Analisis Kinerja Protokol MQTT dan HTTP Pada Akuisisi Data Magnet Berbasis Internet of Things

One of the determinants of the quality of magnetic data is continuous data, so we need a data transmission system that can continuously transmit observational data. In this research, a magnetometer communication system design was carried out with the concept of the Internet of Things (IoT) using the MQTT and HTTP protocol, where measurement data in the form of the x-axis, y-axis, z-axis, horizontal components, and total magnetic field components are displayed on the dashboard in real time and continuously. Testing the performance of sending data is done using the Wireshark, it is known that the MQTT protocol has a better delivery quality compared to the HTPP protocol with an average delay value of 0.0120 seconds, an average value of packet length of 54 bytes and a packet loss value of 0.11%, while the HTTP protocol has an average delay value of 0.0257 seconds, an average packet length value of 268.1 bytes and a packet loss value of 0.5%.

An Efficient Authentication Scheme for Internet of Things

The Internet of Things (IoT) is increasingly affecting human lives in multiple profound ways. “Things” have the ability to communicate, generate, transmit and store data over the network connection. During each communication between “Things”, the data transmitted is potentially vulnerable to malicious attacks, loss, distortions and interruption which impair functionality, system efficiency and user satisfaction. Additionally, inappropriate user controls can cause problems in IoT services, such as granting anonymous users access to personal resources and enable legitimate users to access resources in an illegal manner or preventing legitimate users to access resources in an authorized manner. Therefore, communications between things need to be authenticated, authorized, secured and ensured to have high privacy by applying a strong authentication protocol. The aim of this research is to enhance the authentication protocol, starting by reducing the heavy use of storage in “Things”, and eliminating unnecessary messages during authentication steps, taking into consideration the network security analysis. This research represents a security performance analysis and enhancement authentication for the IoT. The results indicate that the enhanced protocol has a positive effect on minimizing packet length and time performance in authenticating users having once obtained access to the visited location area compared with the other two protocols used for comparative purposes, with 33% increased the proposed protocol performance.

Optimal Packet Camouflage Against Traffic Analysis

Research has proved that supposedly secure encrypted network traffic is actually threatened by privacy and security violations from many aspects. This is mainly due to flow features leaking evidence about user activity and data content. Currently, adversaries can use statistical traffic analysis to create classifiers for network applications and infer users’ sensitive data. In this article, we propose a system that optimally prevents traffic feature leaks. In our first algorithm, we model the packet length probability distribution of the source app to be protected and that of the target app that the source app will resemble. We define a model that mutates the packet lengths of a source app to those lengths from the target app having similar bin probability. This would confuse a classifier by identifying a mutated source app as the target app. In our second obfuscation algorithm, we present an optimized scheme resulting in a trade-off between privacy and complexity overhead. For this reason, we propose a mathematical model for network obfuscation. We formulate analytically the problem of selecting the target app and the length from the target app to mutate to. Then, we propose an algorithm to solve it dynamically. Extensive evaluation of the proposed models, on real app traffic traces, shows significant obfuscation efficiency with relatively acceptable overhead. We were able to reduce a classification accuracy from 91.1% to 0.22% using the first algorithm, with 11.86% padding overhead. The same classification accuracy was reduced to 1.76% with only 0.73% overhead using the second algorithm.

Obfuscated Tor Traffic Identification Based on Sliding Window

Tor is an anonymous communication network used to hide the identities of both parties in communication. Apart from those who want to browse the web anonymously using Tor for a benign purpose, criminals can use Tor for criminal activities. It is recognized that Tor is easily intercepted by the censorship mechanism, so it uses a series of obfuscation mechanisms to avoid censorship, such as Meek, Format-Transforming Encryption (FTE), and Obfs4. In order to detect Tor traffic, we collect three kinds of obfuscated Tor traffic and then use a sliding window to extract 12 features from the stream according to the five-tuple, including the packet length, packet arrival time interval, and the proportion of the number of bytes sent and received. And finally, we use XGBoost, Random Forest, and other machine learning algorithms to identify obfuscated Tor traffic and its types. Our work provides a feasible method for countering obfuscated Tor network, which can identify the three kinds of obfuscated Tor traffic and achieve about 99% precision rate and recall rate.

Experimental Evaluation of the Packet Reception Performance of LoRa

LoRa technology is currently one of the most popular Internet of Things (IoT) technologies. A substantial number of LoRa devices have been applied in a wide variety of real-world scenarios, and developers can adjust the packet reception performance of LoRa through physical layer parameter configuration to meet the requirements. However, since the important details of the relationship between the physical layer parameters and the packet reception performance of LoRa remain unknown, it is a challenge to choose the appropriate parameter configuration to meet the requirements of the scenarios. Moreover, with the increase in application scenarios, the requirements for energy consumption become increasingly high. Therefore, it is also a challenge to know how to configure the parameters to maximize the energy efficiency while maintaining a high data rate. In this work, a complex evaluation experiment on the communication capability under a negative Signal to Noise Ratio is presented, and the specific details of the relationship between physical layer parameters and the packet reception performance of LoRa are clarified. Furthermore, we study the impact of the packet length on the packet reception performance of LoRa, and the experimental results show that when there is a large amount of data to be transmitted, it is better to choose long packets instead of short packets. Finally, considering the influence of physical layer parameters and the packet length on the packet reception performance of LoRa, the optimal parameter combination is explored, so as to propose a transmission scheme with a balanced reliability, delay, and energy consumption. This scheme is the first to consider the physical layer parameters and packet length together to study the communication transmission scheme, which reduces the communication time by 50% compared with the traditional transmission scheme and greatly reduces the energy consumption.