Parallel Methods for Evidence and Trust based Selection and Recommendation of Software Apps from Online Marketplaces

With the popularity of various online software marketplaces, third-party vendors are creating many instances of software applications ('apps') for mobile and desktop devices targeting the same set of requirements. This abundance makes the task of selecting and recommending (S&R) apps, with a high degree of assurance, for a specific scenario a significant challenge. The S&R process is a precursor for composing any trusted system made out of such individually selected apps. In addition to feature-based information, about these apps, these marketplaces contain large volumes of user reviews. These reviews contain unstructured user sentiments about app features and the onus of using these reviews in the S&R process is put on the user. This approach is ad-hoc, laborious and typically leads to a superficial incorporation of the reviews in the S&R process by the users. However, due to the large volumes of such reviews and associated computing, these two techniques are not able to provide expected results in real-time or near real-time. Therefore, in this paper, we present two parallel versions (i.e., batch processing and stream processing) of these algorithms and empirically validate their performance using publically available datasets from the Amazon and Android marketplaces. The results of our study show that these parallel versions achieve near real-time performance, when measured as the end-to-end response time, while selecting and recommending apps for specific queries

Developing a Deterministic Polymorphic Circuit Generator Using Random Boolean Logic Expansion

Securing applications on untrusted platforms can involve protection against legitimate endusers who act in the role of malicious reverse engineers and hackers. Such adversaries have access to the full execution environment of programs, whether the program comes in the form of software or hardware. In this thesis, we consider the nature of obfuscating algorithms that perform iterative, stepwise transformation of programs into more complex forms that are intended to increase the complexity (time, resources) for malicious reverse engineers. We consider simple Boolean logic programs as the domain of interest and examine a specific transformation technique known as Iterative Selection and Replacement (ISR), which represents a practical, syntactic approach for obfuscation. Specifically, we focus on improving the security of ISR by maximizing the flexibility and potential security of the replacement step of the algorithm which can be formulated in the following question: Given a selection of Boolean logic gates (i.e., a subcircuit), how can we produce a semantically equivalent (polymorphic) version of the subcircuit such that the distribution of potential replacements represents a random, uniform distribution from the set of all possible replacements? This practical question is related to the theoretic study of indistinguishability obfuscation, where a transformer for a class of circuits guarantees that given any two semantically equivalent circuits from the class, the distribution of variants from their obfuscation are computationally indistinguishable. Ideally, polymorphic circuits that follow a random, uniform distribution provide stronger protection against malicious analyzers that target identification of distinct patterns as a basis for deobfuscation and simplification. We introduce a novel approach for polymorphic circuit replacement called Random Boolean Logic Expansion (RBLE), which applies Boolean logic laws (of reduction) in reverse. We compare this approach against another proposed method of polymorphic replacement that relies on static circuit libraries. As a contribution, we show the strengths and weaknesses of each approach, examine initial results from empirical studies to estimate the uniformity of polymorphic distributions, and provide the argument for how such algorithms can be readily applied in software contexts. RBLE provides a unique method to generate polymorphic variants of arbitrary input, output, and gate size. We report initial findings for studying variants produced by this method and, from empirical evaluation, show that RBLE has promise for generating distributions of unique, uniform circuits when size is unconstrained, but for targeted size distributions, the approach requires adjustment for reaching potential circuit variant

Using Genetic Algorithm to Minimize False Alarms in Insider Threats Detection of Information Misuse in Windows Environment

Insider threats detection problem has always been one of the most difficult challenges for organizations and research community. Effective behavioral categorization of users plays a vital role for the success of any detection mechanisms. It also helps to reduce false alarms in case of insider threats. In order to achieve this, a fuzzy classifier has been implemented along with genetic algorithm (GA) to enhance the efficiency of a fuzzy classifier. It also enhances the functionality of all other modules to achieve better results in terms of false alarms. A scenario driven approach along with mathematical evaluation verifies the effectiveness of the modified framework. It has been tested for the enterprises having critical nature of business. Other organizations can adopt it in accordance with their specific nature of business, need, and operational processes. The results prove that accurate classification and detection of users were achieved by adopting the modified framework which in turn minimizes false alarms

Cyber security of the smart grid: Attack exposure analysis, detection algorithms, and testbed evaluation

While smart grid technologies are deployed to help achieve improved grid resiliency and efficiency, they also present an increased dependency on cyber resources which may be vulnerable to attack. This dissertation introduces three components that provide new methods to enhancing the cyber security of the smart grid. First, a quantitative exposure analysis model is presented to assess risks inherited from the communication and computation of critical information. An attack exposure metric is then presented to provide a quantitative means to analyze the model. The metric\u27s utility is then demonstrated by analyzing smart grid environments to contrast the effectiveness of various protection mechanisms and to evaluate the impact of new cyber vulnerabilities. Second, a model-based intrusion detection system is introduced to identify attacks against electric grid substations. The system expands previous research to incorporate temporal and spatial analysis of substation control events in order to differentiate attacks from normal communications. This method also incorporates a hierarchical detection approach to improve correlation of physical system events and identify sophisticated coordinated attacks. Finally, the PowerCyber testbed is introduced as an accurate cyber-physical envi- ronment to help facilitate future smart grid cyber security research needs. The testbed implements a layered approach of control, communication, and power system layers while incorporating both industry standard components along with simulation and emulation techniques. The testbed\u27s efficacy is then evaluated by performing various cyber attacks and exploring their impact on physical grid simulations

Control priorization model for improving information security risk assessment

Evaluating particular assets for information security risk assessment should take into consideration the availability of adequate resources and return on investments (ROI). Despite the need for a good risk assessment framework, many of the existing frameworks lack of granularity guidelines and mostly depend on qualitative methods. Hence, they require additional time and cost to test all the information security controls. Further, the reliance on human inputs and feedback will increase subjective judgment in organizations. The main goal of this research is to design an efficient Information Security Control Prioritization (ISCP) model in improving the risk assessment process. Case studies based on penetration tests and vulnerability assessments were performed to gather data. Then, Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) was used to prioritize them. A combination of sensitivity analysis and expert interviews were used to test and validate the model. Subsequently, the performance of the model was evaluated by the risk assessment experts. The results demonstrate that ISCP model improved the quality of information security control assessment in the organization. The model plays a significant role in prioritizing the critical security technical controls during the risk assessment process. Furthermore, the model’s output supports ROI by identifying the appropriate controls to mitigate risks to an acceptable level in the organizations. The major contribution of this research is the development of a model which minimizes the uncertainty, cost and time of the information security control assessment. Thus, the clear practical guidelines will help organizations to prioritize important controls reliably and more efficiently. All these contributions will minimize resource utilization and maximize the organization’s information security

Towards cyber-resilient telecontrol commands using software-defined networking

Cybersecurity enhancement of power systems has become one of the main objectives of utility managers and regulatory agencies because of the increasing number of cyberattacks against critical infrastructures. In this paper, we investigate the application of software-defined networking for improving the cyber-resilience of power systems in the presence of cyberattacks using false telecontrol commands. It is first demonstrated that cyberattackers can use false telecontrol commands to separate a power plant from a power grid or trip a major transmission line. Next, it is shown that software-defined networking can significantly enhance the cyber-resilience of power systems in the presence of cyberattacks using false telecontrol commands compared to legacy communication networks. This is because the source, destination and protocol of telecontrol commands can be examined and verified in software-defined networking before communication packet forwarding actions take place. Moreover, primary and back-up routes of telecontrol commands can be pre-engineered in software-defined networking to counteract potential cyberattacks

Moving target defense for securing smart grid communications: Architectural design, implementation and evaluation

Supervisory Control And Data Acquisition (SCADA) communications are often subjected to various kinds of sophisticated cyber-attacks which can have a serious impact on the Critical Infrastructure such as the power grid. Most of the time, the success of the attack is based on the static characteristics of the system, thereby enabling an easier profiling of the target system(s) by the adversary and consequently exploiting their limited resources. In this thesis, a novel approach to mitigate such static vulnerabilities is proposed by implementing a Moving Target Defense (MTD) strategy in a power grid SCADA environment, which leverages the existing communication network with an end-to-end IP Hopping technique among the trusted peer devices. This offers a proactive L3 layer network defense, minimizing IP-specific threats and thwarting worm propagation, APTs, etc., which utilize the cyber kill chain for attacking the system through the SCADA network. The main contribution of this thesis is to show how MTD concepts provide proactive defense against targeted cyber-attacks, and a dynamic attack surface to adversaries without compromising the availability of a SCADA system. Specifically, the thesis presents a brief overview of the different type of MTD designs, the proposed MTD architecture and its implementation with IP hopping technique over a Control Center–Substation network link along with a 3-way handshake protocol for synchronization on the Iowa State’s Power Cyber testbed. The thesis further investigates the delay and throughput characteristics of the entire system with and without the MTD to choose the best hopping rate for the given link. It also includes additional contributions for making the testbed scenarios more realistic to real world scenarios with multi-hop, multi-path WAN. Using that and studying a specific attack model, the thesis analyses the best ranges of IP address for different hopping rate and different number of interfaces. Finally, the thesis describes two case studies to explore and identify potential weaknesses of the proposed mechanism, and also experimentally validate the proposed mitigation alterations to resolve the discovered vulnerabilities. As part of future work, we plan to extend this work by optimizing the MTD algorithm to be more resilient by incorporating other techniques like network port mutation to further increase the attack complexity and cost

A Comparative Study of Authentication Methods in Mobile Cloud Computing

Abstrac

Air Force Institute of Technology Research Report 2013

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems Engineering and Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

A Design Approach to IoT Endpoint Security for Production Machinery Monitoring

The Internet of Things (IoT) has significant potential in upgrading legacy production machinery with monitoring capabilities to unlock new capabilities and bring economic benefits. However, the introduction of IoT at the shop floor layer exposes it to additional security risks with potentially significant adverse operational impact. This article addresses such fundamental new risks at their root by introducing a novel endpoint security-by-design approach. The approach is implemented on a widely applicable production-machinery-monitoring application by introducing real-time adaptation features for IoT device security through subsystem isolation and a dedicated lightweight authentication protocol. This paper establishes a novel viewpoint for the understanding of IoT endpoint security risks and relevant mitigation strategies and opens a new space of risk-averse designs that enable IoT benefits, while shielding operational integrity in industrial environments