment, a cloud service provider offers “house of resources” includes applications, data, runtime, middleware, operating system, virtualization, servers, data storage and sharing and networking and tries to take up most of the overhead of client. Cloud computing offers lots of benefits, but the journey of the cloud is not very easy. It has several pitfalls along the road because most of the services are outsourced to third parties with added enough level of risk. Cloud computing is suffering from several issues and one of the most significant is Security, privacy, service availability, confidentiality, integrity, authentication, and compliance. Security is a shared responsibility of both client and service provider and we believe security must be information centric, adaptive, proactive and built in. Cloud computing and its security are emerging study area nowadays. In this paper, we are discussing about data security in cloud at the service provider end and proposing a network storage architecture of data which make sure availability, reliability, scalability and security.

The integration of social networking concepts into the Internet of things has led to the Social Internet of Things (SIoT) paradigm, according to which objects are capable of establishing social relationships in an autonomous way with respect to their owners with the benefits of improving the network scalability in information/service discovery. Within this scenario, we focus on the problem of understanding how the information provided by members of the social IoT has to be processed so as to build a reliable system on the basis of the behavior of the objects. We define two models for trustworthiness management starting from the solutions proposed for P2P and social networks. In the subjective model each node computes the trustworthiness of its friends on the basis of its own experience and on the opinion of the friends in common with the potential service providers. In the objective model, the information about each node is distributed and stored making use of a distributed hash table structure so that any node can make use of the same information. Simulations show how the proposed models can effectively isolate almost any malicious nodes in the network at the expenses of an increase in the network traffic for feedback exchange.

Mobile Ad-hoc Network is highly susceptible towards the security attacks due to its dynamic topology, resource constraint, energy constraint operations, limited physical security and lack of infrastructure. Misleading routing attack (MIRA) in MANET intend to delay packet to its fullest in order to generate time outs at the source as packets will not reach in time. Its main objective is to generate delay and increase network overhead. It is a variation to the sinkhole attack. In this paper, we have proposed a detection scheme to detect the malicious nodes at route discovery as well as at packet transmissions. The simulation results of MIRA attack indicate that though delay is increased by 91.30% but throughput is not affected which indicates that misleading routing attack is difficult to detect. The proposed detection scheme when applied to misleading routing attack suggests a significant decrease in delay.

Tor is a popular low-latency anonymous communication system. However, it is currently abused in various ways. Tor exit routers are frequently troubled by administrative and legal complaints. To gain an insight into such abuse, we design and implement a novel system, TorWard, for the discovery and systematic study of malicious traffic over Tor. The system can avoid legal and administrative complaints and allows the investigation to be performed in a sensitive environment such as a university campus. An IDS (Intrusion Detection System) is used to discover and classify malicious traffic. We performed comprehensive analysis and extensive real-world experiments to validate the feasibility and effectiveness of TorWard. Our data shows that around 10% Tor traffic can trigger IDS alerts. Malicious traffic includes P2P traffic, malware traffic (e.g., botnet traffic), DoS (Denial-of-Service) attack traffic, spam, and others. Around 200 known malware have been identified. To the best of our knowledge, we are the first to perform malicious traffic categorization over Tor.
 

Today, companies across the world are adopting cloud services for efficient and cost effective resource management. However, cloud computing is still in developing stage where there are lots of research problems yet to be solved. One such area is security which addresses issues like privacy, identity management, and trust management among other things. As of now, there exists no standard identity management system for a cloud environment. The aspect of trusted propagation still needs to be tackled. This research work proposes a trusted security architecture for cloud identity management that can dynamically federate user identities. The trust architecture proposed use Bayesian inference and roulette wheel selection technique to evaluate trust scores. Using the proposed trust model, dynamic trust relationships are formed across multiple cloud service providers and identity providers thereby eliminating fragmentation of user identities. The trust model was implemented and tested in Google App Engine. The performance of the trust measures was analysed.

Identity Management systems cannot be centralized anymore. Nowadays, users have multiple accounts, profiles and personal data distributed throughout the web and hosted by different providers. However, the online world is currently divided into identity silos forcing users to deal with repetitive authentication and registration processes and hindering a faster development of large scale e-business. Federation has been proposed as a technology to bridge different trust domains, allowing user identity information to be shared in order to improve usability. But further research is required to shift from the current static model, where manual bilateral agreements must be pre-configured to enable cooperation between unknown parties, to a more dynamic one, where trust relationships are established on demand in a fully automated fashion. This paper presents IdMRep, the first completely decentralized reputation-based mechanism which makes dynamic federation a reality. Initial experiments demonstrate its accuracy as well as an assumable overhead in scenarios with and without malicious nodes.

As multi-tenant authorization and federated identity management systems for cloud computing matures, the provisioning of services using this paradigm allows maximum efficiency on business that requires access control. However, regarding scalability support, mainly horizontal, some characteristics of those approaches based on central authentication protocols are problematic. The objective of this work is to address these issues by providing an adapted sticky-session mechanism for a Shibboleth architecture using CAS. This alternative, compared with the recommended shared memory approach, shown improved efficiency and less overall infrastructure complexity.

Although current Internet operations generate voluminous data, they remain largely oblivious of traffic data semantics. This poses many inefficiencies and challenges due to emergent or anomalous behavior impacting the vast array of Internet elements such as services and protocols. In this paper, we propose a Data Semantics Management System (DSMS) for learning Internet traffic data semantics to enable smarter semantics- driven networking operations. We extract networking semantics and build and utilize a dynamic ontology of network concepts to better recognize and act upon emergent or abnormal behavior. Our DSMS utilizes: (1) Latent Dirichlet Allocation algorithm (LDA) for latent features extraction and semantics reasoning; (2) big tables as a cloud-like data storage technique to maintain large-scale data; and (3) Locality Sensitive Hashing algorithm (LSH) for reducing data dimensionality. Our preliminary evaluation using real Internet traffic shows the efficacy of DSMS for learning behavior of normal and abnormal traffic data and for accurately detecting anomalies at low cost.
 

The migration from a vertical to horizontal business model has made it easier to introduce hardware Trojans and counterfeit electronic parts into the electronic component supply chain. Hardware Trojans are malicious modifications made to original IC designs that reduce system integrity (change functionality, leak private data, etc.). Counterfeit parts are often below specification and/or of substandard quality. The existence of Trojans and counterfeit parts creates risks for the life-critical systems and infrastructures that incorporate them including automotive, aerospace, military, and medical systems. In this tutorial, we will cover: (i) Background and motivation for hardware Trojan and counterfeit prevention/detection; (ii) Taxonomies related to both topics; (iii) Existing solutions; (iv) Open challenges; (v) New and unified solutions to address these challenges.