National Research Development Corporation

The network security assessment is vital for improving the overall security posture. With diverse opportunities for using networking devices and configuring them, varying software application portfolios, and the increased flexibility of using numerous applications, today's computer networks are subject to continuous evolution. Such ever-growing computer networks in size and complexity lead to information exposure to an increased threat landscape and attack surface variation. The network attack surface constitutes exploitable technical vulnerabilities, software/hardware misconfigurations (i.e., configuration gaps), vulnerable service connectivities, and service-cum-user privileges. An adversary may exploit the network attack surface to penetrate the enterprise networks incrementally. The discovery of new vulnerabilities and vague access control rules can further set off the attack surface variation. Hence, it is essential to consider the temporal aspect of network security. Attack graph, a graphical network security modeling tool, succinctly captures the attack surface of a vulnerable network in the form of initial security conditions, much needed for an adversary for successful incremental network penetration. Existing attack graph-based metrics are inadequate in capturing the variation in the attack surface. We propose to use a Boolean similarity metric to assess the similarity between the goal-oriented attack graphs generated successively for an enterprise network within the chosen sampling interval. We represent individual attack graphs as a Boolean expression to serve our purpose. A Boolean expression is a sum-of-product expression, i.e., a disjunction of attack paths, each a conjunction of initial conditions. We have conducted a set of experiments to validate the efficacy and applicability of the Boolean similarity metric. The results indicate that the Boolean similarity measure can detect the variation in the network attack surface.

The extensive use of information and communication technology in government and private organizations brings new security vulnerabilities. These vulnerabilities provide multiple opportunities for adversaries to compromise organizations' business-critical resources. Nowadays, new types of sophisticated Cyberattacks, namely “multistage attacks” keep increasing in sophistication and number. Essentially, the adversary chains together multiple vulnerabilities and exploits them to obtain incremental access to the network resources. In practice, mitigating all the identified vulnerabilities, even for a moderate-sized network, is impractical for the security administrator. Existing vulnerability scanners do not consider the causal dependency between the identified vulnerabilities. Moreover, most vulnerabilities reported by scanners are not exploitable because of the absence of enabling condition(s). Therefore, the administrators' absolute reliance on vulnerability scanners makes the vulnerability patching process ineffective. Attack graph, a popular graphical network security model, depicts potential multistage, multi-host attacks for a vulnerable network configuration and thereby helps the administrator harden the network effectively. We propose a framework based on recursive composition algebra to explore the additional advantages of using an attack graph for proactive network hardening. The algebra generates an attack graph (free from attack cycles) for a vulnerable network configuration. Moreover, the proposed framework classifies the identified vulnerabilities. The vulnerability classes help the administrator prioritize the network hardening activities. We have validated the effectiveness and applicability of our framework through a case study.

Forensic investigation after the failure of a bonded post-tension (PT) tendon has shown that in most cases, these failures occur because the strands were not properly protected by the grout. There is often a void or deficient grout which provides an environment that will lead to strand corrosion, and if left unmitigated, eventually failure. Identification of PT tendon grout defects is very difficult to perform with standard inspection techniques and these PT issues are commonly not identified until a serious failure occurs. This paper will review the innovative ways nondestructive testing can locate voided or defective grout and document corrosion in external and internal PT tendons. The paper will focus on the use of the acoustic method, impact echo/pulse velocity (IE/PV), which can determine voiding by analyzing the amplitude and frequency of the resonating compressional, and shear waves along the PT tendons. Through analysis of these stress waves, a trained practitioner can locate defects in the grouting of both internal and external tendons. The paper will provide case studies of two different bridges where a failed PT tendon on each structure prompted an investigation into the condition of the other tendons. It was discovered that grout voids were a prominent issue throughout the structures and that corrosion was at advanced stages.

ABSTRACTBackgroundThe COVID-19 pandemic has brought unparalleled challenges for health systems worldwide, the impact of which has also been borne by the Healthcare Professionals (HCPs). Numerous studies have revealed the positive effects of Pranayama and Meditation on mental health. The effect of Pranayama in improving mental health of frontline HCP exposed to Covid-19 patients has not been studied.Aim & ObjectiveThis quasi-randomized clinical trial was done to study the effect of especially designed Pranayama protocol on perceived stress, wellbeing and quality of life of frontline health care professionals who were exposed to COVID-19 patients in hospital settings.MethodologyThis study was done with 280 frontline healthcare professionals (HCP) assigned duties with COVID-19 patients during September-November, 2020 in 5 government hospitals and COVID-19 quarantine/isolation centres in New Delhi, India. The HCPs were first assessed for COVID-19 infection in the past using antibody test, and only those found negative were recruited. The enrolled respondents were randomly assigned to two arms – an intervention arm where there were to practice 28-day Pranayama module (morning and evening sessions) under supervision of a trainer, and a Control arm where the HCPs continued routine physical activity (walking, jogging etc.). Baseline and end-line (total: 250 HCPs) Psychological parameters of Perceived Stress, Well Being and Quality of Life were collected through self-reported questionnaires.ResultsThe intervention (HCPs: 123) and control (HCPs: 127) groups (Total: 250) were comparable in their demographic profile and baseline characteristics. Intervention with Pranayama module led to a significant reduction (Mean diff: -2.46; P-value: 0.028) in perceived stress score in the intervention group compared to the control group. The wellbeing index in Interventional group intervention showed a non-significant increase. The WHO Quality-of-life score increased in the intervention group as compared to the controls (mean difference 2.78, p-value: 0.17). Of its four components, the one for Psychological domain increased significantly (mean diff: 1.52, P-value: 0.019), while those for Physical domain and Environmental domains increased (mean diff: 0.64, P-value: 0.29 and mean diff: 0.68, p-value: 0.48) though not statistical significantly.CconclusionThe intervention of twice daily practice of the Pranayama module for 28 days in frontline HCPs performing COVID-19 duties had a noteworthy effect in lowering Perceived Stress, improving perceived Quality of life, especially its Psychological domains as measured through standardized questionnaires.CTRI NumberCTRI/2020/07/026667