Model-Based Security Testing

Security testing aims at validating software system requirements related to security properties like confidentiality, integrity, authentication, authorization, availability, and non-repudiation. Although security testing techniques are available for many years, there has been little approaches that allow for specification of test cases at a higher level of abstraction, for enabling guidance on test identification and specification as well as for automated test generation. Model-based security testing (MBST) is a relatively new field and especially dedicated to the systematic and efficient specification and documentation of security test objectives, security test cases and test suites, as well as to their automated or semi-automated generation. In particular, the combination of security modelling and test generation approaches is still a challenge in research and of high interest for industrial applications. MBST includes e.g. security functional testing, model-based fuzzing, risk- and threat-oriented testing, and the usage of security test patterns. This paper provides a survey on MBST techniques and the related models as well as samples of new methods and tools that are under development in the European ITEA2-project DIAMONDS.Comment: In Proceedings MBT 2012, arXiv:1202.582

The Potential Savings to Social Security from Means Testing

Means testing, or reducing Social Security payments to affluent beneficiaries, has been touted as an effective way to reduce the cost of the program. This report examines the feasibility of several different means testing scenarios and finds the potential savings to be rather limited.The report, "The Potential Savings to Social Security from Means Testing," first describes the distribution of Social Security benefits by income level. The authors then look at the effects of phasing out benefits at rates of 10 and 20 percent of every dollar of non-Social Security income above $40,000 or$100,000 and find little in the way of potential savings to Social Security. The savings are even less when behavioral responses in the form of tax avoidance or tax evasion are factored in, since a means test would effectively be an increase in the marginal tax rate for wealthier seniors.The data show that over 75 percent of social security benefits go to individuals with non-Social Security income of less than $20,000 and 90 percent goes to those with non-Social Security income of less than$40,000 a year as of 2009. If means testing that phased out benefits at 10 percent were applied to those who make $100,000 a year and assuming no change in behavior, it would only save Social Security 0.74 percent of its outlays. At a 20 percent rate, this would only yield savings equal to1.33 percent of costs. If the phase out were dropped down to$40,000, hardly wealthy by any standard, the overall savings would just be 2.77 percent of costs at the 10 percent rate and only 4.65 percent of costs at the 20 percent rate. Accounting for behavioral responses would lead to even smaller savings, could cut these potential savings by half or more.Mean testing would also raise the cost of the program. The retirement program currently has very low costs. If the administrative expenses rose to the level of the disability portion of the Social Security program, the higher costs would likely exceed any savings from a means test.On net, a means test would appear to be a dubious way to reduce the cost of Social Security

Consequences of means testing Social Security: evidence from the SSI program

A treatise that draws inferences about the potential behavorial responses to means testing Social Security by examining the effects of the Supplementary Security Income program for the aged on wealth accumulation and employment.Labor supply ; Social security

Security Testing: A Survey

Identifying vulnerabilities and ensuring security functionality by security testing is a widely applied measure to evaluate and improve the security of software. Due to the openness of modern software-based systems, applying appropriate security testing techniques is of growing importance and essential to perform effective and efficient security testing. Therefore, an overview of actual security testing techniques is of high value both for researchers to evaluate and refine the techniques and for practitioners to apply and disseminate them. This chapter fulfills this need and provides an overview of recent security testing techniques. For this purpose, it first summarize the required background of testing and security engineering. Then, basics and recent developments of security testing techniques applied during the secure software development lifecycle, i.e., model-based security testing, code-based testing and static analysis, penetration testing and dynamic analysis, as well as security regression testing are discussed. Finally, the security testing techniques are illustrated by adopting them for an example three-tiered web-based business application

Smart Computer Security Audit: Reinforcement Learning with a Deep Neural Network Approximator

A significant challenge in modern computer security is the growing skill gap as intruder capabilities increase, making it necessary to begin automating elements of penetration testing so analysts can contend with the growing number of cyber threats. In this paper, we attempt to assist human analysts by automating a single host penetration attack. To do so, a smart agent performs different attack sequences to find vulnerabilities in a target system. As it does so, it accumulates knowledge, learns new attack sequences and improves its own internal penetration testing logic. As a result, this agent (AgentPen for simplicity) is able to successfully penetrate hosts it has never interacted with before. A computer security administrator using this tool would receive a comprehensive, automated sequence of actions leading to a security breach, highlighting potential vulnerabilities, and reducing the amount of menial tasks a typical penetration tester would need to execute. To achieve autonomy, we apply an unsupervised machine learning algorithm, Q-learning, with an approximator that incorporates a deep neural network architecture. The security audit itself is modelled as a Markov Decision Process in order to test a number of decisionmaking strategies and compare their convergence to optimality. A series of experimental results is presented to show how this approach can be effectively used to automate penetration testing using a scalable, i.e. not exhaustive, and adaptive approach