Applying Machine Learning to Cyber Security

Intrusion Detection Systems (IDS) nowadays are a very important part of a system. In the last years many methods have been proposed to implement this kind of security measure against cyber attacks, including Machine Learning and Data Mining based. In this work we discuss in details the family of anomaly based IDSs, which are able to detect never seen attacks, paying particular attention to adherence to the FAIR principles. This principles include the Accessibility and the Reusability of software. Moreover, as the purpose of this work is the assessment of what is going on in the state of the art we have selected three approaches, according to their reproducibility and we have compared their performances with a common experimental setting. Lastly real world use case has been analyzed, resulting in the proposal of an usupervised ML model for pre-processing and analyzing web server logs. The proposed solution uses clustering and outlier detection techniques to detect attacks in an unsupervised way

Text watermarking e Social Network: uno studio sperimentale

La diffusione dei Social Network ha portato alla necessità di utilizzare tecniche per fare copyright e autenticazione dei file su di essi diffusi. Viene presentato un metodo di watermarking testuale basato sulla sostituzione dei caratteri omoglifi e studiato nell'ambiente dei Social Network. E' stata posta particolare attenzione sulla possibilità che questi adottino già tecniche di watermarking testuale e successivamente sono state studiate le potenzialità dell'algoritmo proposto sulle diverse piattaforme, valutandone la percentuale di successo, la robustezza e l'invisibilità

Indications for prophylactic osteosynthesis associated with curettage in benign and low-grade malignant primitive bone tumors of the distal femur in adult patients: a case series

Background: The aim of the study was to evaluate whether the use of preventive osteosynthesis after curettage in benign and primitive low-grade malignant bone tumor localized in the distal femur in adult patients provides sufficient mechanical stability to the system as to allow weight-bearing and reduce the risk of postoperative fracture. Additionally, lower limb function after curettage and preventive osteosynthesis was evaluated. Materials and methods: We analyzed twelve cases of benign and low-grade malignant bone lesions of the distal femur in adult patients treated in our orthopedic department between 2008 and 2011 with curettage, bone filling and preventive osteosynthesis. All patients were treated with curettage with the use of high-speed cutters, plus liquid nitrogen as local adjuvant in low-grade malignant lesions, and filling of the lesion with bone graft or allograft or acrylic cement, followed by osteosynthesis. Results: No fractures or major complications were observed; good function of the knee was observed. Conclusion: We recommend preventive osteosynthesis after curettage in patients with very large lesions (>5 cm, >60 cm3) or high functional requirements, in obese patients, and when local adjuvants are used. Level of evidence: Level IV retrospective case-series study

Is Peripheral Oxygen Saturation a Reliable Predictor of Upper Airways Air-Flow Limitation?

Background: Dyspnea secondary to acute upper airways airflow limitation (UAAFL) represents a clinical emergency that can be difficult to recognize without a suitable history; even when etiology is known, parameters to assess the severity are unclear and often improperly used. Objectives: The aim of this study was to assess the role of peripheral oxygen saturation (SpO2) as a predictor of severity of upper airway obstruction. Methods: The authors propose an experimental model of upper airway obstruction by a progressive increase of UAAFL. Ten healthy volunteers randomly underwent ventilation for 6 min with different degrees of UAAFL. SpO2, heart rate, respiratory rate (RR), tidal volume, accessory respiratory muscle activation, and subjective dyspnea indexes were measured. Results: In this model, SpO2 was not reliable as the untimely gravity index of UAAFL. Respiratory rate, visual analogue scale (VAS), and Borg dyspnea scale were statistically correlated with UAAFL (p 0.05) and tidal volume (p > 0.05); a RR ≤ 7 breaths/min; VAS and Borg scale showed statistically significant parameters changes (p < 0.05). Conclusions: RR, VAS, and Borg dyspnea scales are sensitive parameters to detect and stage, easily and quickly, the gravity of an upper airways impairment, and should be used in emergency settings for an early diagnosis of a UAAFL. SpO2 is a poorer predictor of the degree of upper airways flow limitation

Linked Open Data Validity -- A Technical Report from ISWS 2018

Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want to benefit from it as background knowledge for supporting their tasks. LOD has emerged as the backbone of applications in diverse fields such as Natural Language Processing, Information Retrieval, Computer Vision, Speech Recognition, and many more. Nevertheless, regardless of the specific tasks that LOD-based tools aim to address, the reuse of such knowledge may be challenging for diverse reasons, e.g. semantic heterogeneity, provenance, and data quality. As aptly stated by Heath et al. Linked Data might be outdated, imprecise, or simply wrong": there arouses a necessity to investigate the problem of linked data validity. This work reports a collaborative effort performed by nine teams of students, guided by an equal number of senior researchers, attending the International Semantic Web Research School (ISWS 2018) towards addressing such investigation from different perspectives coupled with different approaches to tackle the issue

Linked Open Data Validity -- A Technical Report from ISWS 2018

Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want to benefit from it as background knowledge for supporting their tasks. LOD has emerged as the backbone of applications in diverse fields such as Natural Language Processing, Information Retrieval, Computer Vision, Speech Recognition, and many more. Nevertheless, regardless of the specific tasks that LOD-based tools aim to address, the reuse of such knowledge may be challenging for diverse reasons, e.g. semantic heterogeneity, provenance, and data quality. As aptly stated by Heath et al. Linked Data might be outdated, imprecise, or simply wrong": there arouses a necessity to investigate the problem of linked data validity. This work reports a collaborative effort performed by nine teams of students, guided by an equal number of senior researchers, attending the International Semantic Web Research School (ISWS 2018) towards addressing such investigation from different perspectives coupled with different approaches to tackle the issue