RICERCA ITALIANA     

Programmi di ricerca simili:

• 1 - MIsure sperimentali e MOdelli di traffico dati multiServizio a pAcchetto (MIMOSA)
• 2 - Future applicazioni del paradigma peer-to-peer
• 3 - NADIR: progettazioNe e valutazione delle prestazioni di protocolli e Algoritmi DIstribuiti per Reti mesh con qualità del servizio
• 4 - Web Ram: web retrieval and mining
• 5 - Metodi e strumenti per la migrazione di sistemi software verso architetture web e orientate ai servizi: valutazione sperimentale, usabilita' e trasferimento tecnologico
• 6 - SESAME (Scalable Efficient Secure Autonomic MEsh networks)
• 7 - Gli effetti della povertà e dell'informazione sul traffico di persone nei paesi in via di sviluppo
• 8 - Metodi basati sulla similarita' per la visione artificiale e il riconoscimento delle forme: Teoria, algoritmi, applicazioni
• 9 - Modelli ed algoritmi per l'ottimizzazione robusta delle reti
• 10 - flexible SOftware Router PlAtform for Secure Service-specific Overlay networks (SORPASSO)

Classificazione scientifico-disciplinare

• Area scientifico disciplinare: Ingegneria industriale e dell'informazione
  • TELECOMUNICAZIONI
  • SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI

Classificazione brevettuale

• ELECTRICITY
  • ELECTRIC COMMUNICATION TECHNIQUE
    • TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (typewriters B41J; order telegraphs, fire or police telegraphs G08B; visual telegraphy G08B, C; teleautographic systems G08C; ciphering or deciphering apparatus per se G09C; coding, decoding or code conversion, in general H03M; arrangements common to telegraphic and telephonic communication H04M; selecting H04Q)
• PHYSICS
  • COMPUTING; CALCULATING; COUNTING (score computers for games A63; combinations of writing applicances with computing devices B43K29/08)
    • ELECTRICAL DIGITAL DATA PROCESSING (computers in which a part of the computation is effected hydraulically or pneumatically G06D; optically G06E; self-contained input or output peripheral equipment G06K; impedance networks using digital techniques H03H) [C9603]
  • SIGNALLING (indicating or display devices per se G09F; transmission of pictures H04N) [C9504]
    • TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; disposition of road signs or traffic signals E01F9/00; radar systems or analogous systems G01S13/00, G01S15/00, G01S17/00)

Classificazione geografica

• Regione: Campania

Bibliografia

[1] D. Moore, K. Keys, R. Koga, E. Lagache, K.C. Claffy, The CoralReef Software Suite as a Tool for System and Network Administrators, In LISA ’01: Proc. of the 15th USENIX Conference on System Administration, pp. 133–144, San Diego, CA, USA, Dec. 2001.
[2] V. Paxson, BRO: a system for detecting network intruders in real-time, in Proc. of the 7th USENIX Security Symposium, San Antonio, TX, USA, Jan. 1998.
[3] M. Roesch, SNORT: Lightweight Intrusion Detection for Networks, In LISA ’99: Proc. of the 13th Conference on Systems Administration, pp. 229–238, Seattle, WA, USA, Nov. 1999.
[4] A. W. Moore, K. Papagiannaki, Toward the Accurate Identification of Network Applications, In Proc. of the 6th Passive and Active Measurement Workshop, pp. 41-54, Boston, MA, USA, Oct. 2005.
[5] Y. Guan, X. Fu, D. Xuan, P.U. Shenoy, R. Bettati, W. Zhao, NetCamo: camouflaging network traffic for QoS-guaranteed missioncritical applications, IEEE Trans. on SMC-Part A, 31, (4): 253-265, 2001.
[6] V. Paxson, Empirically derived analytic models of wide-area TCP connections, IEEE/ACM Trans. Netw., 2(4):316–336, 1994.
[7] V. Paxson, S. Floyd, Wide area traffic: the failure of Poisson modeling, IEEE/ACM Trans. Netw., 3(3):226–244, 1995.
[8] A. Mena, J. Heidemann, An Empirical Study of Real Audio Traffic, In Proc. of the IEEE Infocom, pp. 101–110, Tel-Aviv, Israel, Mar. 2000.
[9] C. Dewes, A. Wichmann, A. Feldmann, An analysis of Internet chat systems, In IMC ’03: Proc. of the 3rd ACM SIGCOMM Conference on Internet measurement, pp. 51–64, Miami Beach, FL, USA, Oct. 2003.
[10] F. Hernández-Campos, F.D. Smith, K. Jeffay, A.B. Nobel, Statistical Clustering of Internet Communications Patterns, In Computing Science and Statistics, volume 35, Jul. 2003.
[11] A. McGregor, M. Hall, P. Lorier, J. Brunskill. Flow Clustering Using Machine Learning Techniques, In Proc. of the 5th Passive and Active Measurement Workshop, pp. 205-214, Antibes Juan-les-Pins, France, Mar. 2004.
[12] M. Roughan, S. Sen, O. Spatscheck, N. Duffield, Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification, In IMC ’04: Proc. of the 4th ACM SIGCOMM conference on Internet measurement, pp. 135–148, Taormina, Italy, Oct. 2004.
[13] C. Trivedi, H.J. Trussel, A. Nilsson, M.-Y. Chow, Implicit Traffic Classification for Service Differentiation, Technical report, ITC Specialist Seminar, Wurzburg, Germany, Jul. 2002.
[14] T. Karagiannis, K. Papagiannaki, M. Faloutsos, BLINC: multilevel traffic classification in the dark, In SIGCOMM’05: Proc. of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications, pp. 229–240, Philadelphia, PA, USA, Aug. 2005.
[15] J. Xu, J. Fan, M.H. Ammar, S.B. Moon, Prefix-Preserving IP Address Anonymization: Measurment-Based Security Evaluation and a New Cryptography-Based Scheme, In Proc. of the 10th IEEE International Conference on Network Protocols, pp. 280-289, Paris, France, Nov. 2002.
[16] T. Kohno, A. Broido, K.C. Claffy, Remote Physical Device Fingerprinting, In Proc. of the IEEE Symposium on Security and Privacy, pp. 211-225, Oakland, CA, USA, May 2005.
[17] R. Pang, M. Allman, V. Paxson, J. Lee, The Devil and Packet Trace Anonymization, ACM SIGCOMM Computer Communication Review, 36(1): 29–38, 2006.
[18] Force10 Networks, P-series Datasheet, available online at http://www.force10networks.com/products/p-series_overview.asp.
[19] H. Song, T. Sproull, M. Attig, J. Lockwood, Snort Offloader: A Reconfigurable Hardware NIDS Filter in Proc. of 15th International Conference on Field Programmable Logic and Applications, Tampere, Finland, Aug. 24-26, 2005.
[20] D. Denning. An Intrusion-Detection Model. IEEE Trans. on Software Engineering. Feb. 1987.
[21] A.B. Tucker Jr., editor. CRC Computer Science and Engineering Handbook. CRC Press, Dec. 1996.
[22] T. Ye, S. Kalyanaraman, D. Harrison, B. Sikdar, B.Mo, H.T. Kaur, K. Vastola, B. Szymanski, Network management and control using collaborative on-line simulation, in Proc. CNDSMS, 2000.
[23] I. Norros, A storage model with self-similar input, Queueing Syst., vol. 16, pp. 387–396, 1994.
[24] R.H. Reidi, M.S. Crouse, V.J. Ribeiro, R.G. Baraniuk, A multi-fractal wavelet model with application to network traffic, IEEE Trans. Inform. Theory, vol. 45, pp. 992–1018, Mar. 1999.
[25] A.C. Gilbert, W. Willinger, A. Feldmann, Scaling analysis of con-servative cascades with applications to network traffic, IEEE Trans. Inform. Theory, vol. 45, pp. 971–991, Mar. 1999.
[26] J. Jung, V. Paxon, A Berger, H. Balakrishnan. Fast Portscan Detection Using Sequential Hypothesis Testing. In IEEE Symposium in Security and Privacy, May 2004.
[27] H-A Kim, H-J Kang, S-C Hung, S-H Chung, J.W. Hong, A Flow Based Method for Abnormal Traffic Detection, In IEEE/IFIP Network Operations and Management Symposium. Seoul, Apr. 2004.
[28] H. Wang, D. Zhang, and K. G. Shin, Detecting syn flooding attacks, in Proc. IEEE INFOCOM, 2002.
[29] S. Savage, D. Wetherall, A.R. Karlin, T. Anderson, Practical network support for IP traceback, in Proc. ACM SIGCOMM, 2000, pp. 295–306.
[30] A. Lakhina, M. Crovella, C. Diot, Characterization of Network-Wide Traffic Anomalies. In ACM Sigcomm, Portland, Aug. 2004.
[31] A. Lakhina, M. Crovella, C. Diot, Mining Anomalies Using Traffic Feature Distributions. In ACM Sigcomm. Philadelphia, Aug. 2005.
[32] C. Kruegel, F. Valeur, G. Vigna. Intrusion Detection and Correlation. Springer, 2005.
[33] S. Axelsson, The Base-Rate Fallacy and the Difficulty of Intrusion Detection, ACM Trans. on Information and System Security, vol. 3, no.3, pp. 186-205, 2000.
[34] I. Finizio, C. Mazzariello, C. Sansone, Combining Genetic-Based Misuse and Anomaly Detection for Reliably Detecting Intrusions in Computer Networks, LNCS, vol. 3617, Springer, pp. 66-74, 2005.
[35] G. Giacinto, F. Roli, L. Didaci, Fusion of multiple classifiers for intrusion detection in computer networks, Pattern Recognition Letters, vol. 24, pp. 1795-1803, 2003.
[36] Y. Liu, K. Chen, X. Liao, W. Zhang, A genetic clustering method for intrusion detection, Pattern Recognition vol. 37, 2004.
[37] A.K. Ghosh, A. Schwartzbard, A Study in Using Neural Networks for Anomaly and Misuse Detection. Proc. USENIX Security Symposium, Washington, USA, Aug. 23-26, 1999.
[38] M. Fugate, J.R. Gattiker, Computer Intrusion Detection with Classification and Anomaly Detection, using SVMs, Int. J. of Pattern Recogn. and Artif. Intell., vol. 17, no. 3, pp. 441-458, 2003.
[39] L.P. Cordella, A. Limongiello, C. Sansone, Network Intrusion Detection by a Multi Stage Classification System, LNCS vol. 3077, Springer, pp. 324-333, 2004.
[40] M. Mellia, TCP Statistic and Analysis Tool home page, http://tstat.tlc.polito.it
[41] M. Mellia, R. Lo Cigno, F. Neri, Measuring IP and TCP behaviour on edge nodes with Tstat, Computer Networks (47), Vol. 1, pp. 1-21, Jan. 2005.
[42] M. Crotti, F. Gringoli, P. Pelosato, L. Salgarelli, A statistical approach to IP-level classification of network traffic, to appear in IEEE ICC’06.
[43] P. Giacomazzi, L. Musumeci, G. Verticale, Transport of TCP/IP traffic over assured forwarding IP differentiated services. IEEE Network, 17(5):18–28, Sept. 2003.
[44] V. Trecordi, G. Verticale, Per-flow delay performance in a FIFO scheduler fed by policed UDP sources. Elsevier Science Computer Communications, 23(4):309–316, Feb. 2000.

Parole Chiave

RETI IP, CLASSIFICAZIONE DEL TRAFFICO, MISURE DI TRAFFICO, RILEVAZIONE DI ANOMALIE, SISTEMI PER LA RILEVAZIONE DI INTRUSIONI, SISTEMI PER LA PREVENZIONE DELLE INTRUSIONI, SICUREZZA DELLE RETI, FUSIONE DI INFORMAZIONI