George Stergiopoulos

Insider Threat: Enhancing BPM through Social Media

Modern business environments have a constant need to increase their productivity, reduce costs and offer competitive products and services. This can be achieved via modeling their business processes. Yet, even in light of modellings widespread success, one can argue that it lacks built-in security mechanisms able to detect and fight threats that may manifest throughout the process. Academic research has proposed a variety of different solutions which focus on different kinds of threat. In this paper we focus on insider threat, i.e. insiders participating in an organizations business process, who, depending on their motives, may cause severe harm to the organization. We examine existing security approaches to tackle down the aforementioned threat in enterprise business processes. We discuss their pros and cons and propose a monitoring approach that aims at mitigating the insider threat. This approach enhances business process monitoring tools with information evaluated from Social Media. It exams the online behavior of users and pinpoints potential insiders with critical roles in the organizations processes. We conclude with some observations on the monitoring results (i.e. psychometric evaluations from the social media analysis) concerning privacy violations and argue that deployment of such systems should be only allowed on exceptional cases, such as protecting critical infrastructures.

Risk mitigation strategies for critical infrastructures based on graph centrality analysis

Dependency risk graphs have been proposed as a tool for analyzing cascading failures due to critical infrastructure dependency chains. However, dependency chain analysis is not by itself adequate to develop an efficient risk mitigation strategy - one that specifies which critical infrastructures should have high priority for applying mitigation controls in order to achieve an optimal reduction in the overall risk. This paper extends previous dependency risk analysis research to implement efficient risk mitigation. This is accomplished by exploring the relation between dependency risk paths and graph centrality characteristics. Graph centrality metrics are applied to design and evaluate the effectiveness of alternative risk mitigation strategies. The experimental evaluations are based on random graphs that simulate common critical infrastructure dependency characteristics as identified by recent empirical studies. The experimental results are used to specify an algorithm that prioritizes critical infrastructure nodes for applying controls in order to achieve efficient risk mitigation.

Hunting application-level logical errors

Business applications are complex artefacts implementing custom business logic. While much research effort has been put in the identification of technical vulnerabilities (such as buffer overflows and SQL injections), application-level logic vulnerabilities have drawn relatively limited attention, thus putting the applications mission at risk. In this paper, we design, implement, and evaluate a novel heuristic application-independent framework, which combines static and dynamic analysis, input vector, and information extraction analysis, along with a fuzzy logic system, so as to detect and assert the criticality of application-level logic vulnerabilities in Java stand-alone GUI applications.

Automated detection of logical errors in programs

Static and dynamic program analysis tools mostly focus on the detection of a priori defined defect patterns and security vulnerabilities. Automated detection of logical errors, due to a faulty implementation of applications’ functionality is a relatively uncharted territory. Automation can be based on profiling the intended behavior behind the source code. In this paper, we present a new code profiling method that combines the crosschecking of dynamic program invariants with symbolic execution, an information flow analysis, and the use of fuzzy logic. Our goal is to detect logical errors and exploitable vulnerabilities. The theoretical underpinnings and the practical implementation of our approach are discussed. We test the APP_LogGIC tool that implements the proposed analysis on two real-world applications. The results show that profiling the intended program behavior is feasible in diverse applications. We discuss the heuristics used to overcome the problem of state space explosion and of the large data sets. Code metrics and test results are provided to demonstrate the effectiveness of the approach.

On Business Logic Vulnerabilities Hunting: The APP_LogGIC Framework

While considerable research effort has been put in the identification of technical vulnerabilities, such as buffer overflows or SQL injections, business logic vulnerabilities have drawn limited attention. Logic vulnerabilities are an important class of defects that are the result of faulty application logic. Business logic refers to requirements implemented in algorithms that reflect the intended functionality of an application, e.g. in an online shop application, a logic rule could be that each cart must register only one discount coupon per product. In our paper, we extend a novel heuristic and automated method for the detection of logic vulnerabilitieswhich we presented in a previous publication. This method detects logic vulnerabilities and asserts their criticality in Java GUI applications using dynamic analysis and static together with a fuzzy logic system in order to compare and rank its findings, in an effort to minimize false positives and negatives. An extensive analysis of the code ranking system is given along with empirical results in order to demonstrate its potential.

Using Centrality Measures in Dependency Risk Graphs for Efficient Risk Mitigation

One way to model cascading critical infrastructure failures is through dependency risk graphs. These graphs help assess the expected risk of critical infrastructure dependency chains. This research extends an existing dependency risk analysis methodology towards risk management. The relationship between dependency risk paths and graph centrality measures is explored in order to identify nodes that significantly impact the overall dependency risk. Experimental results using random graphs to simulate common critical infrastructure dependency characteristics are presented. Based on the experimental findings, an algorithm is proposed for efficient risk mitigation. The algorithm can be used to define priorities in selecting nodes for the application of mitigation controls.

Classification and Comparison of Critical Infrastructure Protection Tools

Modeling and analysis of critical infrastructure interdependencies is a research area that has attracted considerable interest. Interdependency and risk analyses can be computationally intensive, but can also yield useful results that enhance risk assessments and offer risk mitigation alternatives. Unfortunately, many tools and methodologies are left unsupported and are forgotten soon after the projects that developed them terminate.

Using Logical Error Detection in Software Controlling Remote-Terminal Units to Predict Critical Information Infrastructures Failures

A method for predicting software failures to critical information infrastructures is presented in this paper. Software failures in critical infrastructures can stem from logical errors in the source code which manipulates controllers that handle machinery; i.e. Remote Terminal Units and Programmable Logic Controllers in SCADA systems. Since these controllers are often responsible for handling hardware in critical infrastructures, detecting such logical errors in the software controlling their functionality implies detecting possible failures in the machine itself and, consequently, predicting single or cascading infrastructure failures. Our method may also be tweaked to provide estimates of the impact and likelihood of each detected error. An existing source code analysis method is adjusted to analyze code able to send commands to SCADA systems. A practical implementation of the method is presented and discussed. Examples are given using open-source SCADA operating interfaces.

“Water, Water, Every Where”: Nuances for a Water Industry Critical Infrastructure Specification Exemplar

The water infrastructure is critical to human life, but little attention has been paid to the nuances of the water industry. Without such attention, evaluating security innovation in this domain without compromising the productivity goals when delivering water services is difficult. This paper proposes four nuances that need to be incorporated into a representative specification exemplar for the water industry; these provided input to the exemplar based on a fictional water company.

Interdependency analysis of junctions for congestion mitigation in Transportation Infrastructures

The resilience of the Transportation road infrastructure network is of major importance, since failures such as prolonged road congestion in specific parts of the infrastructure often initiate major cascading effects that block transportation and/or disrupt services of other infrastructures over wide areas. Existing traffic flow analysis methods lack the ability to understand cascading effect of congestions and how to improve overall resilience in greater areas. Dependency risk graphs have been proposed as a tool for analyzing such cascading failures using infrastructure dependency chains. In this paper, we propose a risk-based interdependency analysis methodology capable to detect large-scale traffic congestions between interconnected junctions of the road network and provide mitigation solutions to increase traffic flow resilience. Dependency risk chains of junctions provide important information about which junctions are affected when other major junctions are congested in the road transportation network. Targeted mitigation mechanisms for traffic congestion can be proposed and the causes of bottlenecks can be analyzed to introduce road constructions or reparations with the best possible results in relieving traffic. We applied the proposed methodology on data collected by the UK government using cyber-physical traffic sensors over the course of 6 years. Our tool analyzed the UK major/A road transportation network, detected n-order junction dependencies and automatically proposed specific mitigation solutions to increase the overall resilience of the road infrastructure network. Simulation results indicate that detected mitigation options, if applied, can increase overall congestion resilience in wider areas of the network up to 12% by lowering likelihood of congestion.