Autori e vittime nella criminalità informatica

Il crimine informatico è oggi sempre più decisamente appannaggio di nuove imprese criminali transnazionali caratterizzate da nuovi modelli di costituzione, di arruolamento di adepti e di riciclaggio. Le nuove fenomenologie criminali che attingono alle sempre più sofisticate tecnologie dell’informatica rendono indispensabili nuovi modulati approcci da parte degli organi istituzionalmente chiamati a contrastarle. Aujourd’hui le crime informatique est de plus en plus lié aux organisations criminelles transnationales qui ont développé de nouveaux modèles pour enrôler les personnes et pour obtenir des profits du recyclage de l’argent sale. Ces nouveaux types de crime, qui sont de plus en plus liés aux technologies informatiques sophistiquées, rendent nécessaire l’adoption de nouvelles stratégies de répression par les institutions. Today computer crime belongs to transnational criminal organizations which have new rules for the development of new strategies able to enrol people in their organizations and to obtain profits from money laundering. These new forms of crime which are more and more related to sophisticated data processing technologies must urge institutional agencies for new strategies against crime

Dal computer crime al computer-related crime

Nowadays, Digital Identity Theft has become one of the most lucrative illegitimate business. Also known as “phishing”, it consists in unauthorized access to an individual’s personal financial data aiming to capture information relative to on line banking and on line financial services. At the beginning people were the victims of such scams, currently the attention is directed to computer networks. “Pharming” and “keylogging” are some of the latest and utmost sophisticated data processing techniques used by computer crime fraudsters. Latest entries are the “botnets”, herds of infected machines, usually managed by one sole command centre which can determine serious damages to network systems. Botnets have made large scale identity theft much simpler to realize. Organized crime is becoming more and more involved in this new crime world that can easily assure huge profits. The Italian State Police, in order to respond more effectively to this new rising challenge, has created, with the Postal and Communication Police, an agency highly specialized in combating such new phenomeno

Police Officer Stress, Loping Mechanisms, and Family Life

Law Enforcement Officers experience stress for a variety of reasons that are related to both the nature and the organization of police work. Consequences of stress are felt by the police department, the individual, as well as their family. Building on previous research in this area, this project describes thirteen in-depth interviews with officers and their significant others in an effort to understand the impact of police stress on work and family life and vice versa. Officers were found to struggle between balancing their police role and home life. The family serves as both a coping resource for the officer but also a source of stress. Interactions among the officers also served as a way to cope with stress (as a way to learn skills related to their job, to relieve stress, and to talk about work) but varied depending on work shift. Each of these factors are discussed to understand how these officers manage the interrelated aspects of police work, family life, and stress

On Two Orderings of Lattice Paths

The \emph{Markov numbers} are positive integers appearing as solutions to the Diophantine equation $x^2 + y^2 + z^2 = 3xyz$. These numbers are very well-studied and have many combinatorial properties, as well as being the source of the long-standing unicity conjecture. In 2018, \c{C}anak\c{c}{\i} and Schiffler showed that the Markov number $m_{\frac{a}{b}}$ is the number of perfect matchings of a certain snake graph corresponding to the Christoffel path from $(0,0)$ to $(a,b)$. Based on this correspondence, Schiffler in 2023 introduced two orderings on lattice paths. For any path $\omega$, associate a snake graph $\mathcal{G}(\omega)$ and a continued fraction $g(\omega)$. The ordering $<_M$ is given by the number of perfect matchings on $\mathcal{G}(\omega)$, and the ordering $<_L$ is given by the Lagrange number of $g(\omega)$. In this work, we settle two conjectures of Schiffler. First, we show that the path $\omega(a,b) = RR\cdots R UU \cdots U$ is the unique maximum over all lattice paths from $(0,0)$ to $(a,b)$ with respect to both orderings $<_M$ and $<_L$. We then use this result to prove that $\sup L(\omega)$ over all lattice paths is exactly $1+\sqrt5$.Comment: 11 pages, 2 figure

Mitigating Adversarial Gray-Box Attacks Against Phishing Detectors

Although machine learning based algorithms have been extensively used for detecting phishing websites, there has been relatively little work on how adversaries may attack such "phishing detectors" (PDs for short). In this paper, we propose a set of Gray-Box attacks on PDs that an adversary may use which vary depending on the knowledge that he has about the PD. We show that these attacks severely degrade the effectiveness of several existing PDs. We then propose the concept of operation chains that iteratively map an original set of features to a new set of features and develop the "Protective Operation Chain" (POC for short) algorithm. POC leverages the combination of random feature selection and feature mappings in order to increase the attacker's uncertainty about the target PD. Using 3 existing publicly available datasets plus a fourth that we have created and will release upon the publication of this paper, we show that POC is more robust to these attacks than past competing work, while preserving predictive performance when no adversarial attacks are present. Moreover, POC is robust to attacks on 13 different classifiers, not just one. These results are shown to be statistically significant at the p < 0.001 level

Making cheese with caprifig sap in Apulia, Italy: possible rebirth of an ancient tradition

Abstract Background and objectives Making cheese by coagulating milk with extracts or parts of plants is a tradition of many countries facing the Mediterranean basin. Such cheeses were historically produced from sheep and goat milk and represent an important cultural heritage. In the European Union (EU), nowadays, their production is allowed only after legal validation of the manufacturing process under the hygienic point of view. Unfortunately, validation has been possible only for a few Protected Designation of Origin cheeses, but other dairy products exist for which it has not been carried out. It is the case of the caprifig sap cheeses produced in the "Murgia" highplain, Apulia region, Southern Italy. In this rural marginal area, three cheeses have been historically made by this coagulant: milk sap ricotta, Pampanella, and Cacioricotta. Due to the above legal concerns, they have become very rare and, if no action is taken, they will disappear very soon. The main purpose of the present work was to make a survey about the status of preservation of their processing methods and to document them before it is too late. A second aim was to perform a first summary investigation about their safety and compositional and sensory characteristics. Methods A series of face-to-face interviews was conducted to owners and cheesemakers of sheep and goat farms laying in the Murgia Hills territory. Cheese samples were prepared at three different rural dairies and subjected to chemical, microbiological, and sensory analyses. Results and conclusions The survey confirmed that caprifig sap cheeses are still occasionally produced for family consumption, mainly from goat milk in the southern part of the highplain. They have the common characteristic of deriving from milk subjected to strong heat treatment and containing both casein and whey proteins. The manufacturing procedures were observed, and two different methods of preparing and using caprifig sap were documented. The cheesemaking process was analyzed and discussed under a technological point of view, and geo-sociological connections were hypothesized. The three cheeses presented significant sensory differences and proved to potentially match the EU hygienic standards if the post-vat operations are performed under correct conditions. Overall, the study gave a contribution for the hygienic validation of the manufacturing process in view of a possible rebirth

On the Evaluation of Sequential Machine Learning for Network Intrusion Detection

Recent advances in deep learning renewed the research interests in machine learning for Network Intrusion Detection Systems (NIDS). Specifically, attention has been given to sequential learning models, due to their ability to extract the temporal characteristics of Network traffic Flows (NetFlows), and use them for NIDS tasks. However, the applications of these sequential models often consist of transferring and adapting methodologies directly from other fields, without an in-depth investigation on how to leverage the specific circumstances of cybersecurity scenarios; moreover, there is a lack of comprehensive studies on sequential models that rely on NetFlow data, which presents significant advantages over traditional full packet captures. We tackle this problem in this paper. We propose a detailed methodology to extract temporal sequences of NetFlows that denote patterns of malicious activities. Then, we apply this methodology to compare the efficacy of sequential learning models against traditional static learning models. In particular, we perform a fair comparison of a `sequential' Long Short-Term Memory (LSTM) against a `static' Feedforward Neural Networks (FNN) in distinct environments represented by two well-known datasets for NIDS: the CICIDS2017 and the CTU13. Our results highlight that LSTM achieves comparable performance to FNN in the CICIDS2017 with over 99.5\% F1-score; while obtaining superior performance in the CTU13, with 95.7\% F1-score against 91.5\%. This paper thus paves the way to future applications of sequential learning models for NIDS

Detection and Threat Prioritization of Pivoting Attacks in Large Networks

Several advanced cyber attacks adopt the technique of "pivoting" through which attackers create a command propagation tunnel through two or more hosts in order to reach their final target. Identifying such malicious activities is one of the most tough research problems because of several challenges: command propagation is a rare event that cannot be detected through signatures, the huge amount of internal communications facilitates attackers evasion, timely pivoting discovery is computationally demanding. This paper describes the first pivoting detection algorithm that is based on network flows analyses, does not rely on any a-priori assumption on protocols and hosts, and leverages an original problem formalization in terms of temporal graph analytics. We also introduce a prioritization algorithm that ranks the detected paths on the basis of a threat score thus letting security analysts investigate just the most suspicious pivoting tunnels. Feasibility and effectiveness of our proposal are assessed through a broad set of experiments that demonstrate its higher accuracy and performance against related algorithms