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Until the development of HTTP request smuggling in 2005, individual HTTP requests were considered as independent entities and could not be split or merged. This is a security problem caused by inconsistent content length interpretation approach between web servers, or the web server is not fully implemented in accordance with the RFC standard. It is especially dangerous for web services with complex web architectures. It can route the victims to receive malicious responses, amplify the impact of certain low-threat vulnerabilities, steal user credentials, or bypass network devices’ defenses. However, since its concept and implementation are quite difficult to overcome, it is often ignored by many network administrators, making users who browse such websites vulnerable to the HTTP request smuggling attacks. This paper proposes a general solution to deal with various HTTP request smuggling attacks. A reverse proxy implemented by Flask validates and cleans dubious HTTP requests from the client side and ensures that the original requests comply with RFC standards. Therefore, the website administrators no longer need to configure complicated network settings or customize some open-source project codes to resist or minimize the risk of the HTTP request smuggling attacks. A series of experiments demonstrate that this method is effective and practical.

Utilizing supervised machine learning algorithms to develop a surveillance and response system based on symptoms of diarrhoea, contingent on the Support Vector Machine (SVM) to predict the probable disease using labelled data. Diarrhoea is amongst the top ten diseases which kill. A prototype system is developed based on the SVM algorithm. The prototype system takes six patient symptoms that which is input, from the user and the output result becomes the prognosis which may likely occur based solely on the given symptoms. Two other supervised learning models have been utilized in the prediction process, Random Forest Model (RFC) and Naïve Bayes Model (NB). Furthermore, a visualization on google maps (my maps) on the area in which a diarrhoea outbreak would likely occur. The constituency and the region of the patient will be used to place a pin on my maps, giving a visualization on the map, with a mapping structure this allows for a vivid demonstration of how diarrhoea is spreading in Eswatini. SVM received an average of 100% accuracy. The other two supervised learning models, random forest model and naïve Bayes model received 97.62% average accuracy on the same dataset. It shows that the SVM does well in data classification and with a small dataset.

In recent years, since edge computing has become more and more popular, its security issues have become apparent and have received unprecedented attention. Thus, the current research concentrates on security not only regarding devices such as PCs, smartphones, tablets, and IoTs, but also the automobile industry. However, since attack vectors have become more sophisticated than ever, we cannot just protect the zone above the system software layer in a certain operating system, such as Linux, for example. In addition, the challenges in IoT devices, such as power consumption, performance efficiency, and authentication management, still need to be solved. Since most IoT devices are controlled remotely, the security regarding system maintenance and upgrades has become a big issue. Therefore, a mechanism that can maintain IoT devices within a trusted environment based on localhost or over-the-air (OTA) will be a viable solution. We propose a mechanism called STBEAT, integrating an open-source project with ARM TrustZone to solve the challenges of upgrading the IoT system and updating system files more safely. This paper focuses on the ARMv7 architecture and utilizes the security stack from TrustZone to OP-TEE under the STM32 board package, and finally obtains the security key from the trusted application, which is used to conduct the cryptographic operations and then install the newer image on the MMC interface. To sum up, we propose a novel software update strategy and integrated ARM TrustZone security extension to beef up the embedded ecosystem.

Recently, as more and more disasters caused by malware have been reported worldwide, people started to pay more attention to malware detection to prevent malicious attacks in advance. According to the diversity of the software platforms that people use, the malware also varies pretty much, for example: Xcode Ghost on iOS apps, FakePlayer on Android apps, and WannaCrypt on PC. Moreover, most of the time people ignore the potential security threats around us while surfing the internet, processing files or even reading email. The Portable Document Format (PDF) file, one of the most commonly used file types in the world, can be used to store texts, images, multimedia contents, and even scripts. However, with the increasing popularity and demands of PDF files, only a small fraction of people know how easy it could be to conceal malware in normal PDF files. In this paper, we propose a novel technique combining Malware Visualization and Image Classification to detect PDF files and identify which ones might be malicious. By extracting data from PDF files and traversing each object within, we can obtain the holistic tree-like structure of PDF files. Furthermore, according to the signature of the objects in the files, we assign different colors obtained from SimHash to generate RGB images. Lastly, our proposed model trained by the VGG19 with CNN architecture achieved up to 0.973 accuracy and 0.975 F1-score to distinguish malicious PDF files, which is viable for personal, or enterprise-wide use and easy to implement.

A three-party encrypted key exchange (3PEKE) protocol for telecare medicine information systems (TMISs) enables two communicating parties, such as patients, doctors, nurses and health visitors, sharing a long-lived secret only with a trusted third party- Medical Center Server (MCS) to exchange confidential and authenticated Electronic Medical Records (EMRs) and Electronic Health Records (EHRs) with another party over an insecure network. Recently, Lee et al. presented an improved 3PEKE protocol to solve the weaknesses of previous protocols. However, this study states that Lee et al.'s improved 3PEKE protocol still has some security faults such that their protocol cannot execute correctly and fails to resist password guessing attacks. This study also develops an enhanced protocol which is based on Lee et al.'s improved 3PEKE protocol. Additionally, the enhanced protocol protects the user's password by using a one-time key shared with the MCS, eliminates the redundant computations, and rearranges the messages. Compared with related protocols, the enhanced protocol not only has higher security, but also increases efficiency in computation and transmission.