Alert correlation systems attempt to discover the relations among alerts produced by one or more intrusion detection systems to determine the ATTACK SCENARIOs and their main motivations. In this paper a new IDS alert correlation method is proposed that can be used to detect ATTACK SCENARIOs in real-time.The proposed method is based on a causal approach due to the strength of causal methods in practice. To provide a picture of the current intrusive activity on the network, we need a real-time alert correlation. Most causal methods can be deployed offline but not in real-time due to time and memory limitations. In the proposed method, the knowledge base of the ATTACK patterns is represented in a graph model called the Causal Relations Graph. In the offline mode, we construct Queue trees related to alerts' probable correlations. In the real-time mode, for each received alert, we can find its correlations with previously received alerts by performing a search only in the corresponding tree.Therefore, the processing time of each alert decreases significantly. In addition, the proposed method is immune to deliberately slowed ATTACKs. To verify the proposed method, it was implemented and tested using DARPA2000 dataset.Experimental results show the correctness of the proposed alert correlation and its efficiency with respect to the running time.

Background: Globally, cardiovascular disease (CVD) is the number one cause of mortality. Objectives: This study aimed to provide policies for the management of CVD by focusing on the reduction of myocardial infarction (MI) mortality in Iran. Methods: The sequential mixed methods design will be employed to predict the prevalence of MI in Iran in the next 10 years. This study consists of five phases. In the first phase, the risk factors of cardiovascular disease will be investigated using a systematic review. In the second phase, the uncertainty and impact of those factors will be evaluated by the experts. Moreover, the impact/uncertainty grid will be used to identify the most important drivers and critical uncertainties. In the third phase, the cross-impact matrix, the SCENARIO logic and the SCENARIOs will be developed. Once the SCENARIO logic is established, details can be added to the SCENARIOs. The next phase consists of statistical estimations of the rate of mortality due to heart ATTACK using artificial neural networks. Finally, the policies will be developed based on the opinions of the panel of experts. The initial results will be published in mid-2021. Results: This future study will develop policies to prevent from MI with SCENARIO-based and modeling approaches. The findings can benefit healthcare professionals and policymakers by enhancing the management of MI patients. Conclusion: Specific policy recommendations will help policymakers to make evidence-based decisions, re-design structures and processes of healthcare interventions, to decrease the MI mortality.

Each ATTACK graph represents a collection of possible ATTACK SCENARIOs in a computer network. In this paper, we use weighted ATTACK graphs (WAGs) for vulnerability assessment of computer networks. In these directed graphs, a weight is assigned to each exploit by the security analyst. The weight of an exploit is proportionate to the cost required to prevent that exploit. The aim of analyzing a weighted ATTACK graph is to find a critical set of exploits such that the sum of their weights is minimum and by preventing them no ATTACK SCENARIO is possible. In this paper, we propose a greedy algorithm, a genetic algorithm with a greedy mutation operator, and a genetic algorithm with a dynamic fitness function for analyzing the weighted ATTACK graphs. The proposed algorithms are used to analyze a sample weighted ATTACK graph and several randomly generated large-scale weighted ATTACK graphs. The results of experiments show that the proposed genetic algorithms outperform the greedy algorithm and find a critical set of exploits with less total weight. Finally, we compare the performance of the second genetic algorithm with an approximation algorithm for analyzing several randomly generated large-scale simple ATTACK graphs. The results of experiments show that our proposed genetic algorithm has better performance than the approximation algorithm and finds a critical set of exploits with less cardinality.

Background: Previous literatures have shown a transient ischemic ATTACK (TIA) mimic rate of 9-31%. We aimed to ascertain the proportion of stroke mimics amongst suspected TIA patients.Methods: A prospective observational study was performed in Ghaem Hospital, Mashhad, Iran during 2012-2013. Consecutive TIA patients were identified in a stroke center.The initial diagnosis of TIA was made by the resident of neurology and final diagnosis of true TIA versus TIA mimics was made after 3 months follow-up by stroke subspecialist.Results: A total of 310 patients were assessed during a 3-month period of which 182 (58.7%) subjects were male and 128 (41.3%) were female. Ten percent of the patients was categorized as a TIA mimic. The presence of hypertension, aphasia, duration of symptoms, and increased age was the strongest predictor of a true TIA. Migraine was the most common etiology of stroke mimic in our study.Conclusion: It seems that many signs and symptoms have low diagnostic usefulness for discrimination of true TIA from non-cerebrovascular events and predictive usefulness of any sign or symptom should be interpreted by a stroke neurologist.

Cooperative Spectrum Sensing (CSS) is an effective approach to overcome the impact of multi-path fading and shadowing issues. The reliability of CSS can be severely degraded under Byzantine ATTACK, which may be caused by either malfunctioning sensing terminals or malicious nodes. Almost, the previous studies have not analyzed and considered the ATTACK in their models. The present study introduces a new issue named ATTACK-aware CSS where the objective is to analyze the occurred ATTACK against CR network to ameliorate the performance of data fusion schemes. The novelty includes the modification of Weighted Sequential Probability Ratio Test (WSPRT) algorithm which resulted in ATTACK-Aware WSPRT (A2WSPRT). The findings indicated considerable reduction in cooperation overhead and enhancement in correct sensing ratio, especially in severe ATTACKs.

Statins are commonly used drugs in the treatment of hyperlipidemia (HL), despite some undesirable side effects. These range from mild symptoms such as myopathy, muscle weakness and myalgia to severe muscle weakness associated with chronic myopathy and acute renal failure (ARF) as a result of rhabdomyolysis. The most serious and deadly side effect of statins is rhabdomyolysis. The case presented here is of a patient with rhabdomyolysis due to treatment with the antihyperlipidemic drug, atorvastatin.

In the present time, web applications are growing constantly in the whole society with the development of communication technology. Since the utilization of WWW (World Wide Web) expanded and increased since it provides many services, such as sharing data, staying connected, and other services. As a consequence, these numerous numbers of web application users are susceptible to cybersecurity breaches to steal sensitive information or crash the users' systems, etc. Particularly, the most common vulnerability today in web applications is the Cross-Site Scripting (XSS) ATTACK. Furthermore, online cyber ATTACKs utilizing cross-site scripting were responsible for 40% of the ATTACK instances that struck enterprises in North America and Europe in 2019. Therefore, cross-site scripting is a form of an injection that targets both vulnerable and non-vulnerable websites, for the injection of malicious scripts. Cross-site scripting XSS operates by directing users to a vulnerable website that contains malicious JavaScript. Then, when malicious code runs in a victim's browser, the ATTACKer has complete control over how they interact with the application. To protect the website or prevent the XSS, must know the application complexity and the way it handles data must be known so it could be controlled by the user. However, Detecting XSS e ectively is still a work in progress, and XSS is considered a gateway for various ATTACKs. However, in this paper, we will introduce the XSS ATTACK and the forms of XSS as a review paper. In addition, the methods and techniques that help to detect cross-site scripting (XSS) ATTACKs.

In cloud computing, multiple users can share the same physical machine that can potentially leak secret information, in particular when the memory de-duplication is enabled. Flush+Reload ATTACK is a cache-based ATTACK that makes use of resource sharing. T-table implementation of AES is commonly used in the crypto libraries like OpenSSL. Several Flush+Reload ATTACKs on T-table implementation of AES have been proposed in the literature which requires a notable number of encryptions. In this paper, we present a technique to enhance the Flush+Reload ATTACK on AES in the ciphertext-only SCENARIO by significantly reducing the number of needed encryptions in both native and cross-VM setups. In this paper, we focus on finding the wrong key candidates and keep the right key by considering only the cache miss event. Our ATTACK is faster than previous Flush+Reload ATTACKs. In particular, our method can speed-up the Flush+Reload ATTACK in cross-VM environment significantly. To verify the theoretical model, we implemented the proposed ATTACK.

Development of Internet network allows voice communication by Voice over Internet Protocol (VOIP). Session Initiation Protocol (SIP) is the most important signaling protocols in this network. This paper related to Denial of Service (DOS) ATTACKs; specifically INVITE flooding ATTACKs on SIP protocol. In this kind of ATTACKs, an ATTACKer disrupts Server network service by sending successive invite packets. In this paper, we explored modes of emergence INVITE flooding ATTACK and the effects of this ATTACK on SIP server. Results of this experiment indicate that after increasing ATTACK rate, server CPU usage consumption surges. In addition, high CPU utilization leads to rise in the number of transmitted duplicate packets and reduced successful session. Moreover, we demonstrated that faster ATTACK detection can be obtained by replacing Jeffrey distance instead of Hellinger distance.