Sap Solution Manager Key Generator Rar
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Sap Solution Manager Key Generator Rar
Intel Secure Key, code-named Bull Mountain Technology, is the Intel name for the Intel 64 and IA-32 Architectures instructions RDRAND and RDSEED and the underlying Digital Random Number Generator (DRNG) hardware implementation. Among other things, the DRNG using the RDRAND instruction is useful for generating high-quality keys for cryptographic protocols, and the RSEED instruction is provided for seeding software-based pseudorandom number generators (PRNGs)
The Digital Random Number Generator, using the RDRAND instruction, is an innovative hardware approach to high-quality, high-performance entropy and random number generation. To understand how it differs from existing RNG solutions, this section details some of the basic concepts underlying random number generation.
PRNG researchers have worked to solve this problem by creating what are known as Cryptographically Secure PRNGs (CSPRNGs). Various techniques have been invented in this domain, for example, applying a cryptographic hash to a sequence of consecutive integers, using a block cipher to encrypt a sequence of consecutive integers ("counter mode"), and XORing a stream of PRNG-generated numbers with plaintext ("stream cipher"). Such approaches improve the problem of inferring a PRNG and its state by greatly increasing its computational complexity, but the resulting values may or may not exhibit the correct statistical properties (i.e., independence, uniform distribution) needed for a robust random number generator. Furthermore, an attacker could discover any deterministic algorithm by various means (e.g., disassemblers, sophisticated memory attacks, a disgruntled employee). Even more common, attackers may discover or infer PRNG seeding by narrowing its range of possible values or snooping memory in some manner. Once the deterministic algorithm and its seed is known, the attacker may be able to predict each and every random number generated, both past and future.
With respect to the RNG taxonomy discussed above, the DRNG follows the cascade construction RNG model, using a processor resident entropy source to repeatedly seed a hardware-implemented CSPRNG. Unlike software approaches, it includes a high-quality entropy source implementation that can be sampled quickly to repeatedly seed the CSPRNG with high-quality entropy. Furthermore, it represents a self-contained hardware module that is isolated from software attacks on its internal state. The result is a solution that achieves RNG objectives with considerable robustness: statistical quality (independence, uniform distribution), highly unpredictable random number sequences, high performance, and protection against attack.
The use of RDRAND and RDSEED leverages a variety of cryptographic standards to ensure the robustness of its implementation and to provide transparency in its manner of operation. These include NIST SP800-90A, B, and C, FIPS-140-2, and ANSI X9.82. Compliance to these standards makes the Digital Random Number Generation a viable solution for highly regulated application domains in government and commerce.
Closely related are government and industry applications. Due to information sensitivity, many such applications must demonstrate their compliance with security standards like FISMA, HIPPA, PCIAA, etc. RDRAND has been engineered to meet existing security standards like NIST SP800-90, FIPS 140-2, and ANSI X9.82, and thus provides an underlying RNG solution that can be leveraged in demonstrating compliance with information security standards.
The role of the deterministic random bit generator (DRBG) is to "spread" a conditioned entropy sample into a large set of random values, thus increasing the amount of random numbers available by the hardware module. This is done by employing a standards-compliant DRBG and continuously reseeding it with the conditioned entropy samples.
The role of the enhanced non-deterministic random number generator is to make conditioned entropy samples directly available to software for use as seeds to other software-based DRBGs. Values coming out of the ENRNG have multiplicative brute-force prediction resistance, which means that samples can be concatenated and the brute-force prediction resistance will scale with them. When two 64-bit samples are concatenated together, the resulting 128-bit value will have 128 bits of brute-force prediction resistance (264 * 264 = 2128). This operation can be repeated indefinitely and can be used to easily produce random seeds of arbitrary size. Because of this property, these values can be used to seed a DRBG of any size.
The DRNG is implemented in hardware as part of the Intel processor. As such, both the entropy source and the DRBG execute at processor clock speeds. Unlike other hardware-based solutions, there is no system I/O required to obtain entropy samples and no off-processor bus latencies to slow entropy transfer or create bottlenecks when multiple requests have been issued.
There are two certifications relevant to the Digital Random Number Generator (DRNG): the Cryptographic Algorithm Validation System (CAVS) and Federal Information Processing Standards (FIPS). FIPS provides specifications for cryptographic modules, and mandates that Random Number Generator (RNG) solutions in FIPS-certified crypto modules must have SP800-90 CAVS certification. The DRNG is not a FIPS cryptographic module: it is an SP800-90 compliant RNG solution which can be CAVS certified, and thus permitted as a component of a FIPS-certified cryptographic module. CAVS certifications for SP800-90 compliant solutions must be obtained per product, which means processor generations must be individually certified.
The current National Institute of Standards and Technology (NIST) model for certifying a cryptographic solution which uses the DRNG necessitates a partnership between Intel and the cryptographic module provider to produce entropy justification documents on a per-product basis.
As described in section 3.2.3, the DRNG uses a deterministic random bit generator, or DRBG, to "spread" a conditioned entropy sample into a large set of random values, thus increasing the number of random numbers available by the hardware module. The DRBG autonomously decides when it needs to be reseeded, behaving in a way that is unpredictable and transparent to the RDRAND caller. There is an upper bound of 511 samples per seed in the implementation where samples are 128 bits in size and can provide two 64-bit random numbers each. In practice, the DRBG is reseeded frequently, and it is generally the case that reseeding occurs long before the maximum number of samples can be requested by RDRAND.
Intel Data Protection Technology with Secure Key represents a new class of random number generator. It combines a high-quality entropy source with a CSPRNG into a robust, self-contained hardware module that is isolated from software attacks. The resulting random numbers offer excellent statistical qualities, highly unpredictable random sequences, and high performance. Accessible via two simple instructions, RDRAND and RDSEED, the random number generator is also very easy to use. Random numbers are available to software running at all privilege levels, and requires no special libraries or operating system handling.
Both of these solutions can come at a huge cost, both in terms of end-user trust, support costs,as well as engineering resources. Suffice it to say, it's a bad situation. And ideally, what wewant to do is avoid the situation entirely, by choosing a modern, secure license keyalgorithm from the get-go.
The other major attack vector is known as a software "keygen", which is much more ominous. Asits name may imply, a keygen is a form of software, often a separate program or webpage, thatgenerates valid license keys, i.e. a key-generator, or "keygen."
Most software vendors have some type of license keygen, which they keep secret. For example, aftera user submits a successful purchase order, part of the order process calls a key generator, whichgenerates a valid, legitimate license key for the new customer.
WPA and WPA2: WPA was introduced as a temporary solution for the devices that did not support WPA2. WPA has now been broken and depreciated. The WPA2 is considered to be the most secure to date. The tools discussed further in the article will also cover details on how to attack WPA and WPA2 but the success of an attack depends on the time and the computing power.
As the name suggests the tool is able to hijack the air i.e. wireless. The tool is able to receive and inject raw packets into the wireless network. It can be used by the developers to tweak the packets and inject it to develop the solution or by wireless hackers. A wireless hacker is able to perform a denial of service attacks by flooding the network with dirty injected packets.
Cloudcracker is a cloud-based solution for cracking the passwords of various utilities. The tool uses dictionary-based attacks to crack the passwords. The size of the dictionary ranges up to 10 digits. Just upload the handshake file along with a few other details and you are all set.
WepDecrypt is written in the C language and is one of the most effective wireless hacking tools for guessing WEP keys by performing key generator, distributed network attack and dictionary attack. WebDecrypt is a great tool for beginners and requires a few directories to function seamlessly.
Getting an idea of these wireless hacking solutions will make you a better security wireless expert, cybersecurity expert, or network security professional. This will help you in configuring your wifi networks properly and refrain from falling into the trap of similar network security hazards.
A password manager is a tool that does the work of creating, remembering and filling in passwords. Simply log into an online account for the first time and LastPass will store your username and password so every time you go back your credentials will be filled in automatically. 350c69d7ab