Samy Meftali

Bio: Samy Meftali is an academic researcher from Lille University of Science and Technology. The author has contributed to research in topics: Smart camera & Malware. The author has an hindex of 1, co-authored 3 publications receiving 5 citations.

A Survey of Keylogger and Screenlogger Attacks in the Banking Sector and Countermeasures to Them

Abstract: Keyloggers and screenloggers are one of the active growing threats to user’s confidentiality as they can run in user-space, easily be distributed and upload information to remote servers. They use a wide number of different techniques and may be implemented in many ways. Keyloggers and screenloggers are very largely diverted from their primary and legitimate function to be exploited for malicious purposes compromising the privacy of users, and bank customers notably. This paper presents a survey of keylogger and screenlogger attacks to increase the understanding and awareness of their threat by covering basic concepts related to bank information systems and explaining their functioning, as it presents and discusses an extensive set of plausible countermeasures.

Dataset Construction and Analysis of Screenshot Malware

Abstract: Among the various types of spyware, screenloggers are distinguished by their ability to capture screenshots. This gives them considerable nuisance capacity, giving rise to theft of sensitive data or, failing that, to serious invasions of the privacy of users. Several examples of attacks relying on this screen capture feature have been documented in recent years. However, there is not sufficient empirical and experimental evidence on this topic. Indeed, to the best of our knowledge, there is no dataset dedicated to screenshot-taking malware until today. The lack of datasets or common testbed platforms makes it difficult to analyse and study their behaviour in order to develop effective countermeasures. The screenshot feature is often a smart feature that does not activate automatically once the malware has infected the machine; the activation mechanisms of this function are often more complex. Consequently, a dataset which is completely dedicated to them would make it possible to better understand the subtleties of triggering screenshots and even to learn to distinguish them from the legitimate applications widely present on devices. The main purpose of this paper is to build such a dataset and analyse the behaviour of screenloggers.

An Optimal Allocation of Tracking Tasks in a SW/HW Fog/Cloud Based Distributed Video Surveillance System

Abstract: In this paper we propose a novel and systematic approach for dynamic allocation of tasks in a video surveillance system using smart cameras and based on Cloud/Fog architecture. Tracking tasks arrive in the system in a random way and must be assigned to the available devices (cameras, Fog nodes and the Cloud). Our approach guarantees the best solution optimizing power consumption and communication cost over the system. The proposed methods uses an integer programming model and its effectiveness is shown on an application example.

Spyware detection technique based on reinforcement learning

Abstract: Analysis of the antivirus technologies, showed that they are not able to detect new spyware with high efficiency, which significantly reduces the reliability and efficiency of its identification Techniques based on heuristic analysis have a high rate of false positives The paper presents a new technique for the spyware detection method in computer systems that provides a principle of proactivity and is based on mechanisms machine learning with the reinforce-mentlearning The suggested method of spyware detection is based on software behavior analysis in computer systems The suggested method involves the computer systems monitoring concerning the software, operates with the behavior

Dataset Construction and Analysis of Screenshot Malware

Abstract: Among the various types of spyware, screenloggers are distinguished by their ability to capture screenshots. This gives them considerable nuisance capacity, giving rise to theft of sensitive data or, failing that, to serious invasions of the privacy of users. Several examples of attacks relying on this screen capture feature have been documented in recent years. However, there is not sufficient empirical and experimental evidence on this topic. Indeed, to the best of our knowledge, there is no dataset dedicated to screenshot-taking malware until today. The lack of datasets or common testbed platforms makes it difficult to analyse and study their behaviour in order to develop effective countermeasures. The screenshot feature is often a smart feature that does not activate automatically once the malware has infected the machine; the activation mechanisms of this function are often more complex. Consequently, a dataset which is completely dedicated to them would make it possible to better understand the subtleties of triggering screenshots and even to learn to distinguish them from the legitimate applications widely present on devices. The main purpose of this paper is to build such a dataset and analyse the behaviour of screenloggers.

Single Sign-On Using Contactless Smart Cards and Fingerprint Authentication

Abstract: As more services are moving onto the Internet, a user needs to maintain several online accounts, ideally each with separate username and password. Remembering a whole set of passwords for each and every web service is definitely a challenging task. There are several single Sign-on services currently available which require to be secured with a master password. The master password can be strong and still be vulnerable to theft. We propose a system which involves contactless smartcards, to store the passwords and fingerprint authorization to authenticate a user for safety and security. This system provides security and convenience at one place.

E-Banking Security: Threats, Challenges, Solutions, and Trends

Abstract: The online banking industry has overgrown in recent years and will continue to grow as economic organizations remain to encourage customers to handle online banking transactions such as money transfers, access to account information, or payment of monthly bills. During this period, internet criminals and fraudsters attempting to steal personal customer information hijacked online banking. This article proposes reviewing the ways by which fraudulent activities are performed and what banks are doing to prevent such activities, as well as the new security measures that banks are using to increase customer confidence. Therefore, the authors present the threats, challenges to address such threats, some trends, and future landscapes regarding online banking security.