1. A steel alloy that provides a unique combination of strength, toughness, and ductility said alloy consisting essentially of, in weight percent
C
0.3-0.6
Mn
3.0-4.5
Si
1.0-2.0
Cr
0.6-2.5
Ni
0.6-5.0
Mo + \xbdW
Up to 0.5
Cu
0.3-1.0
Co
0.01 max.
V + 59Nb
0.1-0.5
Ti
0.025 max.
Al
0.025 max.
Ca
0.005 max.
N
\u20020.02 max.
and the balance is iron and the usual impurities, wherein said impurities include not more than about 0.03% phosphorus and not more than about 0.003% sulfur; and wherein
2\u2266(% Si+% Cu)(% V+(59)\xd7% Nb)\u226634.
2. The alloy as claimed in claim 1 wherein (Mo+\xbdW) is at least about 0.20%.
3. The alloy as claimed in claim 2 wherein 3.5\u2266(% Mo+% Cr)(% C)\u22667.5.
4. The alloy as claimed in claim 1 wherein 3.5\u2266% Mn+% Ni\u22668.
5. The alloy as claimed in claim 1 wherein 4.5\u2266(% Si+% Cu)(% V+(59)\xd7% Nb)\u226610.
6. The alloy as claimed in claim 1 which also contains 0.001-0.025% yttrium.
7. The alloy as claimed in claim 1 which also contains 0.001-0.01% magnesium.
8. A steel alloy that provides a unique combination of strength, toughness, and ductility said alloy consisting essentially of, in weight percent
C
0.30-0.45
Mn
3.5-4.5
Si
1.3-1.8
Cr
0.75-2.35
Ni
0.7-4.5
Mo + \xbdW
Up to 0.3
Cu
0.4-0.7
Co
0.01 max.
V + 59Nb
0.2-0.4
Ti
0.020 max.
Al
0.020 max.
Ca
0.002 max.
N
\u20020.02 max.
and the balance is iron and the usual impurities, wherein said impurities include not more than about 0.03% phosphorus and not more than about 0.003% sulfur; and wherein
4.5\u2266(% Si+% Cu)(% V+(59)\xd7% Nb)\u226610.
9. The alloy as claimed in claim 8 wherein (Mo+\xbdW) is at least about 0.20%.
10. The alloy as claimed in claim 9 wherein 3.5\u2266(% Mo+% Cr)(% C)\u22667.5.
11. The alloy as claimed in claim 8 wherein 3.5\u2266% Mn+% Ni\u22668.
12. The alloy as claimed in claim 8 wherein 4.5\u2266(% Si+% Cu)(% V+(59)\xd7% Nb)\u226610.
13. The alloy as claimed in claim 8 which also contains 0.001-0.025% yttrium.
14. The alloy as claimed in claim 8 which also contains 0.001-0.01% magnesium.
15. A steel alloy that provides a unique combination of strength, toughness, and ductility said alloy consisting essentially of, in weight percent
C
0.30-0.40
Mn
3.5-4.5
Si
1.3-1.7
Cr
\u20021.6-2.35
Ni
3.7-4.3
Mo + \xbdW
0.1 max.
Cu
0.4-0.6
Co
0.01 max.
V + 59Nb
0.30-0.40
Ti
0.020 max.
Al
0.020 max.
Ca
0.002 max.
N
\u20020.02 max.
and the balance is iron and the usual impurities, wherein said impurities include not more than about 0.025% phosphorus and not more than about 0.0025% sulfur; and wherein
(a) 4.5\u2266(% Si+% Cu)(% V+(59)\xd7% Nb)\u226610;
(b) 4.25\u2266(% Mo+% Cr)(% C)\u22667.5; and
(c) 3.5\u2266% Mn+% Ni\u22668.0.
16. The alloy as claimed in claim 15 which also contains 0.001-0.025% yttrium.
17. The alloy as claimed in claim 15 which also contains 0.001-0.01% magnesium.
18. A thin gauge article made from the alloy claimed in claim 15.
19. A shaped part made from the thin gauge article claimed in claim 18.
20. A steel alloy that provides a unique combination of strength, toughness, and ductility, said steel consisting essentially of, in weight percent
C
0.30-0.36
Mn
3.5-4.5
Si
1.3-1.7
Cr
0.75-1.5\u2002
Ni
0.7-2.5
Mo + \xbdW
0.15-0.25
Cu
0.4-0.6
Co
0.01 max.
V + 59Nb
0.20-0.30
Ti
0.020 max.
Al
0.020 max.
Ca
0.002 max.
N
\u20020.02 max.
and the balance is iron and the usual impurities, wherein said impurities include not more than about 0.025% phosphorus and not more than about 0.0025% sulfur; and wherein
2\u2266(% Si+% Cu)(% V+(59)\xd7% Nb)\u226634.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method for detecting a domain generation algorithm (DGA), comprising:
performing processing associated with clustering, utilizing a name-based features clustering module accessing information from an electronic database of NX domain information, the randomly generated domain names based on the similarity in the make-up of the randomly generated domain names;
performing processing associated with clustering, utilizing a graph clustering module, the randomly generated domain names based on the groups of assets that queried the randomly generated domain names;
performing processing associated with determining, utilizing a daily clustering correlation module and a temporal clustering correlation module, which clustered randomly generated domain names are highly correlated in daily use and in time; and
performing processing associated with determining the DGA that generated the clustered randomly generated domain names.
2. The method of claim 1, further comprising: performing processing associated with, determining, whether the DGA is known or unknown.
3. The method of claim 2, further comprising:
if the DGA is known, performing processing associated with modeling the DGA based on the DGA’s characterization; and
if the DGA is unknown, performing processing associated with modeling the DGA.
4. The method of claim 3, further comprising:
performing processing associated with providing a report about the DGA.
5. The method of claim 1, wherein assets within a monitored network that are compromised with DGA-based Dots are detected by analyzing streams of unsuccessful DNS resolutions.
6. The method of claim 1, wherein DNS traffic below the local recursive DNS server is monitored.
7. The method of claim 1, wherein assets infected by known DGA malware andor unknown DGA-based malware are found.
8. The method of claim 2, wherein identification of unknown DGA malware is used to train new models of DGA traffic without reverse engineering andor obtaining the unknown DGA malware.
9. A system for detecting a domain generation algorithm (DGA), comprising:
a processor configured for:
performing processing associated with clustering, utilizing a name-based features clustering module accessing information from an electronic database of NX domain information, the randomly generated domain names based on the similarity in the make-up of the randomly generated domain names;
performing processing associated with clustering, utilizing a graph clustering module, the randomly generated domain names based on the groups of assets that queried the randomly generated domain names;
performing processing associated with determining, utilizing a daily clustering correlation module and a temporal clustering correlation module, which clustered randomly generated domain names are highly correlated in daily use and in time; and
performing processing associated with determining the DGA that generated the clustered randomly generated domain names.
10. The method of claim 9, further comprising: performing processing associated with determining whether the DGA is known or unknown.
11. The method of claim 10, further comprising:
if the DGA is known, performing processing associated with modeling the DGA based on the DGA’s characterization; and
if the DGA is unknown, performing processing associated with modeling the DGA.
12. The method of claim 11,. further comprising:
performing processing associated with providing a report about the DGA.
13. The method of claim 9, wherein assets within a monitored network that are compromised with DGA-based bots are detected by analyzing streams of unsuccessful DNS resolutions.
14. The method of claim 9, wherein DNS traffic below the local recursive DNS server is monitored.
15. The method of claim 9, wherein assets infected by known DGA malware andor unknown DGA-based malware are found.
16. The method of claim 10, wherein identification of unknown DGA malware is used to train new models of DGA traffic without reverse engineering andor obtaining the unknown DGA malware.