Abstract The existence of malicious software (malware) represents a potential threat to users who connect to a large set of services provided by multiple providers. Such malware is capable of stealing, spying on, encrypting data from users, and spreading, provoking impacts that are beyond a single citizen’s device and reaching critical information systems. To detect malware families, Machine Learning and Deep Learning techniques have been employed recently, demonstrating promising results. However, these techniques lack in detecting more advanced malware that employs obfuscation techniques. In this paper, we present DEFENDIFY, a novel framework, empowered by Computer Vision, Deep Learning, and Transfer Learning techniques, that is able to detect completely obfuscated malware with high performance in terms of accuracy and computational consumption. DEFENDIFY comprises three modules: Dataset Creation, Binary Obfuscation, and Model Generation. These modules work together to detect both obfuscated and non-obfuscated malware. The core module, i.e., the Model Generation, employs an entropy tester that determines whether a sample is obfuscated or not. Then, a Deep Learning model powered by Transfer Learning is employed to determine if it is malware or goodware. We validated our framework using real data gathered from malware repositories and legitimate software. The proposed framework was configured to test four Convolutional Neural Network architectures: ResNet18, ResNet34, EfficientNetB3, and EfficientNetV2S. Among them, the ResNet18 architecture obtained the best performance in detecting both non-obfuscated and obfuscated samples with an F1-score of 99.34% and 97.5%, respectively.

Recently, system security represents a big challenge for many organizations, and it must be specifically handled when a system is intended to be deployed in a cloud environment. Cloud environments provide multiple security services that run over a Shared Responsibility Model that requires the participation of the cloud provider and the customer. Thus, this paper proposes an architecture based on Artificial Intelligence to support the finding of system threats and errors in an early stage and on Security Chaos Engineering methodology to reliably test the existence of such errors. This proposed architecture may help orientate better system designs and contribute to building holistic security. A particular use case is described to show how the proposal can be applied to a system that supports services for a military-related organization.

Undoubtedly, modern human digital lives are every day more and more connected, and the revolution of “everything connected” is already becoming a reality. Indeed, humans live in the age of the Internet of Things (IoT), and one of the most usual IoT contexts is a smart home. Unfortunately, such significant enhancement also means that common home devices, such as fridges, cameras, or even bulbs, are exposed to malevolent entities whose primary goal is to threaten the confidentiality, integrity, and availability of the automatically-exchanged information. Aiming at fine-tuning the protection of the smart devices, this paper proposes a novel dynamic rule management solution adaptable to the current status of the IoT environment, so to protect it against cyberattacks. Experiments demonstrated that a notable reduction in the CPU and RAM consumption was achieved when applying this novel scheme. Additionally, the number of packets processed per second increased substantially, inducing a meaningful enhancement also from a security perspective.

Due to the growth of IoT (Internet of Things) devices in different industries and markets in recent years and considering the currently insufficient protection for these devices, a security solution safeguarding IoT architectures are highly desirable. An interesting perspective for the development of security solutions is the use of an event management approach, knowing that an event may become an incident when an information asset is affected under certain circumstances. The paper at hand proposes a security solution based on the management of security events within IoT scenarios in order to accurately identify suspicious activities. To this end, different vulnerabilities found in IoT devices are described, as well as unique features that make these devices an appealing target for attacks. Finally, three IoT attack scenarios are presented, describing exploited vulnerabilities, security events generated by the attack, and accurate responses that could be launched to help decreasing the impact of the attack on IoT devices. Our analysis demonstrates that the proposed approach is suitable for protecting the IoT ecosystem, giving an adequate protection level to the IoT devices.