GroupDroid: Automatically Grouping Mobile Malware by Extracting Code Similarities

As shown in previous work, malware authors often reuse portions of code in the development of their samples. Especially in the mobile scenario, there exists a phenomena, called piggybacking, that describes the act of embedding malicious code inside benign apps. In this paper, we leverage such observations to analyze mobile malware by looking at its similarities. In practice, we propose a novel approach that identifies and extracts code similarities in mobile apps. Our approach is based on static analysis and works by computing the Control Flow Graph of each method and encoding it in a feature vector used to measure similarities. We implemented our approach in a tool, GroupDroid, able to group mobile apps together according to their code similarities. Armed with GroupDroid, we then analyzed modern mobile malware samples. Our experiments show that GroupDroid is able to correctly and accurately distinguish different malware variants, and to provide useful and detailed information about the similar portions of malicious code

Remote Attestation of IoT Devices using Physically Unclonable Functions: Recent Advancements and Open Research Challenges

In the past few years, the diffusion of IoT devices used in everyday life has skyrocketed. From wearable devices to smart home appliances, these gadgets are increasingly exposed to the Internet or to open networks. This means that it is necessary to find security solutions that can guarantee the safety of these devices, while at the same time saving on energy consumption and implementation space. In this paper we explore recent works that use remote attestation as a possible solution to the security of IoT devices while also focusing on the use of Physically Unclonable Functions (PUFs). We provide a thorough analysis of the selected papers, providing insights on possible future research directions

The Synergies of Context and Data Aging in Recommendations

In this paper, we investigate the synergies of data aging and contextual information in data mining techniques used to infer frequent, up-to-date, and contextual user behaviours that enable making recommendations on actions to take or avoid in order to fulfill a specific positive goal. We conduct experiments in two different domains: wearable devices and smart TVs

ICARE: An Intuitive Context-Aware Recommender with Explanations

The chapter presents a framework, called Intuitive Context-Aware Recommender with Explanations (ICARE), that can provide contextual recommendations, together with their explanations, useful to achieve a specific and predefined goal. We apply ICARE in the healthcare scenario to infer personalized recommendations related to the activities (fitness and rest periods) a specific user should follow or avoid in order to obtain a high value for the sleep quality score, also on the base of their current context and the physical activities performed during the past days. We leverage data mining techniques to extract frequent and context-aware sequential rules that can be used both to provide positive and negative recommendations and to explain them