A backdoor is a typically covert method of bypassing normal authentication or encryption in a computer, product, embedded device (e.g. a home router), or its embodiment (e.g. part of a cryptosystem, algorithm, chipset, or even a "homunculus computer"—a tiny computer-within-a-computer such as that found in Intel's AMT technology). Backdoors are most often used for securing remote access to a computer, or obtaining access to plaintext in cryptosystems. From there it may be used to gain access to privileged information like passwords, corrupt or delete data on hard drives, or transfer information within compromised networks. In the United States, the 1994 Communications Assistance for Law Enforcement Act forces internet providers to provide backdoors for government authorities. In 2024, the U.S. government realized that China had been tapping communications in the U.S. using that infrastructure for months, or perhaps longer; China recorded presidential candidate campaign office phone calls—including employees of the then-vice president of the nation, and of the candidates themselves. A backdoor may take the form of a hidden part of a program, a separate program (e.g. Back Orifice may subvert the system through a rootkit), code in the firmware of the hardware, or parts of an operating system such as Windows, for example, device drivers. Trojan horses can be used to create vulnerabilities in a device. A Trojan horse may appear to be an entirely legitimate program, but when executed, it triggers an activity that may install a backdoor. Although some are secretly installed, other backdoors are deliberate and widely known. These kinds of backdoors have "legitimate" uses such as providing the manufacturer with a way to restore user passwords. Many systems that store information within the cloud fail to create accurate security measures. If many systems are connected within the cloud, hackers can gain access to all other platforms through the most vulnerable system. Default passwords (or other default credentials) can function as backdoors if they are not changed by the user. Some debugging features can also act as backdoors if they are not removed in the release version. In 1993, the United States government attempted to deploy an encryption system, the Clipper chip, with an explicit backdoor for law enforcement and national security access. The chip was unsuccessful. Recent proposals to counter backdoors include creating a database of backdoors' triggers and then using neural networks to detect them. == Overview == The threat of backdoors surfaced when multiuser and networked operating systems became widely adopted. Petersen and Turn discussed computer subversion in a paper published in the proceedings of the 1967 AFIPS Conference. They noted a class of active infiltration attacks that use "trapdoor" entry points into the system to bypass security facilities and permit direct access to data. The use of the word trapdoor here clearly coincides with more recent definitions of a backdoor. However, since the advent of public key cryptography the term trapdoor has acquired a different meaning (see: Trapdoor function), and thus the term "backdoor" is now preferred, only after the term trapdoor went out of use. More generally, such security breaches were discussed at length in a RAND Corporation task force report published under DARPA sponsorship by J.P. Anderson and D.J. Edwards in 1970. While initially targeting the computer vision domain, backdoor attacks have expanded to encompass various other domains, including text, audio, ML-based computer-aided design, and ML-based wireless signal classification. Additionally, vulnerabilities in backdoors have been demonstrated in deep generative models, reinforcement learning (e.g., AI GO), and deep graph models. These broad-ranging potential risks have prompted concerns from national security agencies regarding their potentially disastrous consequences. A backdoor in a login system might take the form of a hard coded user and password combination which gives access to the system. An example of this sort of backdoor was used as a plot device in the 1983 film WarGames, in which the architect of the "WOPR" computer system had inserted a hardcoded password-less account which gave the user access to the system, and to undocumented parts of the system (in particular, a video game-like simulation mode and direct interaction with the artificial intelligence). Although the number of backdoors in systems using proprietary software (software whose source code is not publicly available) is not widely credited, they are nevertheless frequently exposed. Programmers have even succeeded in secretly installing large amounts of benign code as Easter eggs in programs, although such cases may involve official forbearance, if not actual permission. == Examples == === Worms === Many computer worms, such as Sobig and Mydoom, install a backdoor on the affected computer (generally a PC on broadband running Microsoft Windows and Microsoft Outlook). Such backdoors appear to be installed so that spammers can send junk e-mail from the infected machines. Others, such as the Sony/BMG rootkit, placed secretly on millions of music CDs through late 2005, are intended as DRM measures—and, in that case, as data-gathering agents, since both surreptitious programs they installed routinely contacted central servers. A sophisticated attempt to plant a backdoor in the Linux kernel, exposed in November 2003, added a small and subtle code change by subverting the revision control system. In this case, a two-line change appeared to check root access permissions of a caller to the sys_wait4 function, but because it used assignment = instead of equality checking ==, it actually granted permissions to the system. This difference is easily overlooked, and could even be interpreted as an accidental typographical error, rather than an intentional attack. In January 2014, a backdoor was discovered in certain Samsung Android products, like the Galaxy devices. The Samsung proprietary Android versions are fitted with a backdoor that provides remote access to the data stored on the device. In particular, the Samsung Android software that is in charge of handling the communications with the modem, using the Samsung IPC protocol, implements a class of requests known as remote file server (RFS) commands, that allows the backdoor operator to perform via modem remote I/O operations on the device hard disk or other storage. As the modem is running Samsung proprietary Android software, it is likely that it offers over-the-air remote control that could then be used to issue the RFS commands and thus to access the file system on the device. === Object code backdoors === Harder to detect backdoors involve modifying object code, rather than source code—object code is much harder to inspect, as it is designed to be machine-readable, not human-readable. These backdoors can be inserted either directly in the on-disk object code, or inserted at some point during compilation, assembly linking, or loading—in the latter case the backdoor never appears on disk, only in memory. Object code backdoors are difficult to detect by inspection of the object code, but are easily detected by simply checking for changes (differences), notably in length or in checksum, and in some cases can be detected or analyzed by disassembling the object code. Further, object code backdoors can be removed (assuming source code is available) by simply recompiling from source on a trusted system. Thus for such backdoors to avoid detection, all extant copies of a binary must be subverted, and any validation checksums must also be compromised, and source must be unavailable, to prevent recompilation. Alternatively, these other tools (length checks, diff, checksumming, disassemblers) can themselves be compromised to conceal the backdoor, for example detecting that the subverted binary is being checksummed and returning the expected value, not the actual value. To conceal these further subversions, the tools must also conceal the changes in themselves—for example, a subverted checksummer must also detect if it is checksumming itself (or other subverted tools) and return false values. This leads to extensive changes in the system and tools being needed to conceal a single change. As object code can be regenerated by recompiling (reassembling, relinking) the original source code, making a persistent object code backdoor (without modifying source code) requires subverting the compiler itself—so that when it detects that it is compiling the program under attack it inserts the backdoor—or alternatively the assembler, linker, or loader. As this requires subverting the compiler, this in turn can be fixed by recompiling the compiler, removing the backdoor insertion code. This defense can in turn be subverted by putting a source meta-backdoor in the compiler, so that when it detects that it is compiling itself
Clipmap
In computer graphics, clipmapping is a method of clipping a mipmap to a subset of data pertinent to the geometry being displayed. This is useful for loading as little data as possible when memory is limited, such as on a graphics processing unit. The technique is used for LODing in NVIDIA’s implementation of voxel cone tracing. The high-resolution levels of the mipmapped scene representation are clipped to a region near the camera, while lower resolution levels are clipped further away. == MegaTexture == MegaTexture is a clipmap implementation developed by id Software. It was introduced in their id Tech 4 engine and also appeared in id Tech 5 and id Tech 6 before being removed in id Tech 7. MegaTexture is a texture allocation technique that uses a single, extremely large texture rather than repeating multiple smaller textures. It is also featured in Splash Damage's game Enemy Territory: Quake Wars, and was developed by id Software former technical director John Carmack. MegaTexture employs a single large texture space for static terrain. The texture is stored on removable media or a computer's hard drive and streamed as needed, allowing large amounts of detail and variation over a large area with comparatively little RAM usage. Depending on the pixel resolution per square meter, covering a large area could require several gigabytes of memory. However, RAM is also filled by the rest of the game and the underlying operating system, limiting the amount available for texturing. As the player moves around the game, different sections of the MegaTexture are loaded into memory. They are then scaled to the correct size and applied to the 3D models of the terrain. Id has presented a more advanced technique that builds upon the MegaTexture idea and virtualizes both the geometry and the textures to obtain unique geometry down to the equivalent of the texel: the sparse voxel octree (SVO). It works by raycasting the geometry represented by voxels (instead of triangles) stored in an octree. The goal is to stream parts of the octree into video memory, going further down along the tree for nearby objects to give them more details, and to use higher level, larger voxels for farther objects, which give an automatic level of detail (LOD) system for both geometry and textures at the same time. The geometric detail that can be obtained using this method is nearly infinite, which removes the need for faking 3-dimensional details with techniques such as normal mapping. Despite that most voxel rendering tests use very large amounts of memory (up to several GB), Jon Olick of id Software claimed the technology is able to compress such SVO to 1.15 bits per voxel of position data. == Virtual texturing == Unlike clipmaps, which clip each mip level around a viewpoint-dependent clipcenter and therefore work best for terrain, virtual texturing preprocesses texture data into equally sized tiles that can be streamed for arbitrary textured geometry. Rage, powered by the id Tech 5 engine, uses a more advanced technique called virtual texturing. Textures can measure up to 128000×128000 pixels and are also used for in-game models and sprites, etc. and not just the terrain. Wolfenstein: The New Order and the 2016 version of Doom also use these. Carmageddon: Reincarnation also uses virtual texturing, though unlike id's virtual texturing system, which is designed for unique texture-mapping everywhere, their system is designed to use storage space sparingly while still offering good blend of texture variation and resolution.
Additive smoothing
In statistics, additive smoothing, also called Laplace smoothing or Lidstone smoothing, is a technique used to smooth count data, eliminating issues caused by certain values having 0 occurrences. Given a set of observation counts x = ⟨ x 1 , x 2 , … , x d ⟩ {\displaystyle \mathbf {x} =\langle x_{1},x_{2},\ldots ,x_{d}\rangle } from a d {\displaystyle d} -dimensional multinomial distribution with N {\displaystyle N} trials, a "smoothed" version of the counts gives the estimator θ ^ i = x i + α N + α d ( i = 1 , … , d ) , {\displaystyle {\hat {\theta }}_{i}={\frac {x_{i}+\alpha }{N+\alpha d}}\qquad (i=1,\ldots ,d),} where the smoothed count x ^ i = N θ ^ i {\displaystyle {\hat {x}}_{i}=N{\hat {\theta }}_{i}} , and the "pseudocount" α > 0 is a smoothing parameter, with α = 0 corresponding to no smoothing (this parameter is explained in § Pseudocount below). Additive smoothing is a type of shrinkage estimator, as the resulting estimate will be between the empirical probability (relative frequency) x i / N {\displaystyle x_{i}/N} and the uniform probability 1 / d . {\displaystyle 1/d.} Common choices for α are 0 (no smoothing), +1⁄2 (the Jeffreys prior), or 1 (Laplace's rule of succession), but the parameter may also be set empirically based on the observed data. From a Bayesian point of view, this corresponds to the expected value of the posterior distribution, using a symmetric Dirichlet distribution with parameter α as a prior distribution. In the special case where the number of categories is 2, this is equivalent to using a beta distribution as the conjugate prior for the parameters of the binomial distribution. == History == Laplace came up with this smoothing technique when he tried to estimate the chance that the sun will rise tomorrow. His rationale was that even given a large sample of days with the rising sun, we still can not be completely sure that the sun will still rise tomorrow (known as the sunrise problem). == Pseudocount == A pseudocount is an amount (not generally an integer, despite its name) added to the number of observed cases in order to change the expected probability in a model of those data, when not known to be zero. It is so named because, roughly speaking, a pseudo-count of value α {\displaystyle \alpha } weighs into the posterior distribution similarly to each category having an additional count of α {\displaystyle \alpha } . If the number of occurrences of each item i {\displaystyle i} is x i {\displaystyle x_{i}} out of N {\displaystyle N} samples, the empirical probability of event i {\displaystyle i} is p i , empirical = x i N , {\displaystyle p_{i,{\text{empirical}}}={\frac {x_{i}}{N}},} but the posterior probability when additively smoothed is p i , α -smoothed = x i + α N + α d , {\displaystyle p_{i,\alpha {\text{-smoothed}}}={\frac {x_{i}+\alpha }{N+\alpha d}},} as if to increase each count x i {\displaystyle x_{i}} by α {\displaystyle \alpha } a priori. Depending on the prior knowledge, which is sometimes a subjective value, a pseudocount may have any non-negative finite value. It may only be zero (or the possibility ignored) if impossible by definition, such as the possibility of a decimal digit of π being a letter, or a physical possibility that would be rejected and so not counted, such as a computer printing a letter when a valid program for π is run, or excluded and not counted because of no interest, such as if only interested in the zeros and ones. Generally, there is also a possibility that no value may be computable or observable in a finite time (see the halting problem). But at least one possibility must have a non-zero pseudocount, otherwise no prediction could be computed before the first observation. The relative values of pseudocounts represent the relative prior expected probabilities of their possibilities. The sum of the pseudocounts, which may be very large, represents the estimated weight of the prior knowledge compared with all the actual observations (one for each) when determining the expected probability. In any observed data set or sample there is the possibility, especially with low-probability events and with small data sets, of a possible event not occurring. Its observed frequency is therefore zero, apparently implying a probability of zero. This oversimplification is inaccurate and often unhelpful, particularly in probability-based machine learning techniques such as artificial neural networks and hidden Markov models. By artificially adjusting the probability of rare (but not impossible) events so those probabilities are not exactly zero, zero-frequency problems are avoided. Also see Cromwell's rule. === Choice of pseudocount === ==== Weakly informative prior ==== One common approach is to add 1 to each observed number of events, including the zero-count possibilities. This is sometimes called Laplace's rule of succession. This approach is equivalent to assuming a uniform prior distribution over the probabilities for each possible event (spanning the simplex where each probability is between 0 and 1, and they all sum to 1). Using the Jeffreys prior approach, a pseudocount of one half should be added to each possible outcome. Pseudocounts should be set to one or one-half only when there is no prior knowledge at all – see the principle of indifference. However, given appropriate prior knowledge, the sum should be adjusted in proportion to the expectation that the prior probabilities should be considered correct, despite evidence to the contrary – see further analysis. Higher values are appropriate inasmuch as there is prior knowledge of the true values (for a mint-condition coin, say); lower values inasmuch as there is prior knowledge that there is probable bias, but of unknown degree (for a bent coin, say). ==== Frequentist interval ==== One way to motivate pseudocounts, particularly for binomial data, is via a formula for the midpoint of an interval estimate, particularly a binomial proportion confidence interval. The best-known is due to Edwin Bidwell Wilson, in Wilson (1927): the midpoint of the Wilson score interval corresponding to z {\displaystyle z} standard deviations on either side is n S + z n + 2 z {\displaystyle {\frac {n_{S}+z}{n+2z}}} Taking z = 2 {\displaystyle z=2} standard deviations to approximate a 95% confidence interval ( z ≈ 1.96 {\displaystyle z\approx 1.96} ) yields pseudocount of 2 for each outcome, so 4 in total, colloquially known as the "plus four rule": n S + 2 n + 4 {\displaystyle {\frac {n_{S}+2}{n+4}}} This is also the midpoint of the Agresti–Coull interval (Agresti & Coull 1998). ==== Known incidence rates ==== Often the bias of an unknown trial population is tested against a control population with known parameters (incidence rates) μ = ⟨ μ 1 , μ 2 , … , μ d ⟩ . {\displaystyle {\boldsymbol {\mu }}=\langle \mu _{1},\mu _{2},\ldots ,\mu _{d}\rangle .} In this case the uniform probability 1 / d {\displaystyle 1/d} should be replaced by the known incidence rate of the control population μ i {\displaystyle \mu _{i}} to calculate the smoothed estimator: θ ^ i = x i + μ i α d N + α d ( i = 1 , … , d ) . {\displaystyle {\hat {\theta }}_{i}={\frac {x_{i}+\mu _{i}\alpha d}{N+\alpha d}}\qquad (i=1,\ldots ,d).} As a consistency check, if the empirical estimator happens to equal the incidence rate, i.e. μ i = x i / N , {\displaystyle \mu _{i}=x_{i}/N,} the smoothed estimator is independent of α {\displaystyle \alpha } and also equals the incidence rate. == Applications == === Classification === Additive smoothing is commonly a component of naive Bayes classifiers. === Statistical language modelling === In a bag of words model of natural language processing and information retrieval, the data consists of the number of occurrences of each word in a document. Additive smoothing allows the assignment of non-zero probabilities to words which do not occur in the sample. Studies have shown that additive smoothing is more effective than other probability smoothing methods in several retrieval tasks such as language-model-based pseudo-relevance feedback and recommender systems.
Moore machine
In the theory of computation, a Moore machine is a finite-state machine whose current output values are determined only by its current state. This is in contrast to a Mealy machine, whose output values are determined both by its current state and by the values of its inputs. Like other finite state machines, in Moore machines, the input typically influences the next state. Thus the input may indirectly influence subsequent outputs, but not the current or immediate output. The Moore machine is named after Edward F. Moore, who presented the concept in a 1956 paper, “Gedanken-experiments on Sequential Machines.” == Formal definition == A Moore machine can be defined as a 6-tuple ( S , s 0 , Σ , Λ , δ , G ) {\displaystyle (S,s_{0},\Sigma ,\Lambda ,\delta ,G)} consisting of the following: A finite set of states S {\displaystyle S} A start state (also called initial state) s 0 {\displaystyle s_{0}} which is an element of S {\displaystyle S} A finite set called the input alphabet Σ {\displaystyle \Sigma } A finite set called the output alphabet Λ {\displaystyle \Lambda } A transition function δ : S × Σ → S {\displaystyle \delta :S\times \Sigma \rightarrow S} mapping a state and the input alphabet to the next state An output function G : S → Λ {\displaystyle G:S\rightarrow \Lambda } mapping each state to the output alphabet "Evolution across time" is realized in this abstraction by having the state machine consult the time-changing input symbol at discrete "timer ticks" t 0 , t 1 , t 2 , . . . {\displaystyle t_{0},t_{1},t_{2},...} and react according to its internal configuration at those idealized instants, or else having the state machine wait for a next input symbol (as on a FIFO) and react whenever it arrives. A Moore machine can be regarded as a restricted type of finite-state transducer. == Visual representation == === Table === A state transition table is a table listing all the triples in the transition relation δ : S × Σ → S {\displaystyle \delta :S\times \Sigma \rightarrow S} . === Diagram === The state diagram for a Moore machine, or Moore diagram, is a state diagram that associates an output value with each state. == Relationship with Mealy machines == As Moore and Mealy machines are both types of finite-state machines, they are equally expressive: either type can be used to parse a regular language. The difference between Moore machines and Mealy machines is that in the latter, the output of a transition is determined by the combination of current state and current input ( S × Σ {\displaystyle S\times \Sigma } as the domain of G {\displaystyle G} ), as opposed to just the current state ( S {\displaystyle S} as the domain of G {\displaystyle G} ). When represented as a state diagram, for a Moore machine, each node (state) is labeled with an output value; for a Mealy machine, each arc (transition) is labeled with an output value. Every Moore machine M {\displaystyle M} is equivalent to the Mealy machine with the same states and transitions and the output function G ( s , σ ) = G M ( δ M ( s , σ ) ) {\displaystyle G(s,\sigma )=G_{M}(\delta _{M}(s,\sigma ))} , which takes each state-input pair ( s , σ ) {\displaystyle (s,\sigma )} and yields G M ( δ M ( s , σ ) ) {\displaystyle G_{M}(\delta _{M}(s,\sigma ))} , where G M {\displaystyle G_{M}} is M {\displaystyle M} 's output function and δ M {\displaystyle \delta _{M}} is M {\displaystyle M} 's transition function. However, not every Mealy machine can be converted to an equivalent Moore machine. Some can be converted only to an almost equivalent Moore machine, with outputs shifted in time. This is due to the way that state labels are paired with transition labels to form the input/output pairs. Consider a transition s i → s j {\displaystyle s_{i}\rightarrow s_{j}} from state s i {\displaystyle s_{i}} to state s j {\displaystyle s_{j}} . The input causing the transition s i → s j {\displaystyle s_{i}\rightarrow s_{j}} labels the edge ( s i , s j ) {\displaystyle (s_{i},s_{j})} . The output corresponding to that input, is the label of state s i {\displaystyle s_{i}} . Notice that this is the source state of the transition. So for each input, the output is already fixed before the input is received, and depends solely on the present state. This is the original definition by E. Moore. It is a common mistake to use the label of state s j {\displaystyle s_{j}} as output for the transition s i → s j {\displaystyle s_{i}\rightarrow s_{j}} . == Examples == Types according to number of inputs/outputs. === Simple === Simple Moore machines have one input and one output: edge detector using XOR binary adding machine clocked sequential systems (a restricted form of Moore machine where the state changes only when the global clock signal changes) Most digital electronic systems are designed as clocked sequential systems. Clocked sequential systems are a restricted form of Moore machine where the state changes only when the global clock signal changes. Typically the current state is stored in flip-flops, and a global clock signal is connected to the "clock" input of the flip-flops. Clocked sequential systems are one way to solve metastability problems. A typical electronic Moore machine includes a combinational logic chain to decode the current state into the outputs (lambda). The instant the current state changes, those changes ripple through that chain, and almost instantaneously the output gets updated. There are design techniques to ensure that no glitches occur on the outputs during that brief period while those changes are rippling through the chain, but most systems are designed so that glitches during that brief transition time are ignored or are irrelevant. The outputs then stay the same indefinitely (LEDs stay bright, power stays connected to the motors, solenoids stay energized, etc.), until the Moore machine changes state again. ==== Worked example ==== A sequential network has one input and one output. The output becomes 1 and remains 1 thereafter when at least two 0's and two 1's have occurred as inputs. A Moore machine with nine states for the above description is shown on the right. The initial state is state A, and the final state is state I. The state table for this example is as follows: === Complex === More complex Moore machines can have multiple inputs as well as multiple outputs. == Gedanken-experiments == In Moore's 1956 paper "Gedanken-experiments on Sequential Machines", the ( n ; m ; p ) {\displaystyle (n;m;p)} automata (or machines) S {\displaystyle S} are defined as having n {\displaystyle n} states, m {\displaystyle m} input symbols and p {\displaystyle p} output symbols. Nine theorems are proved about the structure of S {\displaystyle S} , and experiments with S {\displaystyle S} . Later, " S {\displaystyle S} machines" became known as "Moore machines". At the end of the paper, in Section "Further problems", the following task is stated: Another directly following problem is the improvement of the bounds given at the theorems 8 and 9. Moore's Theorem 8 is formulated as: Given an arbitrary ( n ; m ; p ) {\displaystyle (n;m;p)} machine S {\displaystyle S} , such that every two of its states are distinguishable from one another, then there exists an experiment of length n ( n − 1 ) 2 {\displaystyle {\tfrac {n(n-1)}{2}}} which determines the state of S {\displaystyle S} at the end of the experiment. In 1957, A. A. Karatsuba proved the following two theorems, which completely solved Moore's problem on the improvement of the bounds of the experiment length of his "Theorem 8". Theorem A. If S {\displaystyle S} is an ( n ; m ; p ) {\displaystyle (n;m;p)} machine, such that every two of its states are distinguishable from one another, then there exists a branched experiment of length at most ( n − 1 ) ( n − 2 ) 2 + 1 {\displaystyle {\tfrac {(n-1)(n-2)}{2}}+1} through which one may determine the state of S {\displaystyle S} at the end of the experiment. Theorem B. There exists an ( n ; m ; p ) {\displaystyle (n;m;p)} machine, every two states of which are distinguishable from one another, such that the length of the shortest experiments establishing the state of the machine at the end of the experiment is equal to ( n − 1 ) ( n − 2 ) 2 + 1 {\displaystyle {\tfrac {(n-1)(n-2)}{2}}+1} . Theorems A and B were used for the basis of the course work of a student of the fourth year, A. A. Karatsuba, "On a problem from the automata theory", which was distinguished by testimonial reference at the competition of student works of the faculty of mechanics and mathematics of Moscow State University in 1958. The paper by Karatsuba was given to the journal Uspekhi Mat. Nauk on 17 December 1958 and was published there in June 1960. Until the present day (2011), Karatsuba's result on the length of experiments is the only exact nonlinear result, both in automata theory, and in similar problems of computational complexity theory.
Arthur Zimek
Arthur Zimek is a professor in data mining, data science and machine learning at the University of Southern Denmark in Odense, Denmark. He graduated from LMU Munich in Germany, where he worked with Prof. Hans-Peter Kriegel. His dissertation on "Correlation Clustering" was awarded the "SIGKDD Doctoral Dissertation Award 2009 Runner-up" by the Association for Computing Machinery. He is well known for his work on outlier detection, density-based clustering, correlation clustering, and the curse of dimensionality. He is one of the founders and core developers of the open-source ELKI data mining framework.
Trustworthy computing
The term trustworthy computing (TwC) has been applied to computing systems that are inherently secure, available, and reliable. It is particularly associated with the Microsoft initiative of the same name, launched in 2002. == History == Until 1995, there were restrictions on commercial traffic over the Internet. On, May 26, 1995, Bill Gates sent the "Internet Tidal Wave" memorandum to Microsoft executives assigning "...the Internet this highest level of importance..." but Microsoft's Windows 95 was released without a web browser as Microsoft had not yet developed one. The success of the web had caught them by surprise but by mid 1995, they were testing their own web server, and on August 24, 1995, launched a major online service, The Microsoft Network (MSN). The National Research Council recognized that the rise of the Internet simultaneously increased societal reliance on computer systems while increasing the vulnerability of such systems to failure and produced an important report in 1999, "Trust in Cyberspace". This report reviews the cost of un-trustworthy systems and identifies actions required for improvement. == Microsoft and Trustworthy Computing == Bill Gates launched Microsoft's "Trustworthy Computing" initiative with a January 15, 2002 memo, referencing an internal whitepaper by Microsoft CTO and Senior Vice President Craig Mundie. The move was reportedly prompted by the fact that they "...had been under fire from some of its larger customers–government agencies, financial companies and others–about the security problems in Windows, issues that were being brought front and center by a series of self-replicating worms and embarrassing attacks." such as Code Red, Nimda, Klez and Slammer. Four areas were identified as the initiative's key areas: Security, Privacy, Reliability, and Business Integrity, and despite some initial scepticism, at its 10-year anniversary it was generally accepted as having "...made a positive impact on the industry...". The Trustworthy Computing campaign was the main reason why Easter eggs disappeared from Windows, Office and other Microsoft products.
The Best Free AI Essay Writer for Beginners
In search of the best AI essay writer? An AI essay writer is software that uses machine learning to help you get more done — it turns a rough idea into a polished result in seconds. When choosing one, weigh output quality, pricing, export formats, and how well it fits the tools you already use. Whether you are a beginner or a pro, the right AI essay writer slots into your workflow and pays for itself fast. We tested the leading options and ranked them by quality, value, and ease of use.