Optional group:
curriculum Sistemi Informatici Complessi II ANNO -quattro insegnamenti a scelta tra tredici - (show)
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20810140 -
CYBERSECURITY
(objectives)
The Cybersecurity course intends to provide the student with competencies needed for understanding and tackling cybersecurity problems for ICT systems and complex organizations, to design networks and computing systems with a certain level of security, and to planning e manage activities related to cybersecurity. The course provides competences about attacks, countermeasures, cryptographic tools, applications, and methodologies in the cybersecurity field. Advanced topics in data integrity are also addressed.
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PIZZONIA MAURIZIO
( syllabus)
• Course introduction
• Introduction to computer security and terminology
• Vulneability and threats
◦ Software vulnerabilities. Trusted and untrusted input, input validation. Vulnerabilities of applications written with interpreted languages, code injection. Injection into web pages: XSS. Cross site request forgery. OWASP.
▪ Example of web site that is vulnerable to sql injection
◦ buffer overflow attacks. Exploitation: privilege excalation, intrusions through opens services, intrusions through untrusted documents (email, web, etc).
▪ Example of vulnerable code, buffer overflow and related exploit
◦ Vulnerabilities of networks: sniffing, mac flood, ARP poisoning, vulnerability of DNS, Kaminsky attack. TCP session hijecking, MitM attack, DoS and Distributed DoS, Route hijacking.
• Security planning : security plan content, risk analysis.
• Countermeasures
◦ Design principles of policies and mechanisms.
◦ Models: AAA, confinement, DAC, MAC, access control matrix
◦ Cryptographic techniques:
▪ critptography basics (hash, symmetric c., asymmetric c., MAC, digital signature), birthday attack, rainbow, key quality, pseudo-random number generators.
▪ Authentication protocols and key exchange. replay and reflection attacks. Nonces. Perfect Forward Secrecy. Diffie-Helman.
▪ Certificates, certification authority, public key infrastructures and their vulnerabilities.
▪ Applications: Protocols ssl, tls, ssh, virtual private networks, ipsec, etc. Autnetication protocols wan and lan. radius and vulnerabilities. Other applications.
◦ Anomaly detection systems.
◦ System security:
▪ general principles: passwords and their vulnerabilities, hardening, assessment and auditing
▪ unix: discertionaly access control, file system security, authentication, PAM, syslog
◦ Network security:
▪ Firewalling:stateless and statefull firewall, connections, syn-proxy and syn-cookies, load balancing and high availability, linux netfilter and configuration examples.
▪ Network siecurity at level 1 and 2.
▪ Applicative proxies and network intrusion detection systems .
• Authenticated Data Structures
• Distributed Ledger Technologies and Bitcoin
• Smart contracts
• Cybersecurity in big organizations.
( reference books)
Course handouts
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6
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ING-INF/05
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54
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Core compulsory activities
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20810211 -
Algorithms for big data
(objectives)
In many application contexts huge volumes of data are produced which are
used in the economic-financial, political, social and even institutional
fields. Often the data is stored in huge distributed clouds and is
sometimes generated according to a continuous flow, so large as to make
complete storage unfeasible. In many cases the data pertains to entities
in close relationship with each other and gives rise to massive networks
of connections. Familiar examples for such networks are biological and
social networks, distribution networks, and the Web graph. Furthermore,
the fact that the data is stored in systems managed by third parties
poses integrity problems, which have not been considered in the
classical IT literature in terms of both their type and scale.
This scenario poses unprecedented algorithmic challenges, which are
being considered by a vast audience of researchers. In the last decade,
this effort has produced many innovations on both the methodological and
technological level. This course aims at transferring to the students
some of the most important methodological tools originated from the
research on Big Data algorithms. These methodological tools are
presented within challenging application contexts.
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DI BATTISTA GIUSEPPE
( syllabus)
Data processing in streaming mode: approximate counting, sampling and reservoir sampling, bloom filters, frequent itemsets, number of distinct elements
Multidimensional queries: parallelization, spatial/non-spatial partitioning, orthogonal range searching/counting, closest pair, kD-trees, range trees, layered trees, fractional cascading.
Community detection in social graphs: (bi)connected components, maximal clique, k-cores, k-plexes; algorithms for their calculation in a distributed environment.
DB-Trees: efficient aggregate range queries on any DBMS
Scalability and Big Data: P2P systems, Distributed Hash Tables and Chord, chord-like design in NoSQL DBMSes. Big data integrity: threat model for the cloud, non-scalability of traditional authenticated data structures, scaling with a pipelining approach. Blockchain and Big Data: the scalability trilemma.
( reference books)
Slides plus:
Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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PATRIGNANI MAURIZIO
( syllabus)
1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
2) Sub-linear algorithms
- Diameter approximation
- Property testing
3) Clustering
4) Algorithms and data structures for quantitative features analysis
- 1d-,2d-,3d-range queries
- Skyline (pareto frontier), near-neighbor search, Voronoi diagrams
5) Dimensionality reduction
6) Algorithms for the decomposition of complex networks
- Decomposition into k-connected components
- Decomposition into k-cores, maximal cliques, maximal k-plexes
7) Distributed Hash Tables, Consistent Hashing
8) Integrity of large data sets, consistency in distributed systems, CAP/PACELC theorems and their impact on NoSQL databases
( reference books)
Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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FRATI FABRIZIO
( syllabus)
1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
- Number of distinct elements
2) Dimensionality reduction
- Johnson–Lindenstrauss lemma
- Embedding metric spaces with low distortion
3) Algorithms and data structures for quantitative features analysis
- orthogonal range searching (kd-trees and range trees)
- nearest neighbour search, k-nearest neighbour search
- fractional cascading and simplex range search
4) Algorithms for the decomposition of complex networks
- Decomposition into k-connected components
- Decomposition into k-cores, maximal cliques, maximal k-plexes
5) NoSQL internals: Distributed Hash Tables, chord, consistent hashing
6) Scalable security: integrity of big data sets in the cloud, consistency and scalability issues with authenticated data structures, pipelining, blockchain scalability trilemma.
( reference books)
Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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DA LOZZO GIORDANO
( syllabus)
1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
- Number of distinct elements
2) Dimensionality reduction
-Johnson–Lindenstrauss lemma
Embedding metric spaces with low distortion
3) Algorithms and data structures for quantitative features analysis
- orthogonal range searching (kd-trees and range trees)
- nearest neighbour search, k-nearest neighbour search
- fractional cascading and simplex range search
4) Algorithms for the decomposition of complex networks
- Decomposition into k-connected components
- Decomposition into k-cores, maximal cliques, maximal k-plexes
5) NoSQL internals: Distributed Hash Tables, chord, consistent hashing
6) Scalable security: integrity of big data sets in the cloud, consistency and scalability issues with authenticated data structures, pipelining, blockchain scalability trilemma.
( reference books)
Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/
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6
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ING-INF/05
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54
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Core compulsory activities
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ITA |
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20810223 -
INGEGNERIA DEI DATI
(objectives)
Providing skills on systems, methods, and technologies for extraction, cleaning, analyzing and integrating and management of unstructured data.
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6
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ING-INF/05
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54
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Core compulsory activities
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ITA |
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20810261 -
Computer Graphics
(objectives)
This course aims at illustrating the modern software and hardware computer graphics architectures, and at providing mathematical, technical and methodological solutions for the development of projects concerning the visualization of data in 2D or 3D. The course will expose base concepts in computer graphics such as spaces, curves, surfaces and volumes, focusing on notions and algorithms currently used in scientific visualization, videogames, and computer animation. Moreover, this course aims at exposing details of hardware and software platforms currently in use.
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MILICCHIO FRANCO
( syllabus)
Hardware architectures; Software architectures; OpenGL, Vulkan; CUDA and OpenCL; Curves, surfaces, volumes; Algorithms for computer graphics and animation; Base concepts of physics-based animation.
( reference books)
Official documentations:
- Unity Game Engine
- Unreal Game Engine
- Vulkan
- DirectX
- OpenGL
- CUDA, OpenCL
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6
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ING-INF/05
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54
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Core compulsory activities
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20802136 -
CYBER PHYSICAL SYSTEMS
(objectives)
Building effective CPS of the future require multi-disciplinary skills. In particular, the confluence of real-time computing, wireless sensor networks, control theory, signal processing and embedded systems are required to create these new systems. This course will cover some basic material from these areas, but focus on advanced research papers related to CPS.
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6
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ING-INF/04
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54
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Core compulsory activities
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ITA |
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20810262 -
Deep Learning
(objectives)
Provide advanced and specific skills in Deep neural networks. The course consists of a theoretical part on the fundamental concepts, and laboratory activities in which these concepts are applied and developed through a software framework. At the end of the course the student will be able to: adequately train and optimize Deep neural networks; distinguish between different solutions and be able to choose and customize the most effective architectures in real-world scenarios, supervised, unsupervised or following a reinforcement learning approach.
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GASPARETTI FABIO
( syllabus)
Introduction to DL; Training of Deep Architecture: hyperparameter tuning, batch normalization, faster optimizers, regularization ; Convolutional Neural Networks (CNN/ConvNets); Recurrent Neural Networks (GRU, LSTM, Bidirectional); Encoder-Decoder, Autoencoders, Variational Autoencoders; Attention layers ; Generative Adversarial Networks (GAN); Deep Reinforcement Learning; Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Use cases: Computer Vision and NLP
( reference books)
Simon J.D. Prince. "Understanding Deep Learning". MIT Press Dec 5th 2023 A. Geron,
“Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”, O'Reilly Media, Inc, USA, 2019.
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6
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ING-INF/05
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54
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Core compulsory activities
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ITA |
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20810263 -
Logica
(objectives)
The course aims at giving basic knowledge of classical and some non-classical logics, their formal semantics and proof systems. Students will acquire the capability to use the studied logics for representation purposes and will be presented with some important applications of logic in computer science.
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6
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ING-INF/05
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54
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Core compulsory activities
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ITA |
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Università Roma Tre