Abstract: In one example in accordance with the present disclosure, a method for domain classification includes sorting a set of sample domains into leaves based on syntactical features of the domains. Each sample domain belongs to a family of domains. The method also includes identifying, for each leaf, a regular expression for each family with at least one domain in the leaf. The method also includes determining, for each leaf, at least one lobe with a set of domains in the leaf that matches the regular expression for a first family with at least one domain in the leaf, and that does not match the regular expression for the other families with at least one domain in the leaf. The method also includes creating a classifier for the domains in each lobe by using the set of domains from each family in the lobe as training classes for machine learning.

Abstract: Examples determine a number of hosts, within an enterprise, which are resolving a particular domain. Based on the number of hosts within the enterprise resolving the particular domain, the examples identify whether the particular domain is benign.

Abstract: Examples determine a number of hosts, within an enterprise, which are resolving a particular domain. Based on the number of hosts within the enterprise resolving the particular domain, the examples identify whether the particular domain is benign.

Abstract: In one example in accordance with the present disclosure, a method for domain classification includes sorting a set of sample domains into leaves based on syntactical features of the domains. Each sample domain belongs to a family of domains. The method also includes identifying, for each leaf, a regular expression for each family with at least one domain in the leaf. The method also includes determining, for each leaf, at least one lobe with a set of domains in the leaf that matches the regular expression for a first family with at least one domain in the leaf, and that does not match the regular expression for the other families with at least one domain in the leaf. The method also includes creating a classifier for the domains in each lobe by using the set of domains from each family in the lobe as training classes for machine learning.

Abstract: A method for submatch extraction may include receiving an input string, receiving a regular expression, and converting the regular expression with capturing groups into a plurality of finite automata to extract submatches. The method further includes using a first automaton to determine whether the input string is in a language described by the regular expression, and to process the input string, and using states of the first automaton in a second automaton to extract the submatches.

Abstract: A method for submatch extraction may include receiving an input string, receiving a regular expression, and converting the regular expression with capturing groups into a plurality of finite automata to extract submatches. The method further includes using a first automaton to determine whether the input string is in a language described by the regular expression, and to process the input string, and using states of the first automaton in a second automaton to extract the submatches.

Abstract: A method for one pass submatch extraction may include receiving an input string, receiving a regular expression with capturing groups, and converting the regular expression with capturing groups into a finite automaton M to extract submatches. The finite automaton M may be evaluated to determine whether the regular expression belongs to a set of regular expressions for which submatch extraction is implemented by using one pass by determining whether an automaton M?=rev(close(M)) is deterministic. The input string may be matched to the regular expression if the regular expression belongs to the set of regular expressions for which submatch extraction is implemented by using one pass.

Abstract: According to an example, a method for matching regular expressions including word boundary symbols includes receiving an input string and receiving a regular expression including a word boundary symbol. The method further includes transforming, by a processor, the regular expression into an automaton such that a set of strings accepted by the automaton is the same as a set of strings described by the regular expression. The method also includes processing the input string by the automaton to determine if the input string matches the regular expression.

Abstract: Embodiments disclosed herein relate to identifying potential account numbers in a single pass of a list of digits. A processor 104 may determine in a single pass of a list of digits each consecutive sequence of digits of a selected length satisfying a Luhn check. The processor 104 may provide the determined sequences of digits as potential account numbers.

Abstract: A data packet is transmitted by dividing it into sub-packets, for example by distributing successive bytes of the data packet to different sub-packets each containing at most p.sup.n -1 symbols, where p is a prime number, and transmitting the sub-packets along two or more respective paths. CRC checksums are added to the sub-packets, the checksum for each path being generated using a different and respective generator polynomial of degree b. These generator polynomials are selected so that, for arithmetic carried out modulo p, each polynomial has a respective factor of degree at least b-n+1, and the collection of polynomials which are each exactly divisible by all such factors constitutes a BCH code. As a result the system has advantageous properties in respect of error detection and implementation.

Abstract: Data are transmitted in blocks over a channel together with control signals for coordinating access to the channel. The control signals have the same values as some of the data blocks, these values being selected so that the control signals are spaced throughout the population of data block values and so that there is a greater Hamming distance between the control signal values than between the data signals. The control signals are transmitted between delimiter signals which differ from any of the data block signals, and are passed through a scrambling and encoding system in the same way as data signals, so that the spectrum of radiated electromagnetic emissions from the channel is not affected by the control signals.