| 1. |
- Mishra, Nivedita, et al.
(författare)
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Memcached : An Experimental Study of DDoS Attacks for the Wellbeing of IoT Applications
- 2021
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:23
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Tidskriftsartikel (refereegranskat)abstract
- Distributed denial‐of‐service (DDoS) attacks are significant threats to the cyber world because of their potential to quickly bring down victims. Memcached vulnerabilities have been targeted by attackers using DDoS amplification attacks. GitHub and Arbor Networks were the victims of Memcached DDoS attacks with 1.3 Tbps and 1.8 Tbps attack strengths, respectively. The bandwidth amplification factor of nearly 50,000 makes Memcached the deadliest DDoS attack vector to date. In recent times, fellow researchers have made specific efforts to analyze and evaluate Memcached vulnerabilities; however, the solutions provided for security are based on best practices by users and service providers. This study is the first attempt at modifying the architecture of Memcached servers in the context of improving security against DDoS attacks. This study discusses the Memcached protocol, the vulnerabilities associated with it, the future challenges for different IoT applications associated with caches, and the solutions for detecting Memcached DDoS attacks. The proposed solution is a novel identification‐pattern mechanism using a threshold scheme for detecting volume‐based DDoS attacks. In the undertaken study, the solution acts as a pre‐emptive measure for detecting DDoS attacks while maintaining low latency and high throughput.
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| 2. |
- Rogers, Simon N., et al.
(författare)
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Variations in concerns reported on the patient concerns inventory in patients with head and neck cancer from different health settings across the world
- 2020
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Ingår i: Head and Neck. - : Wiley. - 1043-3074 .- 1097-0347. ; 42:3, s. 498-512
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Tidskriftsartikel (refereegranskat)abstract
- Background The aim was to collate and contrast patient concerns from a range of different head and neck cancer follow-up clinics around the world. Also, we sought to explore the relationship, if any, between responses to the patient concerns inventory (PCI) and overall quality of life (QOL). Methods Nineteen units participated with intention of including 100 patients per site as close to a consecutive series as possible in order to minimize selection bias. Results There were 2136 patients with a median total number of PCI items selected of 5 (2-10). "Fear of the cancer returning" (39%) and "dry mouth" (37%) were most common. Twenty-five percent (524) reported less than good QOL. Conclusion There was considerable variation between units in the number of items selected and in overall QOL, even after allowing for case-mix variables. There was a strong progressive association between the number of PCI items and QOL.
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| 3. |
- Shah, Apeksha, et al.
(författare)
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Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk
- 2021
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Ingår i: Frontiers In Public Health. - : Frontiers Media S.A.. - 2296-2565. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Cardiovascular disease (CVD) is considered to be one of the most epidemic diseases in the world today. Predicting CVDs, such as cardiac arrest, is a difficult task in the area of healthcare. The healthcare industry has a vast collection of datasets for analysis and prediction purposes. Somehow, the predictions made on these publicly available datasets may be erroneous. To make the prediction accurate, real-time data need to be collected. This study collected real-time data using sensors and stored it on a cloud computing platform, such as Google Firebase. The acquired data is then classified using six machine-learning algorithms: Artificial Neural Network (ANN), Random Forest Classifier (RFC), Gradient Boost Extreme Gradient Boosting (XGBoost) classifier, Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT). Furthermore, we have presented two novel gender-based risk classification and age-wise risk classification approach in the undertaken study. The presented approaches have used Kaplan-Meier and Cox regression survival analysis methodologies for risk detection and classification. The presented approaches also assist health experts in identifying the risk probability risk and the 10-year risk score prediction. The proposed system is an economical alternative to the existing system due to its low cost. The outcome obtained shows an enhanced level of performance with an overall accuracy of 98% using DT on our collected dataset for cardiac risk prediction. We also introduced two risk classification models for gender- and age-wise people to detect their survival probability. The outcome of the proposed model shows accurate probability in both classes.
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