Human resource development professionals' perceptions about the Academy of Human Resource Development Standards on Ethics and Integrity

Professional standards of ethics proclaim the core values of a profession, describe expected professional duties and responsibilities, and provide a framework for ethical practice and ethical decision-making. The purpose of this mixed, quantitative and qualitative, survey study was to examine HRD professionals' perceptions about the AHRD Standards on Ethics and Integrity, how HRD professionals used the Standards for research and decision-making, and the extent to which the Standards provided guidance for ethical decision-making. Through an on-line survey instrument, 182 members of AHRD were surveyed. The open-ended questions were analyzed using thematic analysis to expand on, inform, and support the quantitative findings. The close-ended questions were analyzed with frequency distributions, descriptive statistics, cross tabulations, and Spearman rank correlations. The results showed a significant relationship between (a) years of AHRD membership and level of familiarity with the Standards, (b) years of AHRD membership and use of the Standards for research, and (c) level of familiarity with the Standards and use of the Standards for research. There were no significant differences among scholars, scholar practitioners, practitioners, and students regarding their perceptions about the Standards. The results showed that the Standards were not well known or widely used. Nevertheless, the results indicated overall positive perceptions about the Standards. Seventy percent agreed that the Standards provided an appropriate set of ethical principles and reflected respondents' own standards of conduct. Seventy-eight percent believed that the Standards were important for defining HRD as a profession and 54% believed they were important for developing a sense of belonging to the HRD profession. Fifty-one percent believed the Standards should be enforceable and 61% agreed members should sign the membership application form showing willingness to adhere to the Standards. Seventy-seven percent based work-related ethical decisions on personal beliefs of right and wrong and 56% on established professional values and rules of right and wrong. The findings imply that if the professional standards of ethics are to influence the profession, they should be widely publicized and discussed among members, they should have some binding power, and their use should be encouraged.

An Examination of Qualitative Empirical Studies at the AHRD from 1999-2003: Method, Rationale for Method, Data Collection, Sampling Strategies, and Integrity Measures

The paper examines the nature of qualitative empirical studies published in the AHRD proceedings from 1999-2003 and discusses findings on method, rationale for method, data collection, sampling strategies, and integrity measures.

Integrity-based kernel malware detection

Kernel-level malware is one of the most dangerous threats to the security of users on the Internet, so there is an urgent need for its detection. The most popular detection approach is misuse-based detection. However, it cannot catch up with today's advanced malware that increasingly apply polymorphism and obfuscation. In this thesis, we present our integrity-based detection for kernel-level malware, which does not rely on the specific features of malware. ^ We have developed an integrity analysis system that can derive and monitor integrity properties for commodity operating systems kernels. In our system, we focus on two classes of integrity properties: data invariants and integrity of Kernel Queue (KQ) requests. ^ We adopt static analysis for data invariant detection and overcome several technical challenges: field-sensitivity, array-sensitivity, and pointer analysis. We identify data invariants that are critical to system runtime integrity from Linux kernel 2.4.32 and Windows Research Kernel (WRK) with very low false positive rate and very low false negative rate. We then develop an Invariant Monitor to guard these data invariants against real-world malware. In our experiment, we are able to use Invariant Monitor to detect ten real-world Linux rootkits and nine real-world Windows malware and one synthetic Windows malware. ^ We leverage static and dynamic analysis of kernel and device drivers to learn the legitimate KQ requests. Based on the learned KQ requests, we build KQguard to protect KQs. At runtime, KQguard rejects all the unknown KQ requests that cannot be validated. We apply KQguard on WRK and Linux kernel, and extensive experimental evaluation shows that KQguard is efficient (up to 5.6% overhead) and effective (capable of achieving zero false positives against representative benign workloads after appropriate training and very low false negatives against 125 real-world malware and nine synthetic attacks). ^ In our system, Invariant Monitor and KQguard cooperate together to protect data invariants and KQs in the target kernel. By monitoring these integrity properties, we can detect malware by its violation of these integrity properties during execution.^

Integrity-Based Kernel Malware Detection

Kernel-level malware is one of the most dangerous threats to the security of users on the Internet, so there is an urgent need for its detection. The most popular detection approach is misuse-based detection. However, it cannot catch up with today's advanced malware that increasingly apply polymorphism and obfuscation. In this thesis, we present our integrity-based detection for kernel-level malware, which does not rely on the specific features of malware. We have developed an integrity analysis system that can derive and monitor integrity properties for commodity operating systems kernels. In our system, we focus on two classes of integrity properties: data invariants and integrity of Kernel Queue (KQ) requests. We adopt static analysis for data invariant detection and overcome several technical challenges: field-sensitivity, array-sensitivity, and pointer analysis. We identify data invariants that are critical to system runtime integrity from Linux kernel 2.4.32 and Windows Research Kernel (WRK) with very low false positive rate and very low false negative rate. We then develop an Invariant Monitor to guard these data invariants against real-world malware. In our experiment, we are able to use Invariant Monitor to detect ten real-world Linux rootkits and nine real-world Windows malware and one synthetic Windows malware. We leverage static and dynamic analysis of kernel and device drivers to learn the legitimate KQ requests. Based on the learned KQ requests, we build KQguard to protect KQs. At runtime, KQguard rejects all the unknown KQ requests that cannot be validated. We apply KQguard on WRK and Linux kernel, and extensive experimental evaluation shows that KQguard is efficient (up to 5.6% overhead) and effective (capable of achieving zero false positives against representative benign workloads after appropriate training and very low false negatives against 125 real-world malware and nine synthetic attacks). In our system, Invariant Monitor and KQguard cooperate together to protect data invariants and KQs in the target kernel. By monitoring these integrity properties, we can detect malware by its violation of these integrity properties during execution.