An increasing trend exploits steganography to conceal payloads in digital images, e.g., to drop malicious executables or to retrieve configuration files. Due to the very attack-specific nature of the exploited hiding mechanisms, developing general detection methods is a hard task. An effective approach concerns the creation of ad-hoc solutions to be integrated within general toolkits, also to holistically face unknown threats. Therefore, this paper discusses the integration of a tool for detecting malicious contents hidden in digital images via the Invoke-PSImage technique within the Secure Intelligent Methods for Advanced Recognition of Malware and Stegomalware framework. Since the real impact of images embedding steganographic threats and the behavior of ad-hoc solutions in realistic scenarios are still unknown territories, this work also showcases a performance evaluation conducted in a nation-wide telecommunication provider. Results demonstrated the effectiveness of the approach and also support the need of modular architectures to face the emerging wave of highly-specialized threats.

There is a profuse abundance of network security incidents around the world every day. Increasingly, services and data stored on servers fall victim to sophisticated techniques that cause all sorts of damage. Hackers invent new ways to bypass security measures and modify the existing viruses in order to deceive defense systems. Therefore, in response to these illegal procedures, new ways to defend against them are being developed. In this paper, a method for anomaly detection based on machine learning technique is presented and a near real-time processing system architecture is proposed. The main contribution is a test-run of ML algorithms on real-world data coming from a world-class telecom operator. This work investigates the effectiveness of detecting malicious behaviour in network packets using several machine learning techniques. The results achieved are expressed with a set of metrics. For better clarity on the classifier performance, 10-fold cross-validation was used.

The growing diffusion of malware is causing non-negligible economic and social costs. Unfortunately, modern attacks evolve and adapt to defensive mechanisms, and many threats are designed for the optimal exploitation of the traits of the victims. Thus, phenomena such as mobile malware, fileless malware or stegomalware are becoming widespread and represent the next variations of malicious attacks that have to be faced. In particular, the massive amount of digital content shared on the Internet is increasingly more often being used by attackers for the injection of malicious code to bypass security tools or prevent detection.