WinZip will then create the Zip file as per your instructions, and you can share it or store it as you like. The size of the archive should be much less than the sum of the individual files thanks to its high quality compression, giving you the option to upload and download it quickly.
All the litter boxes use the sims 4 litter, meaning they come with poop already in them...soz. Cats' tails might stick out of the covered boxes. And make sure to put them against a wall or else the cat will jump through the back, which isn't ideal for some of the boxes.
sims 2 high compressed rar
Several cybersecurity domains, such as ransomware detection, forensics and data analysis, require methods to reliably identify encrypted data fragments. Typically, current approaches employ statistics derived from byte-level distribution, such as entropy estimation, to identify encrypted fragments. However, modern content types use compression techniques which alter data distribution pushing it closer to the uniform distribution. The result is that current approaches exhibit unreliable encryption detection performance when compressed data appear in the dataset. Furthermore, proposed approaches are typically evaluated over few data types and fragment sizes, making it hard to assess their practical applicability. This paper compares existing statistical tests on a large, standardized dataset and shows that current approaches consistently fail to distinguish encrypted and compressed data on both small and large fragment sizes. We address these shortcomings and design EnCoD, a learning-based classifier which can reliably distinguish compressed and encrypted data. We evaluate EnCoD on a dataset of 16 different file types and fragment sizes ranging from 512B to 8KB. Our results highlight that EnCoD outperforms current approaches by a wide margin, with accuracy ranging from \(\sim 82\%\) for 512B fragments up to \(\sim 92\%\) for 8KB data fragments. Moreover, EnCoD can pinpoint the exact format of a given data fragment, rather than performing only binary classification like previous approaches.
A popular approach to address this problem is to estimate the Shannon entropy of the sequence of interest using the Maximum Likelihood Estimator (MLE): \(\hatH_MLE\). This approach leverages the observation that the distribution of byte values in an encrypted stream closely follows a uniform distribution; therefore, high entropy is used as a proxy for randomness. This estimator has the advantage of being simple and computationally efficient. As non-encrypted digital data are assumed to have low byte-level entropy, the estimator is expected to easily differentiate non-encrypted and encrypted content.
While this approach remains widely used (e.g., [1,2,3,4]), a number of works have highlighted its limitations. Modern applications tend to compress data prior to both storage and transmission. Popular examples include the zip compressed file format, and HTTP compression [8] (both using the DEFLATE algorithm). As compression removes recurring patterns in data, compressed streams tend to exhibit high Shannon entropy. As a result, compressed data exhibit values of \(\hatH_MLE\) that are close and oftentimes overlapping with those obtained by encryption. In principle, compressed content can be identified by using appropriate parsers. However, many security-related applications, such as ransomware detection, traffic analysis and digital forensics, generally do not have access to whole-file information, but rather work at the level of fragments of data. In these settings, the metadata that is required by parsers is not present or is incomplete [9]. Given this issue, a number of works have been looking at alternative tests to distinguish between encrypted and compressed content [10,11,12,13,14,15,16]. While these works have the potential to be useful, there has been limited evaluation of their performance on a standardized dataset. Consequently, there is no clear understanding of how these approaches: (i) fare on a variety of compressed file formats and sizes, and (ii) compare to each other. The potential negative implications are significant: the use of ineffective techniques for identifying encrypted content can hinder the effectiveness of ransomware detectors [17, 18], and significantly limit the capability of forensic tools.
EnCoD can distinguish between compressed and encrypted data fragments as small as 512B with \(86\%\)accuracy. The accuracy increases to up to \(94\%\) when distinguishing between encrypted and purely compressed data (i.e.,zip, gzip), and up to \(100\%\) in the case of compressed application data fragments (e.g., pdf, jpeg, mp3) when the fragment size is 8KB. Furthermore, we investigate the applicability of robust feature extraction techniques such as autoencoders to our architecture, in an effort to understand whether feature vector pre-processing can lead to increased performance compared to a plain neural network (NN) architecture in this domain.
We propose a new neural-network-based approach and show that it outperforms current state-of-the-art tests in distinguishing encrypted from compressed content for most considered formats, over all considered fragment sizes.
We propose a new multi-class classifier that can label a fragment with high accuracy as encrypted data, general-purpose compressed data (zip/gzip/rar/bz2), or one of multiple application-specific compressed data (png, jpeg, pdf, mp3, office, video).
Determining the format of a particular data object (e.g., a file in permanent storage, or an HTTP object) is an extremely common operation. Under normal circumstances, it can be accomplished by looking at content metadata or by parsing the object. Things get more complicated, however, when no metadata is available and the data object is corrupted or partly missing. In this paper, we focus on detection of encrypted content and, in particular, on distinguishing between encrypted and compressed data fragments. We begin by examining relevant applications of encryption detection primitives.
This reasoning assumes that, while encrypted data has high entropy, non-encrypted data does not. This appears reasonable, as most relevant data types (e.g., text, images, audio) are information-rich and highly structured. However, this assumption does not hold true in modern computing. Modern CPUs can efficiently decompress data for processing, and compress it back for storage or transmission; this is oftentimes performed in real time and transparent to the user. As a result, most formats tend to apply compression [26]. Informally, a good compression algorithm works by identifying and removing recognizable structures from the data stream; as a result, compressed data tend to exhibit high entropy. In practice, this fact compromises the ability of entropy-based detectors to distinguish encrypted and non-encrypted, compressed content.
This section reviews three state-of-the-art approaches to distinguish encrypted and compressed content: the NIST suite, \(\chi ^2\) and HEDGE [15]. Strictly speaking, these approaches test the randomness of a string of bytes, and make no attempt to determine its type. However, due to their high precision they can be used to distinguish true pseudorandom (encrypted) sequences and compressed ones which, while approximating a randomly generated stream, maintain structure.
The NIST SP800-22 specification [27] describes a suite of tests whose intended use is to evaluate the quality of random number generators. The suite consists of 15 distinct tests, which analyze various structural aspects of a byte sequence. These tests are commonly employed as a benchmark for distinguishing compressed and encrypted content (e.g., [15, 16]). Each test analyzes a particular property of the sequence, and subsequently applies a test-specific decision rule to determine whether the result of the analysis suggests randomness or not. When using the NIST suite for discriminating random and non-random sequences, an important question concerns aggregation of the results of individual tests. Analysis of the tests [27] suggests that they are largely independent. Given this observation, and the intrinsic complexity of a priori defining a ranking between the tests, we use a majority voting approach. In other words, we consider a fragment to be random (and therefore encrypted) when the majority of tests considers it so. Since some of the tests require a block length much bigger than the ones we use for our smaller fragment sizes, we did not consider in the voting the tests that cannot be executed.
The \(\chi ^2\) test is a simple statistical test to measure goodness of fit. It has been widely applied to distinguish compressed and encrypted content [10, 13, 15]. Given a set of samples, it measures how well the distribution of such samples follows a given distribution. Mathematically, the test is defined as:
HEDGE [15] simultaneously incorporates three methods to distinguish between compressed and encrypted fragments: \(\chi ^2\) test with absolute value, \(\chi ^2\) with confidence interval and a subset of NIST SP800-22 test suite. Out of the NIST SP800-22 test suite HEDGE incorporates 3 tests: frequency within block test, cumulative sums test, and approximate entropy test. These tests were selected due to (i) their ability to operate on short byte sequences, and (ii) their reliable performance on a large and representative dataset. In the HEDGE detector, the threshold of the number of the above-mentioned NIST SP800-22 tests failed is set to 0. For the \(\chi ^2\) with absolute value test, the thresholds are pre-computed for each of the considered packet sizes, by considering the average and its standard deviation. For \(\chi ^2\) with confidence interval, the \(\chi \%\) interval is \((\chi \% > 99\% \chi \% 2ff7e9595c
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