3,322 research outputs found
Demetrio di Faro, un pirata contro Roma sull’Adriatico
No full textAlcune considerazioni su quello che viene definito il pirata Demetrio di Faro: le sue azioni piratesche fanno presumere come egli volesse tornare all'epoca della regina Teuta
Interview with Prof. Demetrio De Luca
No full textDeLuca speaks about his adventure with the Council, touching on the legacy of various Popes. He then turns to the history of religions, pointing to Christ as a turning point in affirming human value and dignity. He argues this sense of human value enables Christians to walk with Christ in dialogue
Formalizing evasion attacks against machine learning security detectors
No full textRecent work has shown that adversarial examples can bypass machine learning-based threat detectors relying on static analysis by applying minimal perturbations.
To preserve malicious functionality, previous attacks either apply trivial manipulations (e.g. padding), potentially limiting their effectiveness, or require running computationally-demanding validation steps to discard adversarial variants that do not correctly execute in sandbox environments.
While machine learning systems for detecting SQL injections have been proposed in the literature, no attacks have been tested against the proposed solutions to assess the effectiveness and robustness of these methods.
In this thesis, we overcome these limitations by developing RAMEn, a unifying framework that (i) can express attacks for different domains, (ii) generalizes previous attacks against machine learning models, and (iii) uses functions that preserve the functionality of manipulated objects.
We provide new attacks for both Windows malware and SQL injection detection scenarios by exploiting the format used for representing these objects.
To show the efficacy of RAMEn, we provide experimental results of our strategies in both white-box and black-box settings.
The white-box attacks against Windows malware detectors show that it takes only the 2% of the input size of the target to evade detection with ease.
To further speed up the black-box attacks, we overcome the issues mentioned before by presenting a novel family of black-box attacks that are both query-efficient and functionality-preserving, as they rely on the injection of benign content, which will never be executed, either at the end of the malicious file, or within some newly-created sections, encoded in an algorithm called GAMMA.
We also evaluate whether GAMMA transfers to other commercial antivirus solutions, and surprisingly find that it can evade many commercial antivirus engines.
For evading SQLi detectors, we create WAF-A-MoLE, a mutational fuzzer that that exploits random mutations of the input samples, keeping alive only the most promising ones.
WAF-A-MoLE is capable of defeating detectors built with different architectures by using the novel practical manipulations we have proposed.
To facilitate reproducibility and future work, we open-source our framework and corresponding attack implementations.
We conclude by discussing the limitations of current machine learning-based malware detectors, along with potential mitigation strategies based on embedding domain knowledge coming from subject-matter experts naturally into the learning process
An assessment of the impact of possible CAP reform scenarios on Romanian agriculture
No full textUsing a simplified model, with key-variable the prices of two different possible scenarios of CAP reform after 2013 (moderate and radical), this paper present a comparison between the price effects of implementation of each reform scenario at 2015 horizon on Romanian agriculture. This short analysis shows that, under the presented hypotheses, the net welfare effect, due to the price changes, for the selected products, is positive in both reform scenarios, yet greater in the case of the radical reform. Integrated in the large context of Romanian development, it seems that the influence of CAP reform upon agriculture and rural areas will be most likely a gradual one: an interpenetration between the two scenarios is foreseeable, starting with the moderate reform that will dominate the period around 2013, the reform measures acquiring a more radical character afterwards.CAP reform, Romania, welfare effects, Agricultural and Food Policy,
Rich, Sturmian, and trapezoidal words
No full textIn this paper we explore various interconnections between rich words, Sturmian words, and trapezoidal words. Rich words, first introduced by the second and third authors together with J. Justin and S. Widmer, constitute a new class of finite and infinite words characterized by having the maximal number of palindromic factors. Every finite Sturmian word is rich, but not conversely. Trapezoidal words were first introduced by the first author in studying the behavior of the subword complexity of finite Sturmian words. Unfortunately this property does not characterize finite Sturmian words. In this note we show that the only trapezoidal palindromes are Sturmian. More generally we show that Sturmian palindromes can be characterized either in terms of their subword complexity (the trapezoidal property) or in terms of their palindromic complexity. We also obtain a similar characterization of rich palindromes in terms of a relation between palindromic complexity and subword complexity
Min-Max Exact and Heuristic Policies for a Two-Echelon Supply Chain with Inventory and Transportation Procurement Decisions
No full textPractical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
No full textWhile machine learning is vulnerable to adversarial examples, it still lacks
systematic procedures and tools for evaluating its security in different
application contexts. In this article, we discuss how to develop automated and
scalable security evaluations of machine learning using practical attacks,
reporting a use case on Windows malware detection
Nebula: Self-Attention for Dynamic Malware Analysis
No full textDynamic analysis enables detecting Windows malware by executing programs in a controlled environment and logging their actions. Previous work has proposed training machine learning models, i.e., convolutional and long short-term memory networks, on homogeneous input features like runtime APIs to either detect or classify malware, neglecting other relevant information coming from heterogeneous data like network and file operations. To overcome these issues, we introduce Nebula, a versatile, self-attention Transformer-based neural architecture that generalizes across different behavioral representations and formats, combining diverse information from dynamic log reports. Nebula is composed by several components needed to tokenize, filter, normalize and encode data to feed the transformer architecture. We firstly perform a comprehensive ablation study to evaluate their impact on the performance of the whole system, highlighting which components can be used as-is, and which must be enriched with specific domain knowledge. We perform extensive experiments on both malware detection and classification tasks, using three datasets acquired from different dynamic analyses platforms, show that, on average, Nebula outperforms state-of-the-art models at low false positive rates, with a peak of 12% improvement. Moreover, we showcase how self-supervised learning pre-training matches the performance of fully-supervised models with only 20% of training data, and we inspect the output of Nebula through explainable AI techniques, pinpointing how attention is focusing on specific tokens correlated to malicious activities of malware families. To foster reproducibility, we open-source our findings and models at https://github.com/dtrizna/nebula
Practical Evaluation of Poisoning Attacks on Online Anomaly Detectors in Industrial Control Systems
No full textRecently, neural networks (NNs) have been proposed for the detection of cyber attacks targeting industrial control systems (ICSs). Such detectors are often retrained, using data collected during system operation, to cope with the evolution of the monitored signals over time. However, by exploiting this mechanism, an attacker can fake the signals provided by corrupted sensors at training time and poison the learning process of the detector to allow cyber attacks to stay undetected at test time. Previous work explored the ability to generate adversarial samples that fool anomaly detection models in ICSs but without compromising their training process. With this research, we are the first to demonstrate such poisoning attacks on ICS cyber attack online detectors based on neural networks. We propose two distinct attack algorithms, namely, interpolation- and back-gradient-based poisoning, and demonstrate their effectiveness. The evaluation is conducted on diverse data sources: synthetic data, real-world ICS testbed data, and a simulation of the Tennessee Eastman process. This first practical evaluation of poisoning attacks using a simulation tool highlights the challenges of poisoning dynamically controlled systems. The generality of the proposed methods under different NN parameters and architectures is studied. Lastly, we propose and analyze some potential mitigation strategies
- …
