Deep reinforcement learning based Evasion Generative Adversarial Network for botnet detection

Botnet detectors based on machine learning are potential targets for adversarial evasion attacks. Several research works employ adversarial training with samples generated from generative adversarial nets (GANs) to make the botnet detectors adept at recognising adversarial evasions. However, the synthetic evasions may not follow the original semantics of the input samples. This paper proposes a novel GAN model leveraged with deep reinforcement learning (DRL) to explore semantic aware samples and simultaneously harden its detection. A DRL agent is used to attack the discriminator of the GAN that acts as a botnet detector. The agent trains the discriminator on the crafted perturbations during the GAN training, which helps the GAN generator converge earlier than the case without DRL. We name this model RELEVAGAN, i.e. [“relieve a GAN” or deep REinforcement Learning-based Evasion Generative Adversarial Network] because, with the help of DRL, it minimises the GAN's job by letting its generator explore the evasion samples within the semantic limits. During the GAN training, the attacks are conducted to adjust the discriminator weights for learning crafted perturbations by the agent. RELEVAGAN does not require adversarial training for the ML classifiers since it can act as an adversarial semantic-aware botnet detection model. The code will be available at https://github.com/rhr407/RELEVAGAN

Assessment of depression and anxiety in adult cancer outpatients: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>The prevalence of anxiety and depressive disorders in cancer patients and its associated factors in Pakistan is not known. There is a need to develop an evidence base to help introduce interventions as untreated depression and anxiety can lead to significant morbidity. We assessed the prevalence of depression and anxiety among adult outpatients with and without cancer as well as the effect of various demographic, clinical and behavioral factors on levels of depression and anxiety in cancer patients.</p> <p>Methods</p> <p>This cross-sectional study was carried out in outpatient departments of Multan Institute of Nuclear Medicine and Radiotherapy and Nishtar Medical College Hospital, Multan. Aga Khan University Anxiety and Depression Scale (AKUADS) was used to define the presence of depression and anxiety in study participants. The sample consisted of 150 diagnosed cancer patients and 268 participants without cancer (control group).</p> <p>Results</p> <p>The mean age of cancer patients was 40.85 years (SD = 16.46) and median illness duration was 5.5 months, while the mean age of the control group was 39.58 years (SD = 11.74). Overall, 66.0% of the cancer patients were found to have depression and anxiety using a cutoff score of 20 on AKUADS. Among the control group, 109 subjects (40.7%) had depression and anxiety. Cancer patients were significantly more likely to suffer from distress compared to the control group (OR = 2.83, 95% CI = 1.89-4.25, P = 0.0001). Performing logistic regression analysis showed that age up to 40 years significantly influenced the prevalence of depression and anxiety in cancer patients. There was no statistically significant difference between gender, marital status, locality, education, income, occupation, physical activity, smoking, cancer site, illness duration and mode of treatment, surgery related to cancer and presence of depression and anxiety. Cancers highly associated with depression and anxiety were gastrointestinal malignancies, chest tumors and breast cancer.</p> <p>Conclusions</p> <p>This study highlights high prevalence rates of depression and anxiety in cancer patients. Younger age was associated with a higher likelihood of meeting criteria for psychological morbidity. The findings support screening patients for symptoms of depression and anxiety as part of standard cancer care and referring those at a higher risk of developing psychological morbidity for appropriate care.</p

Association of Clinicopathological features of Cholecystitis with Helicobacter Pylori Infection in Gall bladders

Background: Helicobacter pylori (H. pylori) have been associated with gastritis, but its presence in other parts of the gastrointestinal system has not been studied much. Few previous studies have identified “H. pylori” in gallbladder and found its association in causing cholecystitis and gallstones, but there is limited data showing a significant association in Pakistan. This study was designed to identify H. pylori microorganism in cholecystitis patients and find its association with the morphological changes seen in the affected gall bladders. Material and Methods: All patients with acute and chronic cholecystitis admitted in Akbar Niazi Teaching Hospital (ANTH) between the ages of 18 and 80 years from January 2017 till March 2019, who underwent cholecystectomy, were included in the study. Gall bladder specimens were sent to Pathology department, ANTH after surgery and were analyzed for the presence of H pylori bacteria using Hematoxylin and Eosin and Giemsa staining. Signs of inflammation, hyperplasia, metaplasia, mucosal atrophy or erosion, lymphoid infiltration, fibrosis, cholesterolosis or any other morphological changes were also noted. Association of H. pylori with cholecystitis and other morphological changes were assessed by Chi Square analysis. P value less than 0.05 was considered statistically significant.Results: Chronic cholecystitis was present in 91% cases and acute cholecystitis in 9%. Other histological findings were Hyperplasia (10%), Metaplasia (15%), Fibrosis (79%), Cholesterolosis (19%) and ulcerations (36%). H pylori was found in 17% of gall bladders and all the cases were of chronic cholecystitis, with 11.7% males and 88% females. Gallstones were present in 76.4% cases and were more common in 41-60 years’ age group (64.7%). Other histological findings seen in H. pylori positive cases were; Hyperplasia in 11.7% cases, Metaplasia in 17.6%, Fibrosis in 94.1%, cholesterolosis in 23.5% and ulcerations in 17.6% cases. Association of H. pylori with gender, cholecystitis, gall stones, histological features and age distribution was non-significant.Conclusion: Although H. pylori infection has been found in cases of chronic cholecystitis and gall stone formation, its association with cholecystitis and other morphological changes could not be proved. Hence, it is uncertain whether H. pylori eradication in patients with gastritis can prevent cholecystitis or gall stones formation

Effect of Zinc on Serum Testosterone Level in Albino Rats

Objective: To study the effect of zinc on serum testosterone levels in Albino rats. Material and Methods: This study was conducted in the Department of Pharmacology and Therapeutics BMSI JPMC Karachi. In this study 60 albino rats were divided into four groups, 15 rats in each group. Group one was control group (normal diet was given to this group), group two was given indomethacin, group three was given zinc and group four was given combination of Zinc and indomethacin. All the drugs were given for 12 weeks. Serum testosterone level was checked at the end of study and finally data was analyzed statistically using SPSS version 18. Results: In group 2, serum testosterone level (3.12±0.63) was low as compared with control (6.26±0.15). In group 3, mean testosterone was high (6.97±0.63) when it was compared with control (6.26±0.15). In group 4, mean testosterone was low (5.15±0.73) but not significant when compared with control (6.26±0.15). Conclusion: Zinc has a protective role on testes and it increases testosterone and fertility.&nbsp

Improving bio aviation fuel yield from biogenic carbon sources through electrolysis assisted chemical looping gasification

The second-generation bio aviation fuel production via Chemical Looping Gasification (CLG) of biomass combined with downstream Fischer-Tropsch synthesis is a possible way to decarbonize the aviation sector. Although CLG has a higher syngas yield and conversion efficiency compared to the conventional gasification processes, the fraction of biogenic carbon which is converted to biofuel is still low (around 28%). To increase carbon utilization and biofuel yield, incorporation of two types of electrolyzers, Polymer Electrolyte Membrane (PEM) and Molten Carbonate Electrolysis Cell (MCEC), for syngas conditioning has been investigated. Full chain process models have been developed using an experimentally validated CLG model in Aspen Plus for Iron sand as an oxygen carrier. Techno-economic parameters were calculated and compared for different cases. The results show that syngas conditioning with sustainable hydrogen from PEM and MCEC electrolyzers results in up to 11.5% higher conversion efficiency and up to 8.1 % higher biogenic carbon efficiencies in comparison to the syngas conditioning with water gas shift reactor. The study shows that the lowest carbon capture rates are found in the configurations with the highest biogenic carbon efficiency which means up to 14% more carbon ends up in FT crude compared to the case with conventional WGS conditioning. Techno-economic analysis indicates that syngas conditioning using PEM and MCEC electrolyzers would result in an increase of the annual profit by a factor of 1.4 and 1.7, respectively, when compared to using only WGS reactors

Analysis of Motivational Theories in Crowdsourcing Using Long Tail Theory: A Systematic Literature Review

Motivational theories have been extensively studied in a wide range of fields, such as medical sciences, business, management, physiology, sociology, and particularly in the natural sciences. These theories are regarded as crucial in motivating online workers to engage in crowdsourcing. Nevertheless, there is a dearth of research on an overarching review of these theories. We performed a systematic literature review of peer-reviewed published studies focusing on motivational theories to identify popular theories and risks associated with nascent theories presented over the last decade in crowdsourcing. Based on a review of 91 papers from the domain of the natural sciences, we identified 35 motivational theories. The long tail theory helped us to identify the contribution of major influencing theories in a crowdsourcing environment. The results justify the long tail theory based on the Pareto principle of 80/20, which underlines the 20% of the popular motivation theories, namely self-determination, expectancy-value, game, gamification, behavior change, and incentive theory, as a cause of 80%. Similarly, we discussed the risks associated with 10 theories presented over the long tail, which have a frequency equal to 2. Understanding the significant impact, approximately 80%, of widely recognized motivational theories and their role in risk identification is crucial. This understanding can assist researchers in optimizing their results by effectively integrating these theories

Op2Vec: An Opcode Embedding Technique and Dataset Design for End-to-End Detection of Android Malware

Android is one of the leading operating systems for smart phones in terms of market share and usage. Unfortunately, it is also an appealing target for attackers to compromise its security through malicious applications. To tackle this issue, domain experts and researchers are trying different techniques to stop such attacks. All the attempts of securing Android platform are somewhat successful. However, existing detection techniques have severe shortcomings, including the cumbersome process of feature engineering. Designing representative features require expert domain knowledge. There is a need for minimizing human experts' intervention by circumventing handcrafted feature engineering. Deep learning could be exploited by extracting deep features automatically. Previous work has shown that operational codes (opcodes) of executables provide key information to be used with deep learning models for detection process of malicious applications. The only challenge is to feed opcodes information to deep learning models. Existing techniques use one-hot encoding to tackle the challenge. However, the one-hot encoding scheme has severe limitations. In this paper, we introduce; (1) a novel technique for opcodes embedding, which we name Op2Vec, (2) based on the learned Op2Vec we have developed a dataset for end-to-end detection of android malware. Introducing the end-to-end Android malware detection technique avoids expert-intensive handcrafted features extraction, and ensures automation. Some of the recent deep learning-based techniques showed significantly improved results when tested with the proposed approach and achieved an average detection accuracy of 97.47%, precision of 0.976 and F1 score of 0.979

Conversion of Waste Marble Powder into a Binding Material

In the marble industry, a lot of marble is wasted in the form of odd blocks of various sizes and slurry consisting of water and micro-fine particles. The slurry on drying converts into powder. Both slurry and powder have adverse effects on the environment. This research is focused on the gainful utilization of waste marble powder (WMP) by converting it into a valuable binding material. For this purpose, WMP and clay were collected, and their physical and chemical properties were determined. A mix of WMP and clay was prepared and burnt at a temperature around 1300 oC. The burnt mix was ground to powder form to get marble cement (MC). The MC was then used in mortar. The compressive and flexural strengths of mortar cubes and prisms were determined. Apart from this, X-ray diffraction (XRD) analysis, thermo-gravimetric analysis (TGA) and scanning electron microscopic (SEM) analysis were also carried out. The chemical composition showed that the MC has 52.5% di-calcium silicate (C2S) and 3.5% tri-calcium silicate (C3S).The  compressive strength of MC mortar after 28 days curing is 6.03 MPa, which is higher than M1 mortar of building code of Pakistan (5 MPa). The compressive strength of MC mortar after one year is 20.67 MPa, which is only 17% less than OPC mortar

Frequency Diversity Array for DOA Estimation

The localization of targets has been presented in this article. DOA (Direction of Arrival) is an important parameter to be determined by radar. The MLE (Maximum Likelihood Estimator) has been widely used to accurately and efficiently estimate the DOAs of multiple targets. The targets at different ranges result in a variation in amplitude of the received signals, so an MLE estimator has to operate at all ranges. For accurate results of DOA, the complex amplitudes of multiple targets should not be much different and also the prior information of Doppler and number of targets is required. In this paper, an approach is proposed which uses the classical 2D algorithm to estimate range, Doppler and number of targets and then FDA (Frequency Diversity Array) is used to focus power in a particular range. As a result, the MLE can get data from a particular range cell where all targets have almost same amplitude and thus MLE can accurately estimate the DOAs of multiple targets. Simulations and results have confirmed the effectiveness of proposed approach