Cybersecurity is critical to the national infrastructure, federal and local government, military, industry, and personal privacy. To defend the U.S. against the cyber threats, a significant demand for skilled cybersecurity workforce is predicted in government and industrial sectors. To address this issue, National Security Agency and the National Science Foundation jointly funded GenCyber program to stimulate the K-12 students' interest in the cybersecurity field and raise their awareness of cybersecurity and safe online behavior. Purdue University Northwest has successfully launched four GenCyber summer camps in 2016 and 2017 to 181 high school students, with 51.3% underrepresented minority ratio (Africa American and Hispanics), and about 2:1 male to female ratio. We delivered GenCyber summer camp activities in the format of game based learning and hands-on labs. The use of game-based learning in the camp was an excellent platform to teach concepts of cyber security principles. For example, in Cyber Defense Tower Game, students need to protect their servers from the different types of cyber-attack. They need to select the correct type of defense to stop each wave of cyber-attack. As the students advanced through the game, combinations of the different attacks would come faster, making it more difficult for the students to defend their servers. This game was well received by the students, support staffs, instructors, and site visit team. Learning through these activities provided high school students with an immersive, learner-centered experience, which has been proven very effective on cybersecurity awareness training and practical skill acquisition for learners from diverse backgrounds. Further analysis of survey data revealed that the gamification of cybersecurity education to raise students' interests in computer science and cybersecurity was more effective in male high school students than in female students.

Current DTN systems are vulnerable to malicious attacks by compromised nodes taking advantage of the DTN protocols that control how nodes join and leave the backbone. Without addressing such security issues, DTN systems could result in performance degrade, the compromise of message confidentiality and integrity, and denial of service. Thus, effective mechanisms to identify and fix this vulnerability are urgently needed. The objective of this research is to address these issues by identifying DTN system vulnerabilities and potential threats, and providing potential countermeasures. We first illustrate vulnerabilities of the epidemic based contact history propagation protocol and then identify the potential threats to system performance, availability, and integrity as well as countermeasures to prevent such threats existing in DTN networks. A secure contact protocol is developed for DTN network with epidemic based contact history propagation protocol and simulations are conducted to assess its effectiveness on attack detection.

Modern computer software usually consists of a number of interacting components. Defects pertaining to component interactions may not be visible until the components are integrated. To exercise component interactions, we propose an integration testing approach based on the core concept of contracts (i.e., preconditions and postconditions of component calls in first-order logic). To enable correctness analysis and generation of coverage-based tests (including state coverage, transition or component coverage, and reachability coverage) of a contract-based test model, we transform the test model into an operational model. We have implemented this approach into a framework that has the capacity to generate executable test code in a large collection of programming languages and to run the generated test code in a rich variety of test execution environments. To demonstrate the cost-effectiveness of our approach, we have conducted a series of experiments in controlled environments to measure the performance of test generation and the fault detection capabilities of coverage-based tests. The results have clearly demonstrated that: 1) our approach can quickly generate coverage-based tests when there is enough memory for dealing with the state space of the given test models; 2) reachability coverage is more capable of detecting faults than state coverage and transition coverage; 3) postcondition-based test oracles are more capable of detecting faults than state-based test oracles; and 4) robustness tests, which exercise the situations when the contracts are broken, are important for improving fault detection capability.

Security attacks typically result from unintended behaviors or invalid inputs. Security testing is labor intensive because a real-world program usually has too many invalid inputs. It is highly desirable to automate or partially automate security-testing process. This paper presents an approach to automated generation of security tests by using formal threat models represented as Predicate/Transition nets. It generates all attack paths, i.e., security tests, from a threat model and converts them into executable test code according to the given Model-Implementation Mapping (MIM) specification. We have applied this approach to two real-world systems, Magento (a web-based shopping system being used by many online stores) and FileZilla Server (a popular FTP server implementation in C++). Threat models are built systematically by examining all potential STRIDE (spoofing identity, tampering with data, repudiation, information disclosure, denial of service, and elevation of privilege) threats to system functions. The security tests generated from these models have found multiple security risks in each system. The test code for most of the security tests can be generated and executed automatically. To further evaluate the vulnerability detection capability of the testing approach, the security tests have been applied to a number of security mutants where vulnerabilities are injected deliberately. The mutants are created according to the common vulnerabilities in C++ and web applications. Our experiments show that the security tests have killed the majority of the mutants.

Availability is an important issue in distributed systems providing quality of services. Data or service availability modeling is very important to ensure such quality of services. Many factors, such as node availability and network link availability, could affect the data or service availability in a distributed system, and replication schemes have been widely used to improve data or service availability. In this paper, we consider node and network link failures, as well as the impact of multiple replicas on data availability. Efficient availability computing algorithms are developed to model data availability for a system with different topologies. Finally, experimental studies have been conducted to see how well our modeling approach is for the availability modeling of distributed environment with data replication.

In a large-scale collaborative filtering system, pair wise similarity between users is usually measured by users' ratings on the whole set of items. However, this measurement may not be well defined due to the sparsity problem, i.e., the lack of adequate ratings on items for calculating accurate predictions. In fact, most correlated users have similar ratings only on a subset of items. In this paper, we consider a kernel-based classification approach for collaborative filtering and propose several kernel matrix construction methods by using biclusters to capture pair wise similarity between users. In order to characterize accurate correlation among users, we embed both local information and global information into the similarity matrix. However, this similarity matrix may not be a kernel matrix. Our solution is to approximate it with the matrix close to it and use low rank constraints to control the complexity of the matrix.

Secret sharing and erasure coding-based approaches have been used in distributed storage systems to ensure the confidentiality, integrity, and availability of critical information. To achieve performance goals in data accesses, these data fragmentation approaches can be combined with dynamic replication. In this paper, we consider data partitioning (both secret sharing and erasure coding) and dynamic replication in data grids, in which security and data access performance are critical issues. More specifically, we investigate the problem of optimal allocation of sensitive data objects that are partitioned by using secret sharing scheme or erasure coding scheme and/or replicated. The grid topology we consider consists of two layers. In the upper layer, multiple clusters form a network topology that can be represented by a general graph. The topology within each cluster is represented by a tree graph. We decompose the share replica allocation problem into two subproblems: the Optimal Intercluster Resident Set Problem (OIRSP) that determines which clusters need share replicas and the Optimal Intracluster Share Allocation Problem (OISAP) that determines the number of share replicas needed in a cluster and their placements. We develop two heuristic algorithms for the two subproblems. Experimental studies show that the heuristic algorithms achieve good performance in reducing communication cost and are close to optimal solutions.

The quality of large-scale recommendation systems has been insufficient in terms of the accuracy of prediction. One of the major reasons is caused by the sparsity of the samples, usually represented by vectors of userspsila ratings on a set of items. Combining information other than userspsila ratings can provide the learning model complementary views of the data and, thus, a more accurate prediction. In this paper, we propose efficient methods for finding the best combination weights among single kernels. The weight parameters are optimized by aligning the combination kernel to ideal kernels. We solve the kernel alignment problem by linear programming techniques.

It has been shown that the cost parameters and kernel parameters are critical in the performance of support vector machines (SVMs). A standard parameter selection method compares parameters among a discrete set of values, called the candidate set, and picks the one which has the best classification accuracy. As a result, the choice of parameters strongly depends on the pre-defined candidate set. In this paper, we formulate the selection of the cost parameter and kernel parameter as a two-level optimization problem, in which the values of parameters vary continuously and thus optimization techniques can be applied to select ideal parameters. Due to the non-smoothness of the objective function in our model, a genetic algorithm has been presented. Numerical results show that the two-level approach can significantly improve the performance of SVM classifier in terms of classification accuracy.

In distributed mobile systems, communication cost and disconnections are major concerns. In this paper, we address replica placement issues to achieve improved performance for systems supporting mobile transactions. We focus on handling correlated data objects and disconnections. Frequently, requests and/or transactions issued by mobile clients may access multiple data objects and should be considered together in terms of replica allocation. We discuss the replication cost model for correlated data objects and show that the problem of finding an optimal solution is NP. We further adjust the replication cost model for disconnections. A heuristic "expansion-shrinking" algorithm is developed to efficiently make replica placement decisions. The algorithm obtains near optimal solutions for the correlated data model and yields significant performance gains when disconnection is considered. Experimental studies show that the heuristic expansion-shrinking algorithm significantly outperforms the general frequency-based replication schemes.

Summary form only given. We investigate a transaction based partial replication strategy for replicating database in the mobile environments. We propose a heuristic algorithm to select a set of data objects for replicating to and deallocating from the mobile node, based on the dynamically changing client access patterns. We conduct experiments to compare the performance of our algorithm with that of the full replication and frequency based partial replication algorithms. The results show that the transaction based partial replication algorithm always has a better performance than the full replication strategy. And, in most cases, it is better than frequency-based partial replication algorithms.

Many agent-based systems have been developed over the past decade; however, the security issues are usually not addressed adequately. In order to make the mobile agent paradigm more useful and acceptable for Web and Internet applications, the security considerations should go beyond the protection of basic agent execution environments. In this paper, we present the design of a robust Java-based mobile agent system, PeAgent. The PeAgent system not only incorporates a fine-grain agent privilege control mechanism, but also includes a multi-level protection mechanism that can be used throughout the life-cycle of agents. A two-level domain hierarchy is constructed to organize the large number of Internet nodes in the agent system into manageable units: the global/domain management unit (GMU/DMU). Each unit consists of a certification authority (CA) and a location manager (LM). The certificates issued from GMU and DMU define the access privileges on the PeAgent servers for each agent. A context hierarchy is built up on the PeAgent servers for fine-level resource access control and agent management. Overall, the PeAgent system provides a secure computing environment for widely distributed applications over a network of heterogeneous platforms. It also facilitates the development of secure Web-based applications.