Efficient intrusion detection scheme based on SVM

The network intrusion detection problem is the focus of current academic research. In this paper, we propose to use Support Vector Machine (SVM) model to identify and detect the network intrusion problem, and simultaneously introduce a new optimization search method, referred to as Improved Harmony Search (IHS) algorithm, to determine the parameters of the SVM model for better classification accuracy. Taking the general mechanism network system of a growing city in China between 2006 and 2012 as the sample, this study divides the mechanism into normal network system and crisis network system according to the harm extent of network intrusion. We consider a crisis network system coupled with two to three normal network systems as paired samples. Experimental results show that SVMs based on IHS have a high prediction accuracy which can perform prediction and classification of network intrusion detection and assist in guarding against network intrusion

Survey of Data Confidentiality and Privacy in the Cloud Computing Environment

The objective of this research is to develop a scheme for improving cloud data confidentiality. A considerable number of people are sharing data through third-party applications in the cloud computing environment. According to reviewed literature, it has been realized that data security and privacy were the key challenges to the wider adoption of cloud services with insider threats being the most prevalent. Similarly, our online survey indicated that 53.3% of the respondents citing insider breaches as the main threat to their organizational data. The survey also confirmed that data security and privacy is one of the greatest barriers to the adoption of cloud services in their organization. Noting the flaws of Attribute-Based Encryption (ABE) and Identity-based encryption (IBE), and with the growth of computing power, applications are constantly being developed which makes them vulnerable to attacks. Since data confidentiality is essential in the provision of information security in the cloud, this paper suggested the development and the deployment of a hybrid attribute-based re-encryption scheme, which is a scheme that bridges the ABE and IBE, to secure data in the cloud computing environment. Keywords: Encryption, Cloud Computing, Data, confidentiality, Privacy DOI: 10.7176/CEIS/11-5-03 Publication date:September 30th 2020

Electrokinetic delivery of persulfate to remediate PCBs polluted soils: Effect of different activation methods

Persulfate-based in-situ chemical oxidation (ISCO) for the remediation of organic polluted soils has gained much interest in last decade. However, the transportation of persulfate in low-permeability soil is very low, which limits its efficiency in degrading soil pollutants. Additionally, the oxidation-reduction process of persulfate with organic contaminants takes place slowly, while, the reaction will be greatly accelerated by the production of more powerful radicals once it is activated. Electrokinetic remediation (EK) is a good way for transporting persulfate in low-permeability soil. In this study, different activation methods, using zero-valent iron, citric acid chelated Fe²⁺, iron electrode, alkaline pH and peroxide, were evaluated to enhance the activity of persulfate delivered by EK. All the activators and the persulfate were added in the anolyte. The results indicated that zero-valent iron, alkaline, and peroxide enhanced the transportation of persulfate at the first stage of EK test, and the longest delivery distance reached sections S4 or S5 (near the cathode) on the 6th day. The addition of activators accelerated decomposition of persulfate, which resulted in the decreasing soil pH. The mass of persulfate delivered into the soil declined with the continuous decomposition of persulfate by activation. The removal efficiency of PCBs in soil followed the order of alkaline activation > peroxide activation > citric acid chelated Fe²⁺ activation > zero-valent iron activation > without activation > iron electrode activation, and the values were 40.5%, 35.6%, 34.1%, 32.4%, 30.8% and 30.5%, respectively. The activation effect was highly dependent on the ratio of activator and persulfate

Opposite Effects of Low and High Doses of Aβ42 on Electrical Network and Neuronal Excitability in the Rat Prefrontal Cortex

Changes in neuronal synchronization have been found in patients and animal models of Alzheimer's disease (AD). Synchronized behaviors within neuronal networks are important to such complex cognitive processes as working memory. The mechanisms behind these changes are not understood but may involve the action of soluble β-amyloid (Aβ) on electrical networks. In order to determine if Aβ can induce changes in neuronal synchronization, the activities of pyramidal neurons were recorded in rat prefrontal cortical (PFC) slices under calcium-free conditions using multi-neuron patch clamp technique. Electrical network activities and synchronization among neurons were significantly inhibited by low dose Aβ42 (1 nM) and initially by high dose Aβ42 (500 nM). However, prolonged application of high dose Aβ42 resulted in network activation and tonic firing. Underlying these observations, we discovered that prolonged application of low and high doses of Aβ42 induced opposite changes in action potential (AP)-threshold and after-hyperpolarization (AHP) of neurons. Accordingly, low dose Aβ42 significantly increased the AP-threshold and deepened the AHP, making neurons less excitable. In contrast, high dose Aβ42 significantly reduced the AP-threshold and shallowed the AHP, making neurons more excitable. These results support a model that low dose Aβ42 released into the interstitium has a physiologic feedback role to dampen electrical network activity by reducing neuronal excitability. Higher concentrations of Aβ42 over time promote supra-synchronization between individual neurons by increasing their excitability. The latter may disrupt frontal-based cognitive processing and in some cases lead to epileptiform discharges

Impaired atrial electromechanical function and atrial fibrillation promotion in alloxan-induced diabetic rabbits

Background: Diabetes mellitus (DM) is an independent risk factor for atrial fibrillation (AF). However, the underlying mechanisms are still not clearly elucidated. The aim of this study was to evaluate the atrial electromechanical function, atrial electrophysiological changes and AF inducibility in alloxan-induced diabetic rabbits. Methods: In 8 alloxan-induced diabetic rabbits and 8 controls, we evaluated atrial electromechanical function by tissue Doppler imaging. Isolated Langendorff-perfused rabbit hearts were prepared to measure atrial refractory effective period (AERP) and its dispersion (AERPD), interatrial conduction time (IACT) and vulnerability to AF. Atrial interstitial fibrosis was evaluated by Sirius-Red staining. Results: Compared with controls, left atrial lateral wall Pa’-start interval (Pastart) and right atrial wall Pastart were increased in diabetic rabbits. AERPD was increased and IACT was prolonged in diabetic rabbits. Inducibility of AF in diabetic group was significant higher than controls (6/8 vs. 1/8, p < 0.05). Extensive interstitial fibrosis was observed in the DM group (p < 0.01). Correlation analysis showed that right atrial wall Pastart, Pa’-peak interval (Papeak) and total electromechanical activity (TEMA); left atrial lateral wall Papeak and TEMA, left atrial posterior wall TEMA, and IACT were correlated with atrial areas of fibrosis. Conclusions: Atrial electromechanical function is impaired in diabetic rabbits, and is associated with atrial fibrosis and interatrial electrical conduction delay

A Divide-and-Conquer Algorithm for Machining Feature Recognition over Network

In this paper, a divide-and-conquer algorithm for machining feature recognition over network is presented. The algorithm consists of three steps. First, decompose the part and its stock into a number of sub-objects in the client and transfer the sub-objects to the server one by one. Meanwhile, perform machining feature recognition on each sub-object using the MCSG based approach in the server in parallel. Finally, generate the machining feature model of the part by synthesizing all the machining features including decomposed features recognized from all the sub-objects and send it back to the client. With divide-and-conquer and parallel computing, the algorithm is able to decrease the delay of transferring a complex CAD model over network and improve the capability of handling complex parts. Implementation details are included and some test results are given

A novel statistical technique for intrusion detection systems

This paper proposes a novel approach for intrusion detection system based on sampling with Least Square Support Vector Machine (LS-SVM). Decision making is performed in two stages. In the first stage, the whole dataset is divided into some predetermined arbitrary subgroups. The proposed algorithm selects representative samples from these subgroups such that the samples reflect the entire dataset. An optimum allocation scheme is developed based on the variability of the observations within the subgroups. In the second stage, least square support vector machine (LS-SVM) is applied to the extracted samples to detect intrusions. We call the proposed algorithm as optimum allocation-based least square support vector machine (OA-LS-SVM) for IDS. To demonstrate the effectiveness of the proposed method, the experiments are carried out on KDD 99 database which is considered a de facto benchmark for evaluating the performance of intrusions detection algorithm. All binary-classes and multiclass are tested and our proposed approach obtains a realistic performance in terms of accuracy and efficiency. Finally a way out is also shown the usability of the proposed algorithm for incremental datasets

Galaxy Morphology Classification Using Multi-Scale Convolution Capsule Network

The classification of galaxy morphology is a hot issue in astronomical research. Although significant progress has been made in the last decade in classifying galaxy morphology using deep learning technology, there are still some deficiencies in spatial feature representation and classification accuracy. In this study, we present a multi-scale convolutional capsule network (MSCCN) model for the classification of galaxy morphology. First, this model improves the convolutional layers through using a multi-branch structure to extract multi-scale hidden features of galaxy images. In order to further explore the hidden information in the features, the multi-scale features are encapsulated and fed into the capsule layer. Second, we use a sigmoid function to replace the softmax function in dynamic routing, which can enhance the robustness of MSCCN. Finally, the classification model achieving 97% accuracy, 96% precision, 98% recall, and 97% F1-score under macroscopic averaging. In addition, a more comprehensive model evaluation were accomplished in this study. We visualized the morphological features for the part of sample set, which using the t-distributed stochastic neighbor embedding (t-SNE) algorithm. The results shows that the model has the better generalization ability and robustness, it can be effectively used in the galaxy morphological classification

NADPH oxidase mediates oxidative stress and ventricular remodeling through SIRT3/FOXO3a pathway in diabetic mice

Oxidative stress and mitochondrial dysfunction are important mechanisms of ventricular remodeling, predisposed to the development of diabetic cardiomyopathy (DCM) in type 2 diabetes mellitus. In this study, we have successfully established a model of type 2 diabetes using a high-fat diet (HFD) in combination with streptozotocin (STZ). The mice were divided into three groups of six at random: control, diabetes, and diabetes with apocynin and the H9c2 cell line was used as an in vitro model for investigation. We examined the molecular mechanisms of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activation on mitochondrial dysfunction and ventricular remodeling in the diabetic mouse model. Hyperglycemia-induced oxidative stress led to a reduced expression of sirtuin 3 (SIRT3), thereby promoting forkhead box class O 3a (FOXO3a) acetylation in ventricular tissue and H9c2 cells. Reactive oxygen species (ROS) overproduction promoted ventricular structural modeling and conduction defects. These alterations were mitigated by inhibiting NADPH oxidase with the pharmaceutical drug apocynin (APO). Apocynin improved SIRT3 and Mn-SOD expression in H9c2 cells transfected with SIRT3 siRNA. In our diabetic mouse model, apocynin improved myocardial mitochondrial function and ROS overproduction through the recovery of the SIRT3/FOXO3a pathway, thereby reducing ventricular remodeling and the incidence of DCM

Anti-drug Antibody Responses Impair Prophylaxis Mediated by AAV-Delivered HIV-1 Broadly Neutralizing Antibodies

Adeno-associated virus (AAV) delivery of potent and broadly neutralizing antibodies (bNAbs is a promising approach for the prevention of HIV-1 infection. The immunoglobulin G (IgG)1 subtype is usually selected for this application, because it efficiently mediates antibody effector functions and has a somewhat longer half-life. However, the use of IgG1-Fc has been associated with the generation of anti-drug antibodies (ADAs) that correlate with loss of antibody expression. In contrast, we have shown that expression of the antibody-like molecule eCD4-Ig bearing a rhesus IgG2-Fc domain showed reduced immunogenicity and completely protected rhesus macaques from simian-HIV (SHIV)-AD8 challenges. To directly compare the performance of the IgG1-Fc and the IgG2-Fc domains in a prophylactic setting, we compared AAV1 expression of rhesus IgG1 and IgG2 forms of four anti-HIV bNAbs: 3BNC117, NIH45-46, 10-1074, and PGT121. Interestingly, IgG2-isotyped bNAbs elicited significantly lower ADA than their IgG1 counterparts. We also observed significant protection from two SHIV-AD8 challenges in macaques expressing IgG2-isotyped bNAbs, but not from those expressing IgG1. Our data suggest that monoclonal antibodies isotyped with IgG2-Fc domains are less immunogenic than their IgG1 counterparts, and they highlight ADAs as a key barrier to the use of AAV1-expressed bNAbs