Botnet-based Distributed Denial of Service (DDoS) Attacks on Web Servers: Classification and Art

Botnets are prevailing mechanisms for the facilitation of the distributed denial of service (DDoS) attacks on computer networks or applications. Currently, Botnet-based DDoS attacks on the application layer are latest and most problematic trends in network security threats. Botnet-based DDoS attacks on the application layer limits resources, curtails revenue, and yields customer dissatisfaction, among others. DDoS attacks are among the most difficult problems to resolve online, especially, when the target is the Web server. In this paper, we present a comprehensive study to show the danger of Botnet-based DDoS attacks on application layer, especially on the Web server and the increased incidents of such attacks that has evidently increased recently. Botnet-based DDoS attacks incidents and revenue losses of famous companies and government websites are also described. This provides better understanding of the problem, current solution space, and future research scope to defend against such attacks efficiently

A new proactive feature selection model based on the enhanced optimization algorithms to detect DRDoS attacks

Cyberattacks have grown steadily over the last few years. The distributed reflection denial of service (DRDoS) attack has been rising, a new variant of distributed denial of service (DDoS) attack. DRDoS attacks are more difficult to mitigate due to the dynamics and the attack strategy of this type of attack. The number of features influences the performance of the intrusion detection system by investigating the behavior of traffic. Therefore, the feature selection model improves the accuracy of the detection mechanism also reduces the time of detection by reducing the number of features. The proposed model aims to detect DRDoS attacks based on the feature selection model, and this model is called a proactive feature selection model proactive feature selection (PFS). This model uses a nature-inspired optimization algorithm for the feature subset selection. Three machine learning algorithms, i.e., k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM), were evaluated as the potential classifier for evaluating the selected features. We have used the CICDDoS2019 dataset for evaluation purposes. The performance of each classifier is compared to previous models. The results indicate that the suggested model works better than the current approaches providing a higher detection rate (DR), a low false-positive rate (FPR), and increased accuracy detection (DA). The PFS model shows better accuracy to detect DRDoS attacks with 89.59%

The Clinical and Nonclinical Values of Nonexercise Estimation of Cardiovascular Endurance in Young Asymptomatic Individuals

Exercise testing is associated with barriers prevent using cardiovascular (CV) endurance (CVE) measure frequently. A recent nonexercise model (NM) is alleged to estimate CVE without exercise. This study examined CVE relationships, using the NM model, with measures of obesity, physical fitness (PF), blood glucose and lipid, and circulation in 188 asymptomatic young (18–40 years) adults. Estimated CVE correlated favorably with measures of PF (r = 0.4 − 0.5) including handgrip strength, distance in 6 munities walking test, and shoulder press, and leg extension strengths, obesity (r = 0.2 − 0.7) including % body fat, body water content, fat mass, muscle mass, BMI, waist and hip circumferences and waist/hip ratio, and circulation (r = 0.2 − 0.3) including blood pressures, blood flow, vascular resistance, and blood (r = 0.2 − 0.5) profile including glucose, total cholesterol, LDL-C, HDL-C, and triglycerides. Additionally, differences (P < 0.05) in examined measures were found between the high, average, and low estimated CVE groups. Obviously the majority of these measures are CV disease risk factors and metabolic syndrome components. These results enhance the NM scientific value, and thus, can be further used in clinical and nonclinical settings

Vascular Function and Handgrip Strength in Rheumatoid Arthritis Patients

Objective. To examine the relationship of handgrip strength with forearm blood flow (BF) and vascular resistance (VR) in rheumatoid arthritis (RA) patients. Methods. Forearm BF at rest (RBF) and after upper arm occlusion (RHBF), and handgrip strength were examined in 78 individuals (RA = 42 and controls (CT) = 36). Subsequently, VR at rest (RVR) and after occlusion (RHVR) were calculated. Results. The patients' RBF (P = 0.02) and RHBF (P = 0.0001) were less, whereas RVR (P = 0.002) and RHVR (P = 0.0001) were greater as compared to the CTs. Similarly, handgrip strength was lower in the RAs (P = 0.0001). Finally, handgrip strength was directly associated with RBF (r = 0.43; P = 0.0001), and RHBF (r = 0.5; P = 0.0001), and inversely related to RVR (r = −0.3; P = 0.009) and RHVR (r = −0.3; P = 0.007). Conclusion. The present study uniquely identifies an association between regional measures of forearm blood flow and handgrip strength in patients and healthy control. In addition, this study confirms the presence of vascular and muscle dysfunction in patients with rheumatoid arthritis, as evidenced by lower forearm blood flow indices, at rest and following occlusion, and lower handgrip strength as compared to healthy individuals

Improving Design and Construction of Transportation Infrastructure Through Bedrock Characterization

RS09220, 69A3551747108This study evaluates a comprehensive experimental investigation to better understand the mechanical and deformation behavior of Wyoming bedrocks and therefore to improve the design and construction of transportation infrastructure. Fifty-six rock samples were collected from different locations around the state of Wyoming, including different rock types, formations, geologic ages, and depths. The prominent rock types are sandstone, shale, siltstone, claystone, and other less common rock types that represent three geological types: igneous, sedimentary, and metamorphic. Geotechnical investigation and rock sampling were performed to obtain standard rock cores with a diameter to height ratio of 1:2 for laboratory testing. A series of uniaxial and triaxial compression, porosity, and specific gravity tests were conducted on these samples, and the results in terms of Mohr-Coulomb failure parameters, Hoek and Brown failure parameters, and elastic properties were analyzed in this study. Findings of this research includes conducting regression analysis for the dataset in order to establish prediction equations that relate bedrock strength and deformation properties and the failure behavior of bedrocks depending upon rock geology and other contributing factors. Finally, recommendations were provided based on the experimental results that will facilitate the design and construction of transportation infrastructure in Wyoming

Augmentation therapy with alpha 1-antitrypsin: present and future of production, formulation, and delivery

Alpha 1-antitrypsin is one of the first protein therapeutics introduced on the market – more than 30 years ago – and, to date, it is indicated only for the treatment of the severe forms of a genetic condition known as alpha-1 antitrypsin deficiency. The only approved preparations are derived from plasma, posing potential problems associated with its limited supply and high processing costs. Moreover, augmentation therapy with alpha 1-antitrypsin is still limited to intravenous infusions, a cumbersome regimen for patients. Here, we review the recent literature on its possible future developments, focusing on i) the recombinant alternatives to the plasma-derived protein, ii) novel formulations, and iii) novel administration routes. Regulatory issues and the still unclear noncanonical functions of alpha 1-antitrypsin – possibly associated with the glycosylation pattern found only in the plasma-derived protein – have hindered the introduction of new products. However, potentially new therapeutic indications other than the treatment of alpha-1 antitrypsin deficiency might open the way to new sources and new formulations

Improving Design and Construction of Transportation Infrastructure through Bedrock Characterization [Research Brief]

This study focuses on understanding the mechanical and deformation behaviors of Wyoming bedrocks to improve the design and construction of transportation infrastructure in the state. Fifty-six samples were tested under different confining stresses. Test rock samples are mostly sandstone (30%), siltstone (23%), shale (14%), and others (33%). Triaxial and uniaxial compression tests are conducted using GCTS RTR-1500 rapid triaxial rock testing equipment on hard rocks and GeoJac triaxial equipment on soft and soil-like rocks. Physical properties such as moisture content, porosity, and specific gravity of tested specimens are measured before compression testing. Laboratory compressive tests are performed to measure the stress and strain of each rock specimen, and elastic properties are determined from the linear stress-strain relationship. Shear strength parameters, such as cohesion and friction angle, are also determined. Tensile strength and material constant for each rock sample were also determined

Malware Detection Using Deep Learning and Correlation-Based Feature Selection

Malware is one of the most frequent cyberattacks, with its prevalence growing daily across the network. Malware traffic is always asymmetrical compared to benign traffic, which is always symmetrical. Fortunately, there are many artificial intelligence techniques that can be used to detect malware and distinguish it from normal activities. However, the problem of dealing with large and high-dimensional data has not been addressed enough. In this paper, a high-performance malware detection system using deep learning and feature selection methodologies is introduced. Two different malware datasets are used to detect malware and differentiate it from benign activities. The datasets are preprocessed, and then correlation-based feature selection is applied to produce different feature-selected datasets. The dense and LSTM-based deep learning models are then trained using these different versions of feature-selected datasets. The trained models are then evaluated using many performance metrics (accuracy, precision, recall, and F1-score). The results indicate that some feature-selected scenarios preserve almost the same original dataset performance. The different nature of the used datasets shows different levels of performance changes. For the first dataset, the feature reduction ratios range from 18.18% to 42.42%, with performance degradation of 0.07% to 5.84%, respectively. The second dataset reduction rate is between 81.77% and 93.5%, with performance degradation of 3.79% and 9.44%, respectively