1,721,017 research outputs found
A System to Prevent Multi-users and Multi-sessions Attack to Breach Privacy Policies in a Trust-End Filter
No full text
An empirical study of metric-based methods to detect obfuscated code
No full textProtecting data and applications from malware and other forms of malicious code has assumed a great relevance in the current era of pervasive web-based applications. Attackers often use code obfuscation to hide harmful programs from automatic detection. Several researchers have proposed methods to classify an unknown program as malicious or benign; however, little work has been done to identify obfuscated code. A promising approach to detect obfuscated code consists of using a set of metrics, collected by static analysis, to classify a program. In this paper we present an empirical evaluation of three text-based metrics to identify obfuscated code. Our experiment shows that the effectiveness of these metrics depends on the obfuscators: there are cases in which the metrics allow the proliferation of false positives (i.e., misclassification of clear code as obfuscated code), which is bothering but not dangerous, and cases where false negatives (i.e. misclassification of obfuscated as clear code) proliferate, which is definitely more dangerous. Based on our experiment, we propose a combination of these three metrics and show how this combination outperforms the individual metrics
Static analysis for the detection of metamorphic computer viruses using repeated-instructions counting heuristics
No full text
Adversarial deep learning for energy management in buildings
No full textDeep learning is a powerful means to classify and thus optimize Energy management in Buildings. Deep learning is effective especially when the training dataset has a reduced volume or when the test set changes at a higher frequency than the training set. Notwithstanding these favourable properties, the classification with deep learning could be distorted by an adversary who can be interested to alter the classification of the energy consumption. Several kinds of fraud could require this attack, as those aimed at energy theft. In this paper we will provide experimental implants where a dataset is tampered with in order to lead the classifier to acquire it as valid, while it contains samples attributable to energy thefts
la misurazione della conoscenza tecnologica nelle organizzazione produttrici di software
No full textProMisE: a Framework for Process models custoMisation to the opErative context
No full textProcess diversity has recently become a target for the attention of a large part of the Software Engineering community. It implies that in order for a process model to be effective it must be specialized with respect to the context in which the process is execute. The authors face this problem by proposing ProMisE, a process pattern based framework able to capitalize the experiences gained in using a process model in diverse environments. It is an experience base focused on process models
- …
