Background
We recently adapted a guideline for Community-Acquired Pneumonia (CAP) in children to the Egyptian health system. Adaptation of evidence-based clinical practice guidelines to the local ...healthcare context is a valid alternative to de novo development that can upgrade their application without enforcing a major burden on resources. The objective of this manuscript is to elucidate diagnosis, treatment, and prevention of CAP as well as methods used for the adaptation process to produce the 1st National Guideline for Community-Acquired Pneumonia in children in Egypt using Adapted ADAPTE method. The full process was described extensively with all three phases of set up, adaptation, and finalization. An adaptation group and an external review including clinical content experts and methodologists conducted the process.
Results
The authors adapted 10 principal categories of recommendations from three source Clinical Practice Guidelines. Recommendations incorporate; common clinical manifestations, indications for hospitalization and intensive care unit admission, indications for laboratory investigations and radiology in diagnosis, choice of empiric antibiotic therapy in the outpatient and hospitalized children with non-complicated CAP and the duration of therapy, the role of influenza antiviral therapy, follow-up anticipated response to therapy, management of non-responding pneumonia, criteria of safe discharge, and prevention of CAP. Many tools were gathered and established to improve implement ability containing two clinical algorithms for management of non-complicated CAP and for non-responding pneumonia in children, pathway for assessment of severity of CAP in primary care facilities, medication tables, simplified Arabic patient information, PowerPoint slide presentation lecture for management of CAP, and online resources.
Conclusion
The final clinical guideline supports pediatricians and related healthcare workers with evidence-based applicable guidance for managing community-acquired pneumonia in Egypt. This work demonstrated the efficiency of Adapted ADAPTE and highlighted the importance of a cooperative clinical and methodological professional group for adaptation of national guidelines.
Encrypted traffic is an essential part of maintaining the security and privacy of data transmission. It plays an important role in keeping our networks secure by preventing attackers from ...intercepting confidential information, which they may access without authorization; However, its effectiveness relies heavily on accurate classification techniques being applied correctly, so we can differentiate between legitimate users' activities versus those attempting malicious activity within the networks’ boundaries. Encrypted network traffic is becoming increasingly common in modern communication systems, presenting a challenge for effective network management and security. To address this challenge, machine learning models have been employed to classify encrypted traffic but with limited success due to the lack of clear visibility into packet contents and an inability to inspect their content. For the sake of tackling this issue, more effective research has begun on developing machine learning models for classifying encrypted payloads without relying on inspecting their contents directly. This research will investigate how features like packet length, time stamps or transport layer security (TLS) and encrypted payload information can be used as input features when attempting classification tasks, instead of analyzing unencrypted content directly from packets themselves which would otherwise be impossible given the current technology constraints. The evaluation process will focus on assessing different model architectures, as well as feature selection techniques that yield improved results over the existing approaches. In this paper, we proposed three approaches to identify encrypted traffic and classify different applications such as browsing, VOIP, file transfer and video streaming. The first two techniques consist of two stages: the first stage is either a neural network or a bi-directional LSTM, and the second stage is a selection of different classification techniques, namely Random Forest, Support vector machine, Linear regression, and K-nearest neighbor. The final result is achieved using an ensemble voting technique. As for the third technique, the network packets are grouped together by Source IP, destination IP and session time before feeding them into three different combinations of LSTM networks; either coupled with convolution 1D or 2D layers, or without. Like the first two techniques, the final result is achieved by means of ensemble voting. Through extensive comparison between the three approaches, The first approach yielded the highest accuracy. However, the performance of the second and third techniques in terms of time complexity was superior. The achieved accuracies were 96.8%, 95.2% and 96.5% for the proposed techniques, respectively.
Software defined networks are an emerging category of networks in which the data plane and control plane are separated. This separation of planes opens the door for designing sophisticated routing ...algorithms that would overwhelm the computing power of traditional networking nodes. In this paper, we consider the possibility of introducing node trust into the routing problem. There are many ways for measuring node trust. However, in this paper, we focus on denial of service attacks. We develop a hybrid method for detecting denial of service attacks and incorporate this information in routing decisions so that nodes that are part of a botnet can be quickly identified and excluded from the network. The proposed method is flexible enough to allow nodes that have been suspected of participating in a denial of service attack to be “rehabilitated” if they cease their malicious behavior. The technique is also able to detect the start of a second attack while another one is on-going. Our results indicate that the proposed method for detecting denial of service attacks performs better than non-hybrid techniques.
A Cloud-based Malware Detection Framework Ahmed, Eman; Sorrour, Amin A.; Sobh, Mohamed A. ...
International journal of interactive mobile technologies,
04/2017, Letnik:
11, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Malwares are increasing rapidly. The nature of distribution and effects of malwares attacking several applications requires a real-time response. Therefore, a high performance detection platform is ...required. In this paper, Hadoop is utilized to perform static binary search and detection for malwares and viruses in portable executable files deployed mainly on the cloud. The paper presents an approach used to map the portable executable files to Hadoop compatible files. The Boyer–Moore-Horspool Search algorithm is modified to benefit from the distribution of Hadoop. The performance of the proposed model is evaluated using a standard virus database and the system is found to outperform similar platforms.
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