Revolutionizing SIEM Security: An Innovative Correlation Engine Design for Multi-Layered Attack Detection.

Advances in connectivity, communication, computation, and algorithms are driving a revolution that will bring economic and social benefits through smart technologies of the Industry 4.0 era. At the same time, attackers are targeting this expanded cyberspace to exploit it. Therefore, many cyberattacks are reported each year at an increasing rate. Traditional security devices such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), anti-viruses, and the like, often cannot detect sophisticated cyberattacks. The security information and event management (SIEM) system has proven to be a very effective security tool for detecting and mitigating such cyberattacks. A SIEM system provides a holistic view of the security status of a corporate network by analyzing log data from various network devices. The correlation engine is the most important module of the SIEM system. In this study, we propose the optimized correlator (OC), a novel correlation engine that replaces the traditional regex matching sub-module with a novel high-performance multiple regex matching library called "Hyperscan" for parallel log data scanning to improve the performance of the SIEM system. Log files of 102 MB, 256 MB, 512 MB, and 1024 MB, generated from log data received from various devices in the network, are input into the OC and simple event correlator (SEC) for applying correlation rules. The results indicate that OC is 21 times faster than SEC in real-time response and 2.5 times more efficient in execution time. Furthermore, OC can detect multi-layered attacks successfully.

Multi-Layered Filtration Framework for Efficient Detection of Network Attacks Using Machine Learning.

The advancements and reliance on digital data necessitates dependence on information technology. The growing amount of digital data and their availability over the Internet have given rise to the problem of information security. With the increase in connectivity among devices and networks, maintaining the information security of an asset has now become essential for an organization. Intrusion detection systems (IDS) are widely used in networks for protection against different network attacks. Several machine-learning-based techniques have been used among researchers for the implementation of anomaly-based IDS (AIDS). In the past, the focus primarily remained on the improvement of the accuracy of the system. Efficiency with respect to time is an important aspect of an IDS, which most of the research has thus far somewhat overlooked. For this purpose, we propose a multi-layered filtration framework (MLFF) for feature reduction using a statistical approach. The proposed framework helps reduce the detection time without affecting the accuracy. We use the CIC-IDS2017 dataset for experiments. The proposed framework contains three filters and is connected in sequential order. The accuracy, precision, recall and F1 score are calculated against the selected machine learning models. In addition, the training time and the detection time are also calculated because these parameters are considered important in measuring the performance of a detection system. Generally, decision tree models, random forest methods, and artificial neural networks show better results in the detection of network attacks with minimum detection time.

Attention based automated radiology report generation using CNN and LSTM.

The automated generation of radiology reports provides X-rays and has tremendous potential to enhance the clinical diagnosis of diseases in patients. A new research direction is gaining increasing attention that involves the use of hybrid approaches based on natural language processing and computer vision techniques to create auto medical report generation systems. The auto report generator, producing radiology reports, will significantly reduce the burden on doctors and assist them in writing manual reports. Because the sensitivity of chest X-ray (CXR) findings provided by existing techniques not adequately accurate, producing comprehensive explanations for medical photographs remains a difficult task. A novel approach to address this issue was proposed, based on the continuous integration of convolutional neural networks and long short-term memory for detecting diseases, followed by the attention mechanism for sequence generation based on these diseases. Experimental results obtained by using the Indiana University CXR and MIMIC-CXR datasets showed that the proposed model attained the current state-of-the-art efficiency as opposed to other solutions of the baseline. BLEU-1, BLEU-2, BLEU-3, and BLEU-4 were used as the evaluation metrics.

Relationship Between Clinical Features and Body Mass Index Among Hypertensive Patients: A Cross-Sectional Study.

Objectives Hypertension is strongly related to body mass index (BMI). Obesity has been the single main contributor to hypertension. Furthermore, the clinical manifestations are normally associated with BMI in hypertensive patients. This study aimed to evaluate the relationship between clinical features and BMI among hypertensive patients in both males and females. Methodology A retrospective cross-sectional study was conducted among 296 patients having a self-reported history of hypertension and on anti-hypertensive medication. The study was conducted in the medical outpatient department of a secondary care hospital in Karachi during six months (January 2020 to June 2020). A detailed history was taken from each patient about hypertension-related symptoms, and clinical examination was performed. Blood pressure was measured using a sphygmomanometer with a stethoscope. Results Of the 296 patients, 156 (52.2%) were males and 140 (47.3%) were females; 16 (5.4%) of them were underweight, 91 (30.7%) were normal weight, 129 (43.6%) were overweight, and 60 (20.3%) were obese; in addition, 106 (35.8%) reported edema and 71 (24.0%) reported nausea and so on. As far as the association of clinical features and BMI was concerned, our study results showed that only edema (p=0.017) and nausea (p=0.044) were significantly associated with the BMI of the patients. Patients with edema were more likely to be obese than those without edema (29.2% vs. 15.3%), whereas patients with nausea were more likely to be overweight than those without nausea (57.7% vs. 39.1%). Conclusions Our study showed that among the clinical features, edema and nausea were significantly associated with the BMI of the patients, whereas the relationship with others was insignificantly related to BMI of the patients in both male and female patients.

Cardiovascular Disease & Cancer Risk Among South Asians: Impact of Sociocultural Influences on Lifestyle and Behavior.

A comprehensive literature review revealed cultural beliefs, societal obligations, and gender roles within the South Asian community to be indirect contributors to the health of South Asian immigrants (SAIs). Health professionals need to increase their work with SAI communities to change less beneficial cultural elements such as misconceptions about health and exercise, and lack of communication when using alternative medicines. Community engaged efforts and continuing medical education are both needed to improve the health of the South Asian immigrant population in a culturally appropriate manner.

Effectiveness of Oral Methotrexate Therapy versus Systemic Corticosteroid Therapy in Treatment of Generalised Lichen Planus.

OBJECTIVE: To compare the effectiveness of oral methotrexate versus systemic corticosteroids in treatment of lichen planus. STUDY DESIGN: Randomised controlled trial. PLACE AND DURATION OF STUDY: Department of Dermatology, Lady Reading Hospital, Peshawar from September 2013 to February 2014. METHODOLOGY: Patients with generalised lichen planus involving at least 20% of total body area diagnosed clinically and were randomly allocated into two equal groups by lottery method. Patients in group A were subjected to oral methotrexate 10 mg once weekly for eight weeks with standard monitoring of full blood count and liver and renal function tests. Patients in group B were subjected to oral corticosteroids 40 mg for eight weeks taken daily than tapered according to the protocol. The results were assessed at baseline and at the eighth week after starting the treatment. The responses were analysed by Visual Analogue Scale (VAS) and sorted into four categories: 0-3 = poor response, 4-5 = moderate, 6-7 = good, and >7 excellent response. Chi-square test was applied to compare the efficacy in two groups with significance of less than or equal to 0.05. RESULTS: Group A (methotrexate) had 47 (60%) male patients and 32 (40%) female patients. Group B (oral corticosteroids) had 51 (64%) male patients and 28 (36%) females. Group A 55 (70%) patients had lichen planus in <50% of the body; whereas in group B 53 (67%) patients had lichen planus in <50% of the body. Methotrexate was effective in 63 (80%) patients; whereas, oral corticosteroid was effective in 57 (72%) patients. No remarkable side effects were observed with either agent. CONCLUSION: Methotrexate is more efficacious than systemic corticosteroids, but the effect is not statistically significant.

Explanatory models of coronary heart disease among South Asian immigrants.

OBJECTIVE: This study investigated South Asians' explanatory models (EM) of CHD and compared them to the biomedical model as part of an effort to inform the development of culturally targeted CHD prevention messages. METHODS: We conducted in-depth, semi-structured interviews in English, Hindi and Urdu with 75 respondents from a federally qualified health center and at a community center for South Asian immigrants in Chicago, Illinois. RESULTS: While EMs of CHD included risk factors from the biomedical model, they also included psychosocial and spiritual risk factors. Respondents emphasized that stress causes CHD and suggested that CHD was caused by sudden or inexplicable factors. Few respondents discussed cholesterol, blood pressure, or diabetes as part of CHD prevention. Women and those with lower education had low perceptions of being at-risk for CHD. CONCLUSION: South Asians' EMs of CHD encompassed the biomedical model; however, EMs also included psychosocial and spiritual factors. PRACTICE IMPLICATIONS: Clinicians and health educators should be aware that South Asian individual's EM of CHD may include psychosocial and spiritual factors which can affect CHD prevention behaviors.

Explanatory models of health and disease among South Asian immigrants in Chicago.

To identify concepts of health and disease as part of a study on designing culturally-targeted heart disease prevention messages for South Asians. We conducted qualitative, semi-structured interviews in English, Hindi and Urdu with 75 respondents from a federally qualified health center and at a community center for South Asian immigrants in Chicago, Illinois. Age ranged from 20 to 70 years; 60% were women; 60% held advanced degrees; 70% migrated to the US in the last 10 years; and 60% of the interviews were in Hindi or Urdu. Concepts of health and disease fell into four domains: behavioral, physical, psycho-social and spiritual. Muslim participants consistently evoked spiritual factors such as faith and prayer. Women more frequently included performing home duties and positive affect in their concept of health. Men more frequently cited behavioral factors such as smoking and drinking as the cause of disease. Many South Asians have a holistic conceptualization of health and disease, incorporating spiritual, physical and psycho-social factors. Health promotion strategies aimed at South Asians in the US should take into account this holistic model of health and disease, while also recognizing that variations exist within South Asians, by gender and religion.