Understanding the attributes of critical data and implementing suitable security measures help organisations bolster their data-protection strategies and diminish the potential impacts of ransomware incidents. Unauthorised extraction and acquisition of data are the principal objectives of most cyber invasions. We underscore the severity of this issue using a recent attack by the Clop ransomware group, which exploited the MOVEit Transfer vulnerability and bypassed network-detection mechanisms to exfiltrate data via a Command and Control server. As a countermeasure, we propose a method called Buffer-Based Signature Verification (BBSV). This approach involves embedding 32-byte tags into files prior to their storage in the cloud, thus offering enhanced data protection. The BBSV method can be integrated into software like MOVEit Secure Managed File Transfer, thereby thwarting attempts by ransomware to exfiltrate data. Empirically tested using a BBSV prototype, our approach was able to successfully halt the encryption process for 80 ransomware instances from 70 ransomware families. BBSV not only stops the encryption but also prevents data exfiltration when data are moved or written from the original location by adversaries. We further develop a hypothetical exploit scenario in which an adversary manages to bypass the BBSV, illicitly transmits data to a Command and Control server, and then removes files from the original location. We construct an extended state space, in which each state represents a tuple that integrates user authentication and system components at the filesystem level.

Compression is widely used in Internet applications to save communication time, bandwidth and storage. Recently invented by Jarek Duda asymmetric numeral system (ANS) offers an improved efficiency and a close to optimal compression. The ANS algorithm has been deployed by major IT companies such as Facebook, Google and Apple. Compression by itself does not provide any security (such as confidentiality or authentication of transmitted data). An obvious solution to this problem is an encryption of compressed bitstream. However, it requires two algorithms: one for compression and the other for encryption. In this work, we investigate natural properties of ANS that allow to incorporate authenticated encryption using as little cryptography as possible. We target low-level security communication and storage such as transmission of data from IoT devices/sensors. In particular, we propose three solutions for joint compression and encryption (compcrypt). The solutions offer different tradeoffs between security and efficiency assuming a slight compression deterioration. All of them use a pseudorandom bit generator (PRBG) based on lightweight stream ciphers. The first solution is close to original ANS and applies state jumps controlled by PRBG. The second one employs two copies of ANS, where compression is switched between the copies. The switch is controlled by a PRBG bit. The third compcrypt modifies the encoding function of ANS depending on PRBG bits. Security and efficiency of the proposed compcrypt algorithms are evaluated. The first compcrypt is the most efficient with a slight loss of compression quality. The second one consumes more storage but the loss of compression quality is negligible. The last compcrypt offers the best security but is the least efficient.

In this paper, we propose a novel method for generating visually appealing two-dimensional (2D) barcodes that resemble meaningful images to human observers. The technology of 2D barcodes, currently dominated by quick response codes, is widely adopted in many applications, including product tracking, document management, and general marketing. Such barcodes typically lack user friendly appearance and do not convey any visual significance to human observers. The proposed method addresses this problem by allowing 2D barcodes to resemble an arbitrary image or a logo. Our method is based on a generalization of the Kuznetsov-Tsybakov problem that served as a foundation for wet paper codes, commonly adopted in digital steganography. We introduce weaker statistical constraints to obtain additional flexibility allowing the barcode to assume the appearance of an arbitrary pattern. This paper provides the theoretical analysis of the proposed coding framework and a practical algorithm for rapid approximation of the optimal code. We also discuss the introduction of error correction capabilities, and experimentally evaluate a prototype implementation in a smartphone-based acquisition scenario.

The method of types is one of the most popular techniques in information theory and combinatorics. However, thus far the method has been mostly applied to 1-D Markov processes, and it has not been thoroughly studied for general Markov fields. Markov fields over a finite alphabet of size m ≥ 2 can be viewed as models for multidimensional systems with local interactions. The locality of these interactions is represented by a shape S while its marking by symbols of the underlying alphabet is called a tile. Two assignments in a Markov field have the same type if they have the same empirical distribution, i.e., if they have the same number of tiles of a given type. Our goal is to study the growth of the number of possible Markov field types in either a d-dimensional box of lengths n1, .. . ,ndor its cyclic counterpart, a d-dimensional torus. We relate this question to the enumeration of nonnegative integer solutions of a large system of Diophantine linear equations called the conservation laws. We view a Markov type as a vector in a D =m|S| dimensional space and count the number of such vectors satisfying the conservation laws, which turns out to be the number of integer points in a certain polytope. For the torus, this polytope is of dimension μ = D - 1 - rk(C), where rk(C) is the number of linearly independent conservation laws C. This provides an upper bound on the number of types. Then, we construct a matching lower bound leading to the conclusion that the number of types in the torus Markov field is θ(Nμ), where N = n1. . . nd. These results are derived by geometric tools, including ideas of discrete and convex multidimensional geometry.

Entropy coding is an integral part of most data compression systems. Huffman coding (HC) and arithmetic coding (AC) are two of the most widely used coding methods. HC can process a large symbol alphabet at each step allowing for fast encoding and decoding. However, HC typically provides suboptimal data rates due to its inherent approximation of symbol probabilities to powers of 1 over 2. In contrast, AC uses nearly accurate symbol probabilities, hence generally providing better compression ratios. However, AC relies on relatively slow arithmetic operations making the implementation computationally demanding. In this paper we discuss asymmetric numeral systems (ANS) as a new approach to entropy coding. While maintaining theoretical connections with AC, the proposed ANS-based coding can be implemented with much less computational complexity. While AC operates on a state defined by two numbers specifying a range, an ANS-based coder operates on a state defined by a single natural number such that the x ∈ ℕ state contains ≈ log2(x) bits of information. This property allows to have the entire behavior for a large alphabet summarized in the form of a relatively small table (e.g. a few kilobytes for a 256 size alphabet). The proposed approach can be interpreted as an equivalent to adding fractional bits to a Huffman coder to combine the speed of HC and the accuracy offered by AC. Additionally, ANS can simultaneously encrypt a message encoded this way. Experimental results demonstrate effectiveness of the proposed entropy coder.

Many two-dimensional (2D) barcodes, such as quick response (QR) codes, lack user-friendly appearance. Our goal in this paper is to generate 2D barcodes that “look” like recognizable images or logos. Standard steganographic methods hide a message (payload) in an image usually by modifying bits in a specific way using predetermined pixels of the image. This approach cannot be directly used for very low bit rates commonly used in 1 bit per pixel 2D barcodes. It is possible to produce barcodes in which the grayness of a pixel in an image is interpreted as the probability of assigning a value (black or white) to the corresponding pixel of the encoded message (payload). This can be viewed as statistical constraints enforced on the encoded bit-sequence. Using an information theoretic approach, Kuznetsov and Tsybakov have shown that this can be done for a specific case of constraints almost without any loss of capacity. In this paper, we propose generalizations of this approach with weaker constraints as an application to generating 2D barcodes that resemble images. We describe a coding framework, various types of constraints, a practical approximation and some example 2D barcodes generated from our implementation.

The method of types is one of the most popular technique in information theory and combinatorics. However, it was never thoroughly studied for Markov fields. Markov fields can be viewed as models for systems involving a large number of variables with local dependencies and interactions. These local dependencies can be captured by a shape of interactions (locations that contribute the next probability transition). Shapes marked by symbols from a finite alphabet are called tiles. Two assignments in a Markov filed have the same type if they have the same empirical distribution or they can be tiled by the same number of tile types. Our goal is to study the growth of the number of Markov field types or the number of tile types. This intricate and important problem was left open for too long.