A survey of simulation techniques in commerce and defence

Despite the developments in Modelling and Simulation (M&S) tools and techniques over the past years, there has been a gap in the M&S research and practice in healthcare on developing a toolkit to assist the modellers and simulation practitioners with selecting an appropriate set of techniques. This study is a preliminary step towards this goal. This paper presents some results from a systematic literature survey on applications of M&S in the commerce and defence domains that could inspire some improvements in the healthcare. Interim results show that in the commercial sector Discrete-Event Simulation (DES) has been the most widely used technique with System Dynamics (SD) in second place. However in the defence sector, SD has gained relatively more attention. SD has been found quite useful for qualitative and soft factors analysis. From both the surveys it becomes clear that there is a growing trend towards using hybrid M&S approaches

Improvement of DHRA-DMDC Physical Access Software DBIDS Using Cloud Computing Technology: a Case Study

The U.S government has created and been executing an Identity and Management (IdM) vision to support a global, robust, trusted and interoperable identity management capability that provides the ability to correctly identify individuals and non-person entities in support of DoD mission operations. Many Directives and Instructions have been issued to standardize the process to design, re-designed new and old systems with latest available technologies to meet the visions requirements. In this thesis we introduce a cloud-based architecture for the Defense Biometric Identification System (DBIDS), along with a set of DBIDS Cloud Services that supports the proposed architecture. This cloud-based architecture will move DBIDS in the right direction to meet Dod IdM visions and goals by decoupling current DBIDS functions into DBIDS core services to create interoperability and flexibility to expand future DBIDS with new requirements. The thesis will show its readers how DBIDS Cloud Services will help Defense Manpower Data Center (DMDC) easily expanding DBIDS functionalities such as connecting to other DMDC services or federated services for vetting purposes. This thesis will also serve as a recommendation of a blue-print for DBIDS architecture to support new generation of DBIDS application. This is a step closer in moving DMDC Identity Enterprise Solution toward DoD IdM realizing vision and goals. The thesis also includes a discussion of how to utilize virtualized DBIDS workstations to address software-deployment and maintenance issues to resolve configuration and deployment issues which have been costly problems for DMDC over the years.http://archive.org/details/improvementofdhr109457379Civilian, Department of Defens

SQL Injection Detection Using Machine Learning Techniques and Multiple Data Sources

SQL Injection continues to be one of the most damaging security exploits in terms of personal information exposure as well as monetary loss. Injection attacks are the number one vulnerability in the most recent OWASP Top 10 report, and the number of these attacks continues to increase. Traditional defense strategies often involve static, signature-based IDS (Intrusion Detection System) rules which are mostly effective only against previously observed attacks but not unknown, or zero-day, attacks. Much current research involves the use of machine learning techniques, which are able to detect unknown attacks, but depending on the algorithm can be costly in terms of performance. In addition, most current intrusion detection strategies involve collection of traffic coming into the web application either from a network device or from the web application host, while other strategies collect data from the database server logs. In this project, we are collecting traffic from two points: the web application host, and a Datiphy appliance node located between the webapp host and the associated MySQL database server. In our analysis of these two datasets, and another dataset that is correlated between the two, we have been able to demonstrate that accuracy obtained with the correlated dataset using algorithms such as rule-based and decision tree are nearly the same as those with a neural network algorithm, but with greatly improved performance

Automated security analysis in a SCADA system

Supervisory control and data acquisition (SCADA) is a computer system for analysing, and monitoring data, as well as, controlling a plant in industries such as power grids, oil, gas refining, and water control. SCADA belongs to the category of critical systems that are needed to maintain the infrastructure of cities and households. Therefore, the security aspect of such a system has a significant role. The early SCADA systems were designed with the operation as the primary concern rather than security since they were a monolithic networked system without external access. However, the systems evolved, and SCADA systems were embedded with web technologies for users to monitor the data externally. These changes improved the efficiency of monitoring and productivity; however, this caused a problem of potential cyber-attacks to a SCADA system. One such example was Ukraine's power grid blackout in 2015. Therefore, it is beneficial for the security of a SCADA system to create a threat modeling technique that can understand the critical components of SCADA, discover potential threats, and propose possible mitigation strategies. One issue when creating a threat model is the significant difference of SCADA from traditional Operational Technology (OT) systems. Another significant issue is that SCADA is a highly customisable system, and each SCADA instance can have different components. Therefore, for this work, we implemented a threat modeling language scadaLang, which is specific to the domain of a SCADA system. We started by defining the major assets of a SCADA system, attackers, entry surfaces, and built attacks and defense strategies. Then we developed a threat modeling domain-specific language scadaLang that can create a threat model for a particular instance of SCADA taking the differences in components and connections into account. As a result, we achieved a threat modeling language for SCADA, ensured the reliability of the results by peer-reviewing of an engineer familiar with the domain of the problem, and proposed a Turing test to ensure the validity of the result of scadaLang as the future development of the project

Cyber indicators of compromise: a domain ontology for security information and event management

It has been said that cyber attackers are attacking at wire speed (very fast), while cyber defenders are defending at human speed (very slow). Researchers have been working to improve this asymmetry by automating a greater portion of what has traditionally been very labor-intensive work. This work is involved in both the monitoring of live system events (to detect attacks), and the review of historical system events (to investigate attacks). One technology that is helping to automate this work is Security Information and Event Management (SIEM). In short, SIEM technology works by aggregating log information, and then sifting through this information looking for event correlations that are highly indicative of attack activity. For example: Administrator successful local logon and (concurrently) Administrator successful remote logon. Such correlations are sometimes referred to as indicators of compromise (IOCs). Though IOCs for network-based data (i.e., packet headers and payload) are fairly mature (e.g., Snort's large rule-base), the field of end-device IOCs is still evolving and lacks any well-defined go-to standard accepted by all. This report addresses ontological issues pertaining to end-device IOCs development, including what they are, how they are defined, and what dominant early standards already exist.http://archive.org/details/cyberindicatorso1094553041Lieutenant, United States NavyApproved for public release; distribution is unlimited

Building an Emulation Environment for Cyber Security Analyses of Complex Networked Systems

Computer networks are undergoing a phenomenal growth, driven by the rapidly increasing number of nodes constituting the networks. At the same time, the number of security threats on Internet and intranet networks is constantly growing, and the testing and experimentation of cyber defense solutions requires the availability of separate, test environments that best emulate the complexity of a real system. Such environments support the deployment and monitoring of complex mission-driven network scenarios, thus enabling the study of cyber defense strategies under real and controllable traffic and attack scenarios. In this paper, we propose a methodology that makes use of a combination of techniques of network and security assessment, and the use of cloud technologies to build an emulation environment with adjustable degree of affinity with respect to actual reference networks or planned systems. As a byproduct, starting from a specific study case, we collected a dataset consisting of complete network traces comprising benign and malicious traffic, which is feature-rich and publicly available