This paper proposed a method of allocating additional TXOP duration to replenish the bandwidth used during the retransmission of frames at link layer in audio-video transmission by IEEE 802.11e HCCA. In the proposed scheme, the HC monitors the number of retransmission at link layer and utilize the surplus bandwidth to allocate additional TXOP duration on the basis of number of retransmission performed during the last transmission. The proposed scheme also reduces the polling overhead to gain surplus bandwidth by polling only a station in a polling interval. By simulation, we compare the application-level QoS of the TGe scheme and the proposed scheme. Numerical results show that the proposed scheme can keep the QoS of audio and video high under lossy wireless channel especially when the number of multimedia stations is small. Furthermore, the proposed scheme can admit more traffic flows than the TGe scheme.
This study focus on the implementation of expiry date detection for medicine using RFID in the health care industry. The motivation for doing this research is the process of searching for the expired medicine is a time consuming and lack of security features included in current NFC implementation. Therefore, the objective of this research is to study the RFID technology used for detecting medicine expiry product and to develop a new system that integrated NFC with authentication feature. Moreover, the problem of current data management for medicine still using manual or barcode system that lead to inconsistency, easy duplication and human error. Here, the NFC is chosen, due to smaller distance of signal coverage, since less interference and the time spending for sniffing activity by the hacker can be reduced. The system is developed using C#, SQLite, Visual Studio, NFC Tag and NFC reader (ACR122U-A9). Experiments have shown that the proposed system has produced medicine expiry date system and only authorized person in charge can monitor the medicine. The impact of the proposed system produces safer, greener and easier environment for better medicine data management. The significance of this study gives a medicine expiry date detection system for health care.
As the popularity of mobile devices are on the rise, millions of users are now exposed to mobile malware threats. Malware is known for its ability in causing damage to mobile devices. Attackers often use it as a way to use the resources available and for other cybercriminal benefits such stealing users’ data, credentials and credit card number. Various detection techniques have been introduced in mitigating mobile malware, yet the malware author has its own method to overcome the detection method. This paper presents mobile malware analysis approaches through opcode analysis. Opcode analysis on mobile malware reveals the behaviour of malicious application in the binary level. The comparison made between the numbers of opcode occurrence from a malicious application and benign shows a significance traits. These differences can be used in classifying the malicious and benign mobile application.
Mobile device has become an essential tool among the community across the globe and has turned into a necessity in daily life. An extensive usage of mobile devices for everyday life tasks such as online banking, online shopping and exchanging e-mails has enable mobile devices to become data storage for users. The data stored in these mobile devices can contain sensitive and critical information to the users. Hence, making mobile devices as the prime target for cybercriminal. To date, Android based mobile devices is one of the mobile devices that are dominating the phone market. Moreover, the ease of use and open-source feature has made Android based mobile devices popular. However, the widely used Android mobile devices has encourage malware author to write malicious application. In a short duration of time mobile malware has rapidly evolve and have the capability to bypass signature detection approach which requires a constant signature update to detect mobile malware. To overcome this drawback an anomaly detection approach can be used to mitigate this issue. Yet, using a single classifier in an anomaly detection approach will not improve the classification detection performance. Based on this reason, this research formulates an ensemble classification method of different n-gram system call sequence features to improve the accuracy of mobile malware detection. This research proposes n-number of classifier models for each different n-gram sequence call feature. The probability output of each classifier is then combined to produce a better classification performance which is better compared to a single classifier.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.