Search Results (4)

The performance of pattern classification is affected significantly by feature selection. However, for security applications, selecting proper features is difficult, as malicious software continuously changes its characteristics. Thus, compression-based pattern recognition has attracted much attention because it does not require explicit feature selection to design proper distance measures. LZJD (Lempel–Ziv Jaccard Distance), in particular, has been useful for malware classification, as it computes compression distances without actually compressing objects and is suitable for handling large files like malware. LZJD extracts a compression dictionary for every object in advance and estimates a similarity between two objects by comparing their compression dictionaries. However, LZJD ignores the similarity between words in a compression dictionary. As a result, even if the dictionary has many similar words, they are simply processed as different words. To exploit the similarity between words, we propose to remove the last characters of words in the dictionary and to unify similar words that share the same prefix. This unification of words turns the compression dictionary into a multiset of words. Hence, our compression distance is named MLZJD (Multiset LZJD). In addition, the unification of words in MLZJD decreases the number of word kinds in compression dictionaries and contributes to speeding up the distance computation. We experimentally show that MLZJD halves the execution time as compared with LZJD, while hardly damaging the classification accuracy. Even on condition that the compression distances are approximated with Min-Hash, MLZJD achieves a much shorter running time than LZJD, while retaining almost the same classification accuracy as LZJD. Full article

As artificial intelligence (AI) systems grow more powerful, autonomous, and embedded in critical infrastructure, their identification and traceability become foundational to regulatory oversight and sustainable digital governance. In digitally transformed enterprises, long-term sustainability depends on transparent, accountable, and lifecycle-governed AI systems, all of which require verifiable identity. This study proposes a conceptual and architectural framework for AI identification, combining technical and governance mechanisms to support lifecycle accountability. The framework integrates five components: model fingerprinting, cryptographic hashing, blockchain-based registration, zero-knowledge proof (ZKP)-based proof of possession, and post-deployment structural change screening. We introduce a dual-layer identifier, consisting of a machine-verifiable primary hash and a human-readable secondary identifier, anchored in a tamper-resistant registry. Identity validation is supported by selective ZKP-based verification at governance-defined checkpoints, while post-deployment changes are monitored using Lempel-Ziv Jaccard Distance (LZJD) as a governance-oriented screening signal rather than a semantic performance metric. The framework establishes an enforceable and transparent identity infrastructure that enables continuity, auditability, and policy-aligned oversight across AI system lifecycles. By embedding AI identification within enterprise architecture and governance processes, the proposed approach supports sustainable innovation, strengthens institutional accountability, and provides a foundation for selective, policy-defined verification during digital transformation. Full article

The goal of digital forensics is to recover and investigate pieces of data found on digital devices, analysing in the process their relationship with other fragments of data from the same device or from different ones. Approximate matching functions, also called similarity preserving or fuzzy hashing functions, try to achieve that goal by comparing files and determining their resemblance. In this regard, ssdeep, sdhash, and LZJD are nowadays some of the best-known functions dealing with this problem. However, even though those applications are useful and trustworthy, they also have important limitations (mainly, the inability to compare files of very different sizes in the case of ssdeep and LZJD, the excessive size of sdhash and LZJD signatures, and the occasional scarce relationship between the comparison score obtained and the actual content of the files when using the three applications). In this article, we propose a new signature generation procedure and an algorithm for comparing two files through their digital signatures. Although our design is based on ssdeep, it improves some of its limitations and satisfies the requirements that approximate matching applications should fulfil. Through a set of ad-hoc and standard tests based on the FRASH framework, it is possible to state that the proposed algorithm presents remarkable overall detection strengths and is suitable for comparing files of very different sizes. A full description of the multi-thread implementation of the algorithm is included, along with all the tests employed for comparing this proposal with ssdeep, sdhash, and LZJD. Full article

Security and privacy concerns are challenging the way users interact with devices. The number of devices connected to a home or enterprise network increases every day. Nowadays, the security of information systems is relevant as user information is constantly being shared and moving in the cloud; however, there are still many problems such as, unsecured web interfaces, weak authentication, insecure networks, lack of encryption, among others, that make services insecure. The software implementations that are currently deployed in companies should have updates and control, as cybersecurity threats increasingly appearing over time. There is already some research towards solutions and methods to predict new attacks or classify variants of previous known attacks, such as (algorithmic) information theory. This survey combines all relevant applications of this topic (also known as Kolmogorov Complexity) in the security and privacy domains. The use of Kolmogorov-based approaches is resource-focused without the need for specific knowledge of the topic under analysis. We have defined a taxonomy with already existing work to classify their different application areas and open up new research questions. Full article