Biblio

Along with the development of the Windows operating system, browser applications to surf the internet are also growing rapidly. The most widely used browsers today are Google Chrome and Mozilla Firefox. Both browsers have a username and password management feature that makes users login to a website easily, but saving usernames and passwords in the browser is quite dangerous because the stored data can be hacked using brute force attacks or read through a program. One way to get a username and password in the browser is to use a program that can read Google Chrome and Mozilla Firefox login data from the computer's internal storage and then show those data. In this study, an attack will be carried out by implementing Rubber Ducky using BadUSB to run the ChromePass and PasswordFox program and the PowerShell script using the Arduino Pro Micro Leonardo device as a USB Password Stealer. The results obtained from this study are the username and password on Google Chrome and Mozilla Firefox successfully obtained when the USB is connected to the target device, the average time of the attack is 14 seconds then sending it to the author's email.

Universal serial bus (USB) devices have widespread use in different computing platforms, including IoT gadgets, but this popularity makes them attractive targets for exploits and being used as an attack vector by malicious software. During recent years, several reports [17] ranked USB-based malware among top 10 popular malware. This security flaw can slow down the increasing penetration rate of IoT devices since most of those devices have USB ports. The research community and industry has tried to address USB security problem by implementing authentication protocols to protect users' private information and also scanning USB's storage space for any malicious software using their own repository of malware signatures, or simply disallowing use of USB devices on desktops. The new generation of USB malware does not hide in storage space, which means they are not detectable by conventional anti-malware. BadUSB is a malware recently introduced by security researchers. BadUSB modifies USB firmware and can attack all the systems which the infected USB is plugged in. The only applicable solution against this new generation of malware is whitelisting. However, generating a unique fingerprint for USB devices is challenging. In this paper, we propose an accurate USB feature based fingerprinting approach which helps us to create a list of trusted USBs as device whitelist. Our solution prevents and detects BadUSB and similar attacks by generating fingerprint from trusted USB devices' features and their primary usage. We verified the uniqueness of our generated fingerprints by analyzing real data which is collected from USB drives used by students in academic computer labs over one year. Our results indicate that our feature based whitelisting approach with an accuracy of 98.5% can identify USB whitelist members.

The USB protocol has become ubiquitous, supporting devices from high-powered computing devices to small embedded devices and control systems. USB's greatest feature, its openness and expandability, is also its weakness, and attacks such as BadUSB exploit the unconstrained functionality afforded to these devices as a vector for compromise. Fundamentally, it is virtually impossible to know whether a USB device is benign or malicious. This work introduces FirmUSB, a USB-specific firmware analysis framework that uses domain knowledge of the USB protocol to examine firmware images and determine the activity that they can produce. Embedded USB devices use microcontrollers that have not been well studied by the binary analysis community, and our work demonstrates how lifters into popular intermediate representations for analysis can be built, as well as the challenges of doing so. We develop targeting algorithms and use domain knowledge to speed up these processes by a factor of 7 compared to unconstrained fully symbolic execution. We also successfully find malicious activity in embedded 8051 firmwares without the use of source code. Finally, we provide insights into the challenges of symbolic analysis on embedded architectures and provide guidance on improving tools to better handle this important class of devices.