Shuffling Based Mechanism for DDoS Prevention on Cloud Environment

Cloud Computing has evolved as a new paradigm in which users can use on-demand services, according to their needs. However, security concerns are primary obstacles to a wider adoption of clouds. Newly born concepts that clouds introduced, such as multi-tenancy, resource sharing and outsourcing, create new challenges for the security research. DDoS (Distributed Denial of service) attack is the biggest threat to the cloud since it affects the availability of services. There are a lot of techniques proposed by various researchers to prevent DDoS attacks on a cloud infrastructure. We are using a Shuffling Based approach for preventing DDoS in the cloud environment. This approach is reactive and uses the resource elasticity of the cloud. The aim of this technique is to save the maximum number of benign clients from the attack through shuffling. For assignment of clients to the replica servers, we are using a greedy algorithm. Every time we call this algorithm, we estimate the number of malicious clients using a proposed random function for that round of shuffle. We have shown that we can save a desired percentage of benign clients from the ongoing attacks after some shuffles. To detect the attack on each server, a detector is deployed that uses an entropy-based approach for detecting DDoS. A significant deviation in entropy represents the DDoS attack. We have also performed some tests to select the suitable attributes for entropy-based DDoS detection in different type of DDoS attacks. So in our work we have worked on both detection and prevention of DDoS on cloud infrastructur

Neuroprotective Strategies of Blood-Brain Barrier Penetrant “Forskolin” (AC/cAMP/PK_A/CREB Activator) to Ameliorate Mitochondrial Dysfunctioning in Neurotoxic Experimental Model of Autism

New developments in the study of brain are among the most exciting frontiers of contemporary neuroscientific research for the clinical practitioner. Increasing knowledge of neurocomplications and of their discrete localization in the various regions of brain permits new modes of pharmacological management of some major neurological disorders like autism. The research work reported in this scheme is undertaken with an objective to explore the potential molecular targets (AC/cAMP/PKA/CREB) for the development of newer therapeutics strategies (forskolin) for the management of neurological disorders and associated symptoms. Studies aimed at addressing these questions have fallen into two main categories: in-vivo behavioral paradigms and in-vitro differentiation biochemical, morphological and histopathological analysis. Therefore, first time, we aim to gather the propensity of mitochondrial cofactors, neuropathological mechanisms and various diagnostic methods to explore the clinical therapeutic strategies to ameliorate the neurodevelopmental disorder autism

An Innovative Strategy of Energy Generation using Piezoelectric Materials: A Review

Certain material when strained produce electric potential over their surface which is directly proportional to the amount of mechanical stress applied. These materials are known as piezoelectric materials and this effect is referred as a direct piezoelectric effect. Piezoelectricity is intensely used in the working of transducers, actuators, surface acoustic wave devices, frequency controls, etc. Use of piezoelectric material for power generation is now becoming a new promising area of its usage. Many countries like Japan, Israel India have already moved ahead in this direction with its wide range of experimentation and testing on using the material as a source for power generation. Also, with the advancement in the manufacturing and production capabilities of these materials the aspects like performance, affordability, reliability, easy implantation and longevity have greatly enhanced. This paper focuses on using the piezoelectric material as a power generating source and extension of its use in various areas

Renal Manifestations of Tuberous Sclerosis Complex

Tuberous sclerosis complex (TSC) is a genetic condition caused by a mutation in either the TSC1 or TSC2 gene. Disruption of either of these genes leads to impaired production of hamartin or tuberin proteins, leading to the manifestation of skin lesions, tumors, and seizures. TSC can manifest in multiple organ systems with the cutaneous and renal systems being the most commonly affected. These manifestations can secondarily lead to the development of hypertension, chronic kidney disease, and neurocognitive declines. The renal pathologies most commonly seen in TSC are angiomyolipoma, renal cysts, and less commonly, oncocytomas. In this review, we highlight the current understanding on the renal manifestations of TSC along with current diagnosis and treatment guidelines

Modeling of Breakdown Voltage of White Minilex Paper in Presence of Voids Under AC and DC conditions using Artificial Neural Network as Computational Method

Insulating materials contain various voids due to which when an electrical signal is passed through the insulator above a threshold level they start deteriorating and breakdown occurs. Hence it is of great importance to find out the breakdown voltage of an insulator. In this project the Artificial Neural Network method has been employed to model the desired breakdown voltages under AC and DC conditions. By using neural networks a relationship between the input parameters and breakdown voltage has been established. The insulating material used is White Minilex Paper. Voids of varying dimensions are created artificially. The calculated values of mean absolute error and mean square error show the effectiveness of the model

Estimating nitrogen risk to Himalayan forests using thresholds for lichen bioindicators

Himalayan forests are biodiverse and support the cultural and economic livelihoods of their human communities. They are bounded to the south by the Indo-Gangetic Plain, which has among the highest concentrations of atmospheric ammonia globally. This source of excess nitrogen pushes northwards into the Himalaya, generating concern that Himalayan forests will be impacted. To estimate the extent to which atmospheric nitrogen is impacting Himalayan forests we focussed on lichen epiphytes, which are a well-established bioindicator for atmospheric nitrogen pollution. First, we reviewed published literature describing nitrogen thresholds (critical levels and loads) at which lichen epiphytes are affected, identifying a mean and confidence intervals based on previous research conducted across a diverse set of biogeographic and ecological settings. Second, we used estimates from previously published atmospheric chemistry models (EMEP-WRF and UKCA-CLASSIC) projected to the Himalaya with contrasting spatial resolution and timescales to characterise model variability. Comparing the lichen epiphyte critical levels and loads with the atmospheric chemistry model projections, we created preliminary estimates of the extent to which Himalayan forests are impacted by excess nitrogen; this equated to c. 80–85% and c. 95–98% with respect to ammonia and total nitrogen deposition, respectively. Recognising that lichens are one of the most sensitive bioindicators for atmospheric nitrogen pollution, our new synthesis of previous studies on this topic generated concern that most Himalayan forests are at risk from excess nitrogen. This is a desk-based study that now requires verification through biological surveillance, for which we provide key recommendations