# Framework for Malware Triggering Using Steganography
## 5 Discussion  
The reference studies in this issue are few. Therefore, we were trying to talk about a different track and successfully carried out this kind of attack. We noticed a real lack of studies on offensive methods. We did not find research illustrating a targeted attack using geolocation information. Most of them are indiscriminate and not targeted attacks. In [11], they used an external extractor program to determine the target and trigger the stego-image. In our work, our attack happens when we reach the target location. Furthermore, it occurs on the same web page just by opening the image without user interaction. We have concealed the trigger conditions by embedding them in the image. The suggested scheme's two primary components are the geolocation condition (trigger) and the stego-image. It was established that there were variations between the geolocation services offered by W3C, IP, and WPS. As shown in Figure 5, the comparison analysis of the W3C geolocation method was performed on the four devices identified earlier, using different browsers and two access networks. The result shows that the accuracy is much better for DSL than for the data SIM card. iPhone browsers have the most accurate location results for devices, with an average accuracy of fewer than 20 m. In windows, the Firefox browser had the most accurate location results, with an average accuracy of 3.95 m. In contrast, the Google Chrome browser provided the worst value and was not acceptable. In a Mac laptop, we found that Google Chrome had greater accuracy, with an average accuracy of 7.7 m. Microsoft Edge had the worst performance compared to the other browsers, with an average accuracy of 43.85 m. In the iPhone, the Firefox browser had the most accurate location results, with an average accuracy of 12.15 m. Finally, in Huawei, we find that Microsoft Edge had greater accuracy than Chrome, with an average accuracy of 20.2 m, while Chrome had an average accuracy of 151.2 m.  
We evaluated location accuracy for eight IP services databases for IP geolocation, as shown in Figure 6. According to the findings, the databases achieved perfect accuracy at the country level. Furthermore, we noticed that all services were correct when connected to a DSL network, except for the Skyhook Hyperlocal IP. However, when the device is connected to a SIM card wireless network, findings show that most databases produced incorrect results at the region and city levels. The best database was Google, with an accuracy of 5 km. On the other hand, when the device is connected to a DSL wireless network, most databases produce valid results at the district and city level except for the Skyhook database, which returned coordinates for another city. The best database again was Google, with an accuracy of 2 km. Figure 14: Malware detection in an image file. Figure 13: Malware detection in a JavaScript file. In Figure 7, we see the comparison analysis of the WPS in Windows and Mac. When connected to a SIM card wireless network, Google achieved better results, while Skyhook achieved better results than Google when connected to a DSL-type network. Comparing Google geolocation API and Skyhook achieved close mean accuracy values in Windows. On the other hand, they both demonstrated poor performance when using a Mac compared to a Windows device. Secondly, we have the stego-image analysis. It is difficult for the naked eye to distinguish between the original image, an encoded image, and a polyglot image, as shown in Figure 9.  
Finally, as shown in Figure 10, we successfully demonstrated the execution of the exploit for the browser in a specific city by using a single image, thus limiting exposure to detection engines and bypassing detection altogether. We successfully combined the W3C geolocation API and Google IP geolocation techniques to develop a stegosploit attack. We carried out our attack on color and grayscale images of PNG format type.