Impregnable Defence Architecture using Dynamic Correlation-based Graded Intrusion Detection System for Cloud

Data security and privacy are perennial concerns related to cloud migration, whether it is about applications, business or customers. In this paper, novel security architecture for the cloud environment designed with intrusion detection and prevention system (IDPS) components as a graded multi-tier defense framework. It is a defensive formation of collaborative IDPS components with dynamically revolving alert data placed in multiple tiers of virtual local area networks (VLANs). The model has two significant contributions for impregnable protection, one is to reduce alert generation delay by dynamic correlation and the second is to support the supervised learning of malware detection through system call analysis. The defence formation facilitates malware detection with linear support vector machine- stochastic gradient descent (SVM-SGD) statistical algorithm. It requires little computational effort to counter the distributed, co-ordinated attacks efficiently. The framework design, then, takes distributed port scan attack as an example for assessing the efficiency in terms of reduction in alert generation delay, the number of false positives and learning time through comparison with existing techniques is discussed

Preliminary in Vitro- Investigation on Antimicrobial Activity of Mononuclear and Dinuclear Iron (III) Complexes

In vitro effect of two dinuclear and mononuclear iron complexes with different ligands was examined on Gram positive and Gram negative bacterial strains. Broad spectrum antibiotic oxytetracycline was used as control. The experiment was performed by the routine agar-diffusion method of bauer et al. and the method of  minimum  inhibitory concentrations (MICs). It was  found  that mononuclear complexes expressed antibacterial effect in vitro, especially against Gram (+) strains. The minimum inhibitory effect of Fe (NADP) Cl2 was more pronounced

NANOENCAPSULATION AUGMENTS RELEASE EFFICACY AND GLUCOSE TOLERANCE OF 14-DEOXY, 11, 12-DIDEHYDRO ANDROGRAPHOLIDE LOADED POLYCAPROLACTONE NANOPARTICLES IN STREPTOZOTOCIN-NICOTINAMIDE INDUCED TYPE 2 DIABETES

Objective: This study was designed to study the release efficacy and glucose tolerance of 14-deoxy, 11, 12-didehydroandrographolide loaded polycaprolactone nanoparticles in streptozotocin-nicotinamide induced type 2 diabetes. Methods: Biodegradable polymer based novel drug delivery systems had brought a considerable attention to improve therapeutic efficacy and bioavailability of various drugs. In this study, 14-deoxy-11, 12-didehydroandrographolide (sparingly water soluble) loaded polycaprolactone (nano-DDA) was synthesized using polyvinyl alcohol and tween20 as surfactants. MTT assay was performed to analyse the cytotoxicity of both the formulations on L6 myoblasts. Free DDA and nano-DDA were administered orally to the streptozotocin-nicotinamide induced experimental diabetic rats for 45d. Oral glucose tolerance test (OGTT) was carried out at the end of the study. After one week washout period, animals were administered with free and nano-DDA and release efficacy of DDA from polymer matrix and concentration of glucose were analysed. Results: MTT assay revealed that nano-DDA prepared using tween-20 as a surfactant elicited cytotoxicity towards L6 myoblasts, whereas nano-DDA prepared using polyvinyl alcohol as a surfactant remained non-toxic till 10µM. OGTT studies revealed an initial increase of glucose at 30 min followed by a progressive decrease in the glucose level. In rat plasma, a gradual decrease in glucose level was observed up to 32h (139 mg/dl) for free DDA, whereas nano-DDA exhibited a major decrease in glucose concentration at 32h (115 mg/dl) which continued even after 48h (117 mg/dl). Conclusion: A slow and sustained release of DDA from the polymer matrix substantiated that nanoencapsulation enhanced the oral bioavailability of DDA which resulted in decreasing the concentration of glucose which could be due to the pronounced antihyperglycemic activity of nano-DDA over free DDA

Using Attribute-Based Access Control, Efficient Data Access in the Cloud with Authorized Search

The security and privacy issues regarding outsourcing data have risen significantly as cloud computing has grown in demand. Consequently, since data management has been delegated to an untrusted cloud server in the data outsourcing phase, data access control has been identified as a major problem in cloud storage systems. To overcome this problem, in this paper, the access control of cloud storage using an Attribute-Based Access Control (ABAC) approach is utilized. First, the data must be stored in the cloud and security must be strong for the user to access the data. This model takes into consideration some of the attributes of the cloud data stored in the authentication process that the database uses to maintain data around the recorded collections with the user\u27s saved keys. The clusters have registry message permission codes, usernames, and group names, each with its own set of benefits. In advance, the data should be encrypted and transferred to the service provider as it establishes that the data is still secure. But in some cases, the supplier\u27s security measures are disrupting. This result analysis the various parameters such as encryption time, decryption time, key generation time, and also time consumption. In cloud storage, the access control may verify the various existing method such as Ciphertext Policy Attribute-Based Encryption (CP-ABE) and Nth Truncated Ring Units (NTRU). The encryption time is 15% decreased by NTRU and 31% reduced by CP-ABE. The decryption time of the proposed method is 7.64% and 14% reduced by the existing method

Amelioration of Heat Stress Induced Disturbances of Antioxidant Defense System in Chicken by Brahma Rasayana

Since the range of comfort zone or thermo neutral zone of domestic chickens is narrow, they become easily susceptible to heat and cold environmental stress. We evaluated Brahma Rasayana (BR) supplementation on concentrations of certain oxidative stress markers associated with heat stress. A total of 48 egg type male chickens of local strain were divided into six groups (n = 8) for the study. Three groups were fed with BR orally at the rate of 2 g/kg bw daily for 10 days prior to and during the period of experiment. Two of the four groups that were exposed to heat stress (HST i.e. to a temperature of 40 ± 1°C and relative humidity of 80 ± 5% in an environmental chamber) for 4 h daily for 5 or 10 days, received BR orally. The other two groups remained as BR treated and untreated non-heat stressed (NHST) controls. There was a significant (P < 0.05) increase in the activities of antioxidant enzymes in blood such as catalase (CAT) and superoxide dismutase (SOD), as well as liver CAT, glutathione peroxidase (GPX) and glutathione reductase (GR) in NHST-BR treated and HST-BR treated (both 5 and 10 days) chickens when compared with untreated controls. A great deal of significant (P < 0.05) variations were seen in serum and liver reduced glutathione (GSH) concentration in NHST-BR treated and HST-BR treated (both 5 and 10 days) chickens. Serum and liver lipid peroxidation levels were found to be significantly (P < 0.05) higher in HST-untreated (both 5 and 10 days) chickens when compared with other groups. Thus BR supplementation during HST brings about enhanced action of enzymatic and non-enzymatic antioxidants, which nullified the undesired side effects of free radicals that are generated during HST

Evaluation of Bag of Visual Words for Category Level Object Recognition

Object recognition in a large scale collection of images has become an important application in machine vision. The recent advances in the object or image recognition for classification of objects shows that Bag-of-visual words approach is a better method for image classification problems. In this work, the effect of different possible parameters and performance evaluation of Bag of visual words approach in terms of their recognition performance such as Accuracy rate, Precision and F1 measure using 8 different classes of real world datasets that are commonly used in restaurant applications is explored. The system presented here is based on visual vocabulary. Features are extracted, clustered, trained and evaluated on an image database of 1600 images of different categories. To validate the obtained results,a performance evaluation on vehicle datasetsunder SURF and SIFT descriptors with Kmeans and K-medoid clustering and KNN classifier has been made. Among these SURF K-means performs better

Validity of international ovarian tumour analysis simple rules in characterization of ovarian mass

Background: Ovarian malignancy is one of the most common cancer in women and is diagnosed at later stage in majority. The limiting factor for early diagnosis is lack of standardized terms and procedures in gynaecological sonography. Recently, IOTA simple rules have been externally validated to have an increased sensitivity and specificity in diagnosing ovarian malignancy. Methods: This is a prospective study in the Department of obstetrics and gynaecology conducted at ESIC-MC &amp; PGIMSR Hospital, Bangalore from January 2020 to June 2021. 50 women diagnosed with ovarian mass and scheduled for surgery were admitted and evaluated for nature of ovarian mass using IOTA simple rules on ultrasonography and correlated with their histopathological diagnosis. Results: Among 50 ovarian masses, all 38 masses (76%) characterized as benign by IOTA simple rules were true benign (100%) on histopathological diagnosis. 10 masses (20%) characterized as malignant, 9 were true malignant and 1 was false malignant on histopathological diagnosis. 2 cases which were inconclusive by IOTA simple rules were characterized as benign on histopathological diagnosis. Thus in our study test sensitivity was 100%, specificity 97.56%, positive predictive value 90% and negative predictive value 100%. Conclusions: In clinical practice, IOTA simple rules as a diagnostic tool helps in characterization of most ovarian masses, which aids in optimal management and enhance better outcome. In ovarian masses for which the rules yielded an inconclusive results, subjective assessment by an experienced sonologist is advocatedd

Maternal pregnancy associated plasma protein-A (PAPP-A) and uterine artery Doppler changes as predictors of pre-eclampsia: a prospective observational study from a teaching hospital in Mysore, Karnataka, India

Background: Hypertensive disorder affects 10-12% of pregnancies. Identifying women, who are at risk is conducive to prompt gestational management. PAPP-A is a protein complex produced by the developing trophoblasts. Low levels of PAPP-A at 10–14 weeks is a marker of impaired placentation and a smaller placental mass. Doppler imaging permits non-invasive evaluation of the uteroplacental circulation and is invaluable in the management of high-risk pregnancies. The uterine artery Doppler screening identifies patients at risk for developing preeclampsia. To study the association of PAPP-A and the uterine artery Doppler changes as predictor of pre-eclampsia in pregnant women at 11-14 weeks of gestation.Methods: This was a prospective study of 150 pregnant women presenting at 11-14 weeks of gestation for a prenatal check-up. After considering the inclusion and exclusion criteria, serum samples for PAPP-A were assayed. Ultrasound Doppler was used to obtain uterine artery flow velocity waveforms and mean pulsatility index and resistance index of uterine arteries were calculated. Cases were followed up till term and observed for development of pre-eclampsia.Results: 48.6% had low serum PAPP-A levels, in which 77% developed PE. The Mean PI and RI is 2.34±1.16 and 0.58±0.1 respectively. 30% women with abnormal PI values and 24% of women with abnormal RI values developed PE.Conclusions: The combination of maternal history with low serum PAPP-A levels and abnormal uterine artery Doppler at 11-14 weeks can be used as predictor of pre-eclampsia

Early heart disease detection using data mining techniques with hadoop map reduce Early Heart Disease Detection Using Data Mining Techniques with Hadoop Map Reduce

International audienceHeart and other organs are important parts in human body. As per World Health Organisation(WHO)'s statistics, the cause of death in all over world is mostly due to cardiovascular diseases. The reason behind this are sedentary lifestyle which may lead to obesity, increase in cholesterol level, high blood pressure and hypertension. In this paper, by using various data mining techniques, such as Naive Bayes(NB), Decision Tree(DT), Artificial Intelligence (AI), Neural Network (NN) and clustering algorithms such as Association Rules. Support Vector Machine (SVM) and K-NN algorithms are used to extract the Knowledge from the large number of data set. The generated reports help doctors and nurses to identify about disease and their levels with which they can provide a better treatment to the patient. Text Mining is most commonly used mining technique in health care industry. In this paper we compare K-means clustering algorithm with Map Reduce Algorithm's implementation efficiency in parallel and distributed systems