Towards a Multi-Layered Phishing Detection.

Phishing is one of the most common threats that users face while browsing the web. In the current threat landscape, a targeted phishing attack (i.e., spear phishing) often constitutes the first action of a threat actor during an intrusion campaign. To tackle this threat, many data-driven approaches have been proposed, which mostly rely on the use of supervised machine learning under a single-layer approach. However, such approaches are resource-demanding and, thus, their deployment in production environments is infeasible. Moreover, most previous works utilise a feature set that can be easily tampered with by adversaries. In this paper, we investigate the use of a multi-layered detection framework in which a potential phishing domain is classified multiple times by models using different feature sets. In our work, an additional classification takes place only when the initial one scores below a predefined confidence level, which is set by the system owner. We demonstrate our approach by implementing a two-layered detection system, which uses supervised machine learning to identify phishing attacks. We evaluate our system with a dataset consisting of active phishing attacks and find that its performance is comparable to the state of the art

Stimulating sustainable development in Brazil’s coffee sector : an empirical analysis on integrated landscape management strategies

This report investigates the sustainable agricultural performance of coffee farms in the south-eastern states of Brazil under integrated landscape management strategies. 651 municipalities were analysed across Paraná, Sao Paulo, Minas Gerais, Rio de Janeiro and Espirito Santo between 2002 and 2016 to study the socioeconomic and environmental impacts of Nespresso’s AAA Sustainable Quality Program and the 2012 Brazil Investment Plan for Sustainable Land Use and Forest Management in the Cerrado biome. Using a Difference in Difference model with fixed effects estimations, I identified that both programs have facilitated significant improvements across income, yields and crop value. The research provides insights into the strategic opportunities for value chain investors, governments, financial institutions and farmers to improve environmental practices in coffee farming with economic incentives. Ultimately, my research provides compelling insights on the efficacy of integrated landscape management approaches for meeting the growing consumption demand as well as the commitments of Brazil’s ecosystem conservation and restoration initiatives. “You cannot tackle hunger, disease, and poverty unless you can also provide people with a healthy ecosystem in which their economies can grow.” — Gro Harlem Brundtlandnhhma

Διατροφή και ταχεία απώλεια βάρους σε αθλητές πάλης

Σκοπός αυτής της εργασίας είναι να παρουσιάσει ένα σύνολο μελετών που έχουν διεξαχθεί παγκοσμίως σχετικά με την διατροφή των αθλητών πάλης και συγκεκριμένα με τις μεθόδους που ακολουθούν για την ταχεία απώλεια του βάρους τους προ-αγωνιστικά καθώς και τις επιπτώσεις που φέρει τόσο στην απόδοση όσο και στην ψυχολογία τους. Η διατροφή αποτελεί έναν κρίσιμο παράγοντα για την υγεία και την απόδοση των αθλητών, και αυτό ισχύει ιδιαίτερα για τους παλαιστές. Στην πάλη, οι αθλητές πρέπει να διατηρούν την κατάλληλη κατηγορία του βάρους τους και να είναι σε καλή σωματική κατάσταση για να ανταποκριθούν στις απαιτήσεις των αγώνων. Ωστόσο, η ταχεία απώλεια βάρους μπορεί να επηρεάσει αρνητικά την υγεία και την απόδοση των παλαιστών. Τα συμπεράσματα της παρούσης βιβλιογραφικής ανασκόπησης έδειξαν ότι στις περισσότερες έρευνες όλες οι τεχνικές ταχείας απώλειας βάρους θα επιτρέψουν στους παλαιστές να φτάσουν σε μια συγκεκριμένη κατηγορία βάρους όμως αυτό μπορεί να μην είναι ασφαλές για την μακροπρόθεσμη υγεία του αθλητή. Συνολικά, η διατροφή και η απώλεια βάρους αποτελούν κρίσιμους παράγοντες για την υγεία και την απόδοση των παλαιστών. Οι παλαιστές πρέπει να είναι ενημερωμένοι για τις σωστές διατροφικές ανάγκες και να γίνεται σωστή επιλογή υγιεινών τροφίμων καθώς επίσης να λαμβάνουν συμβουλές από επαγγελματίες προπονητές και διατροφολόγους με σκοπό να έχουν μια επιτυχημένη καριέρα στην πάλη και να διατηρήσουν την υγεία τους για την υπόλοιπη ζωή τους.ΟΧ

Stimulating sustainable development in Brazil’s coffee sector : an empirical analysis on integrated landscape management strategies

This report investigates the sustainable agricultural performance of coffee farms in the south-eastern states of Brazil under integrated landscape management strategies. 651 municipalities were analysed across Paraná, Sao Paulo, Minas Gerais, Rio de Janeiro and Espirito Santo between 2002 and 2016 to study the socioeconomic and environmental impacts of Nespresso’s AAA Sustainable Quality Program and the 2012 Brazil Investment Plan for Sustainable Land Use and Forest Management in the Cerrado biome. Using a Difference in Difference model with fixed effects estimations, I identified that both programs have facilitated significant improvements across income, yields and crop value. The research provides insights into the strategic opportunities for value chain investors, governments, financial institutions and farmers to improve environmental practices in coffee farming with economic incentives. Ultimately, my research provides compelling insights on the efficacy of integrated landscape management approaches for meeting the growing consumption demand as well as the commitments of Brazil’s ecosystem conservation and restoration initiatives. “You cannot tackle hunger, disease, and poverty unless you can also provide people with a healthy ecosystem in which their economies can grow.” — Gro Harlem Brundtlan

Component Analysis

components, saliency Abstract: A method is proposed for constructing salient features from a set of fea-tures that are given as input to a feedforward neural network used for supervised learning. Combinations of the original features are formed that maximize the sensi-tivity of the network’s outputs with respect to variations of its inputs. The method exhibits some similarity to Principal Component Analysis, but also takes into account supervised character of the learning task. It is applied to classification problems lead-ing to improved generalization ability originating from the alleviation of the curse of dimensionality problem. This paper has not been submitted elsewhere in identical or similar form, nor will it be during the first three months after its submission to Neural Processing Letters

Dimensionality Reduction Using a Novel Neural Network Based Feature Extraction Method

A novel neural network based method for feature extraction is proposed. The method achieves dimensionality reduction of input vectors used for supervised learning problems. Combinations of the original features are formed that maximize the sensitivity of the network's outputs with respect to variations of its inputs. The method exhibits some similarity to Principal Component Analysis, but also takes into account supervised character of the learning task. It is applied to classification problems leading to efficient dimensionality reduction and increased generalization ability. 2. Introduction Methods for dimensionality reduction concentrate either on selecting from the original set of features a smaller subset of salient features, or on combining the original features in such a way as to extract a small number of salient features. Application of such methods to data analysis or pattern recognition problems has distinct advantages in terms of generalization properties and processing s..

Πολεμώντας μια άνιση μάχη: η χρήση μη συμβατικών αμυντικών τεχνολογιών για την αντιμετώπιση εξεζητημένων απειλών (APT)

The number and complexity of cyber-attacks has been increasing steadily in recent years. The major players in today’s cyber conflicts are well organized and heavily funded teams with specific goals and objectives, working for or supported by a nation-state. A commonly used term to describe such teams/groups is Advanced Persistent Threat (APT). APT target the communication and information systems of government, military and industrial organizations and are willing to use vast amounts of money, time and expertise to reach their goals.In addition, serious insider attacks have occurred that resulted in the publication of several thousand classified documents, highlighting the fact that even in sensitive institutions, the effectiveness of the existing security safeguards is insufficient.Advances in attacker sophistication have not been matched by similar defensive advances. The concept of keeping the internal, trusted network separated from the external, untrusted one (i.e. boundary protection) has become obsolete. The use of blacklists or signatures for attack detection is practically useless against sophisticated attackers. The security industry, having spent decades developing security products such as anti-malware solutions and intrusion-detection/prevention systems, refuses to admit the shortcomings of these products.It is not uncommon for security companies to advertise that their products can detect and stop APT, even though the same products have been unable to detect such attacks for several years. Furthermore, C-level executives fail to understand the need for more robust security mechanisms, as they believe that by following vendor recommendations and making significant investments in traditional security solutions, they will keep their organization secure. However reality has proven them wrong, over and over again.In order to defend against such sophisticated adversaries, it is necessary to redesign our defenses and develop technologies focused more on detection than prevention.The purpose of this thesis is to offer a comprehensive view of the APT problem by analyzing the most common techniques, tools and attack paths that attackers are using, and highlighting the shortcomings of current security solutions. The use of deception techniques for attack detection is one of the integral focal points of this thesis. Based on this concept, a novel APT detection model is proposed, implemented and evaluated.The evaluation results highlight the significant efficacy of the model in detecting sophisticated attacks, with a very low false positive rate.Τόσο ο αριθμός όσο και η πολυπλοκότητα των κυβερνοεπιθέσεων αυξάνονται διαρκώς. Τα ευρυζωνικά δίκτυα, οι σύνθετες διαδικτυακές πλατφόρμες, η χρήση social networks και cloud services και η αυξανόμενη χρήση έξυπνων συσκευών (π.χ. smartphones, tablets) - ακόμα και σε διαβαθμισμένα δίκτυα - έχουν δημιουργήσει νέες προκλήσεις. Οι πολύπλοκες και άρτια οργανωμένες επιθέσεις σε κρίσιμες υποδομές, στρατιωτικά δίκτυα και κυβερνητικούς φορείς που έχουν γίνει γνωστές τα τελευταία χρόνια, οφείλονται σε εξαιρετικά ικανές και άρτια οργανωμένες τεχνικές ομάδες, που συνήθως εργάζονται υπό την αιγίδα κάποιου κρατικού οργανισμού και είναι γνωστές ως Advanced Persistent Threat (APT). Η χρήση περίτεχνου ιομορφικού λογισμικού και άγνωστων αδυναμιών (zero-day vulnerabilities), κάνει την εντοπισμό και την αντιμετώπιση των συγκεκριμένων επιθέσεων ιδιαίτερα προβληματική με τις υπάρχουσες τεχνολογίες, οι οποίες ακολουθούν τις ίδιες σχεδιαστικές αρχές εδώ και δεκαετίες: Προσπάθεια πρόληψης επιθέσεων (prevention), και προσπάθεια εντοπισμού επιθέσεων σε πραγματικό χρόνο. Αυτή η σχεδιαστική λογική καθιστά ακόμα και τις λύσεις ασφάλειας που θεωρούνται ως state-of-the-art, ανεπαρκείς για την αντιμετώπιση εξεζητημένων (sophisticated) απειλών.Συνεπώς, είναι σαφής η ανάγκη επανασχεδιασμού των αμυντικών τεχνολογιών. Η χρήση τεχνικών παραπλάνησης (deception) αποτελεί μια εξαιρετική μέθοδο για εντοπισμό επιθέσεων, ανεξάρτητα από τις τεχνικές δεξιότητες των επιτιθεμένων. Η χρήση honeypots για τον εντοπισμό δικτυακών επιθέσεων, honey files και honey tokens για τον εντοπισμό μη εξουσιοδοτημένης πρόσβασης σταθμούς βάσης & εξυπηρετητές, και honey user-accounts / authentication tokens για τον εντοπισμό privilege escalation και pass-the-hash επιθέσεων, είναι μερικές από τις μεθόδους πάνω στις οποίες έχει βασιστεί το προτεινόμενο μοντέλο, για τον εντοπισμό εξεζητημένων επιθέσεων.Η αξιολόγηση του μοντέλου τονίζει τη υψηλή αποτελεσματικότητα του στον εντοπισμό εξεζητημένων απειλών

Efficient linear discriminant analysis using a fast quadratic programming algorithm

Abstract-An algorithm is proposed for performing linear discriminant analysis using a single-layered feedforward network. The algorithm follows successive steepest descent directions with respect to the perceptron cost function, taking care not to increase the number of misclassified patterns. The algorithm has no free parameters and therefore no heuristics are involved in its application. Its efficiency in terms of speed of convergence is demonstrated in a number of pattern classification problems. I