Software Attestation with Static and Dynamic Techniques

L'abstract è presente nell'allegato / the abstract is in the attachmen

On the impossibility of effectively using likely-invariants for software attestation purposes

Invariants monitoring is a software attestation technique that aims at proving the integrity of a running application by checking likely-invariants, which are statistically significant predicates inferred on variables’ values. Being very promising, according to the software protection literature, we developed a technique to remotely monitor invariants. This paper presents the analysis we have performed to assess the effectiveness of our technique and the effectiveness of likely-invariants for software attestation purposes. Moreover, it illustrates the identified limitations and our studies to improve the detection abilities of this technique. Our results suggest that, despite further studies and future results may increase the efficacy and reduce the side effects, software attestation based on likely-invariants is not yet ready for the real world. Software developers should be warned of these limitations, if they could be tempted by adopting this technique, and companies developing software protections should not invest in development without also investing in further research

Privacy issues of ISPs in the modern web

In recent years, privacy issues in the networking field are getting more important. In particular, there is a lively debate about how Internet Service Providers (ISPs) should collect and treat data coming from passive network measurements. This kind of information, such as flow records or HTTP logs, carries considerable knowledge from several points of view: traffic engineering, academic research, and web marketing can take advantage from passive network measurements on ISP customers. Nevertheless, in many cases collected measurements contain personal and confidential information about customers exposed to monitoring, thus raising several ethical issues. Modern web is very different from the one we experienced few years ago: web services converged to few protocols (i.e., HTTP and HTTPS) and a large share of traffic is encrypted. The aim of this work is to provide an insight about which information is still visible to ISPs, with particular attention to novel and emerging protocols, and to what extent it carries personal information. We illustrate that sensible information, such as website history, is still exposed to passive monitoring. We illustrate privacy and ethical issues deriving by the current situation and provide general guidelines and best practices to cope with the collection of network traffic measurements

Comparative analysis of neural networks techniques to forecast Airfare Prices

With the growth of tourism industry, airplanes have became an affordable choice for medium- and long-distance travels. Accurate forecasting of flights tickets helps the aviation industry to match demand, supply flexibly and optimize aviation resources. Airline companies use dynamic pricing strategies to determine the price of airline tickets to maximize profits. Passengers want to purchase tickets at the lowest selling price for the flight of their choice. However, airline tickets are a special commodity that is time-sensitive and scarce, and the price of airline tickets is affected by various factors. Our research work provides a systematic comparison of various traditional machine learning methods (i.e., Ridge Regression, Lasso Regression, K-Nearest Neighbor, Decision Tree, XGBoost, Random Forest) and deep learning methods (e.g., Fully Connected Networks, Convolutional Neural Networks, Transformer) to address the problem of airfare prediction, by keeping the consumers’ needs. Moreover, we proposed innovative Bayesian neural networks, which represent the first exploitation attempt of Bayesian Inference for the airfare prediction task, to the best of our knowledge. Therefore, we evaluate the performance of our implemented and optimized models on an open dataset. The experimental results show that deep learning-based methods achieve better results on average than traditional ones, while Bayesian neural networks can achieve better performance among the other machine learning methods. However, taking into account both prediction performance and computational time, the Random Forest turns out to be the best choice to apply in this scenario

Towards Automatic Risk Analysis and Mitigation of Software Applications

This paper proposes a novel semi-automatic risk analysis approach that not only identifies the threats against the assets in a software application, but it is also able to quantify their risks and to suggests the software protections to mitigate them. Built on a formal model of the software, attacks, protections and their relationships, our implementation has shown promising performance on real world applications. This work represents a first step towards a user-friendly expert system for the protection of software applications

Estimating Software Obfuscation Potency with Artificial Neural Networks

This paper presents an approach to estimate the potency of obfuscation techniques. Our approach uses neural networks to accurately predict the value of complexity metrics - which are used to compute the potency - after an obfuscation transformation is applied to a code region. This work is the first step towards a decision support to optimally protect software applications