1 |
G. Ruffo and F. Bergadano, Enfilter: a password enforcement and filter tool based on pattern recognition techniques, in Proc. Int. Conf. Image Analysis Process., Cagliari, Italy, Sept. 2005, pp. 75-82.
|
2 |
S. Y. Ooi, S. C. Tan, and C. W. Ping, Anomaly Based Intrusion Detection through Temporal Classification, in Proc. Int. Conf. Neural Inf. Process., Kuching, Malaysia, Nov. 2014, pp. 612-619.
|
3 |
J. Kim et al., A lightweight network anomaly detection technique, in Int. Conf. Comput., Netw. Commun.(ICNC), Santa Clara, CA, USA, Jan. 2017, pp. 1-5.
|
4 |
G. Wang, J. Yang, and R. Li, Imbalanced SVM based anomaly detection algorithm for imbalanced training datasets, ETRI J. 39 (2017), no. 5, 621-631.
DOI
|
5 |
P. Parrend et al., Foundations and applications of artificial Intelligence for zero-day and multi-step attack detection, EURASIP J. Inf. Security 4 (2018), 1-21.
|
6 |
F. Maggi et al., Investigating web defacement campaigns at large, in Proc. Asia Conf. Comput. Commun. Security, Incheon, Rep. of Korea, June 2018, pp. 443-456.
|
7 |
C. Shen et al., Touch-interaction behavior for continuous user authentication on smartphones, in Proc. IEEE Int. Conf. Biometrics, Phuket, Thailand, May 2015, pp. 157-162.
|
8 |
M. Kearns and M. Li, Learning in the presence of malicious errors, SIAM J. Comput. 22 (1993), no. 4, 807-837.
DOI
|
9 |
F. Bergadano, D. Gunetti, and C. Picardi, Identity verification through dynamic keystroke analysis, Intell. Data Analysis J. 7 (2003), no. 5, 469-496.
DOI
|
10 |
D. Lowd and C. Meek, Adversarial Learning, in Proc. ACM Conf. Knowledge Discovery Data Mining, ACM, Chicago, IL, UDS, Aug. 2005, pp. 641-647.
|
11 |
L. Huang et al., Adversarial machine learning, in Proc. ACM Workshop Security Artif. Intell., Chicago, IL, USA, Oct. 2011, pp. 43-58.
|
12 |
R. Bendale et al., KIDS: Keyed Anomaly Detection System, Int. J. Adv. Eng. Res. Dev. 12 (2017), 312-325.
|
13 |
N. Srndic and P. Laskov, Practical evasion of a learning-based classifier: A case study, in Proc. IEEE Symp.Security Privacy, San Jose, CA, USA, May 2014, pp. 197-211.
|
14 |
J. E. Tapiador et al., Key-recovery attacks on KIDS, a keyed anomaly detection system, IEEE Trans. Dependable Secure Comput. 12 (2015), no. 3, 312-325.
DOI
|
15 |
C. Aggarwal, J. Pei, and B. Zhang, On privacy preservation against adversarial data mining, in Proc. ACM SIGKDD Int. Conf. Knowled. Discovery Data Mining, ACM, Philadelphia, PA, USA, Aug. 2006, pp. 510-516.
|
16 |
H. Xiao et al., Is feature selection secure against training data poisoning? in Proc. Int. Conf. Mach. Learn., Lille, France, July 2015, pp. 1689-1698.
|
17 |
F. Bergadano and A. Giordana, A knowledge intensive Approach to concept induction, in Proc. Fifth Int. Conf. Mach. Learn., Margan Kaufmann Publishers, Ann Arbor, MI, USA, June 1988, pp. 305-317.
|
18 |
T. Dierks and E. Rescorla, The Transport Layer Security (TLS) Protocol Version 1.2, RFC 5246, IETF, 2008, https://doi.org/10.17487/rfc5246.
DOI
|
19 |
J. Arkkom et al., MIKEY: Multimedia Internet KEYing, RFC3820, IETF, 2004, https://doi.org/10.6028/NIST.SP.800-108.
DOI
|
20 |
L. Chen, Recommendation for Key Derivation Using Pseudorandom Functions, NIST Special Publication 800-108, NIST, 2009.
|
21 |
Y. Vorobeychik and B. Li, Optimal randomized classification in adversarial settings, in Proc. Conf. Autonomous Agents Multiagent Syst., Paris, France, May 2014, pp. 485-492.
|
22 |
K. Wang, J. Parekh, and S. Stolfo, Anagram: a Content Anomaly Detector Resistant to Mimicry Attack, in Proc. Int. Conf. Recent Adv. Intrusion Detection, Springer, Hamburg, Germany, Sept. 2005, pp. 226-248.
|
23 |
S. Rota Bulo et al., Randomized prediction games for adversarial machine, learning, IEEE Trans. Neural Netw. Learn. Syst. 28 (2017), no. 11, 2466-2478.
DOI
|
24 |
M. Bellare, R. Canetti, and H. Krawczyk, Keying hash functions for message authentication, in Proc. Auun. Int. Cryptology Conf., Santa Barbara, CA, USA, Sug. 1996, pp. 1-15.
|
25 |
M. Barreno et al., Can machine learning be secure? in Proc. ACM Symp. Inf., Comput. Commun. Security (AsiaCCS), ACM, Taipei, Taiwan, Mar. 2006, pp. 16-25.
|
26 |
R. S. Mrdovic and B. Drazenovic, KIDS: a Keyed Intrusion Detection System, in Proc. Int. Conf. Detection Intrusions Malware, Vulnerability Assessment (DIMVA), IEEE, Bonn, Germany, July 2010, pp. 173-182.
|
27 |
B. Biggio, G. Fumera, and F. Roli, Adversarial pattern classification using multiple classifiers and randomization, in Proc. Joint IAPR Int. Workshop Structural, Syntactic, Statistical Pattern Recogn., Springer, Orlando, FL, USA, Dec. 2008, pp. 500-509.
|
28 |
V. M. Lomte and D. Patil, Survey on keyed IDS and key recovery attacks, Int. J. Sci. Research 4 (2015), no. 12, 846-849.
|
29 |
R. Perdisci et al., McPAD: a multiple classifier system for accurate payload-based anomaly detection, Comput. Netw. 53 (2009), no. 6, 864-881.
DOI
|
30 |
F. Bergadano et al., Defacement response via keyed learning, in Proc. Int. Conf. Inf. Intell. Syst. Applicat., Larnaca, Cyprus, Aug. 2017, pp. 1-6.
|
31 |
G. Davanzo, E. Medvet, and A. Bartoli, Anomaly detection techniques for a web defacement monitoring service, Expert Syst. Applicat. 38 (2011), no. 10, 12521-12530.
DOI
|
32 |
K. Scarfone, W. Jansenamd, and M. Tracy, Guide to General Server Security, Section 2.4, Special Publication 800-123, NIST, Gaithersburg, MD, 2008.
|
33 |
Y. Bae, I. Kim, and S. O. Hwang, An efficient detection of TCP Syn flood attacks with spoofed IP addresses, J. Intell. Fuzzy Syst. 35 (2018), no. 6, 5983-5991.
DOI
|
34 |
N. Munaiah et al., Are Intrusion Detection Studies Evaluated Consistently? A Systematic Literature Review, Technical Report, University of Rochester, 2016.
|
35 |
S. Anwar et al., From intrusion detection to an intrusion response system: fundamentals, requirements, and future directions, MDPI Algorithms J. 10 (2017), no. 39, 1-24.
|
36 |
S. Kim and B. B. Kang, FriSM: malicious exploit kit detection via feature-based string-similarity matching, in Proc. Int. Conf. Security Privacy Commun. Netw., Singapore, Aug. 2018, pp. 416-432.
|
37 |
Q. T. Hai and S. O. Hwang, An efficient classification of malware behavior using deep neural network, J. Intell. Fuzzy Syst. 35 (2018), no. 6, 5801-5814.
DOI
|
38 |
K. Borgolte, C. Kruegel, and G. Vigna, Meerkat: Detecting website defacements through image-based object recognition, in Proc. USENIX Security Symp., Washington, D.C., USA, Aug. 2015, pp. 595-610.
|
39 |
A. Bartoli, G. Davanzo, and E. Medvet, A framework for largescale detection of web site defacements, ACM Trans. Internet Technol. 10 (2010), no. 3, 1-3.
|
40 |
X. Liao et al., Seeking nonsense, looking for trouble: efficient promotional-infection detection through semantic inconsistency search, in Proc. IEEE Symp. Security Privacy, San Jose, CA, USA, May 2016, pp. 707-723.
|
41 |
A. Basso and F. Bergadano, Anti-bot strategies based on human interactive proofs, in Handbook of Information and Communication Security, Springer, New York, 2010, pp. 273-291.
|