No abstract

No abstract

At the moment, the best and only way to reduce the spread of coronavirus disease 2019 is by limiting close contact with others. Respecting social distance, infection is less probable. Since this is a new lifestyle for everyone, it’s hard to be distance all the times. People forget to keep distance or they are not taking seriously the actual situation. That’s why in this paper, we propose a smart surveillance solution. Our test prototype ensures the respect of social distancing by detecting persons, calculating distances between them and generating loud vocal alerts. The smart surveillance prototype is based on Raspberry Pi and Camera Pi. Then, we make a comparison study of object detection pretrained models. SSD-MobileNet gives the most satisfying result using Raspberry pi with limited computing resources. Despite that implementing CNN based model on the Raspberry Pi is such a challenging work, we reach a value of 1,1 FPS on real-time object detection and distance analysis system.

No abstract

Objective: Newborn malware increase significantly in recent years, becoming more dangerous for many applications. So, researchers are focusing more on solutions that serve the defense of new malwares trends and variance, especially zero-day malware attacks. The prime goal of our proposition is to reach a high security level by defending against malware attacks effectively using advanced techniques. Methods: In this paper, we propose an Intelligent Cybersecurity Framework specialized on malware attacks in a layered architecture. After receiving the unknown malware, the Framework Core layer use malware visualization technique to process unknown samples of the malicious software. Then, we classify malware samples into their families using: K-Nearest Neighbor, Decision Tree and Random Forest algorithms. Classification results are given in the last layer, and based on a Malware Behavior Database we are able to warn users by giving them a detail report on the malicious behavior of the given malware family. The proposed Intelligent Cybersecurity Framework is implemented in a graphic user interface easy to use. Results: Comparing machine learning classifiers, Random Forest algorithm gives best results in the classification task with a precision of 97,6%. Conclusion: However, we need to take into account results of the other classifiers for more reliability. Finally, obtained results are as efficient as fast that meets cybersecurity frameworks general requirements.