Information hiding and copyrights

This chapter explores the use of steganography on digital files and produces an enhanced technique that addresses the major vulnerabilities that make algorithms less reliable in securing data. Through a review of historical techniques in the field, the study identifies weaknesses in the algorithms to improve security and increase capacity using different techniques. One of the approaches proposed in this study involves a distributed method, which is simple, clear, low-cost, and agile. The study also analyses data manipulation and embedding processes in different files and for different purposes, such as vulnerabilities or placeholders exploited by criminals distributing viruses over the internet using Steganography. The results of the study can help forensic analysts identify secret content and raise awareness about protecting against eavesdropping data on devices. The study proposes a new scheme to improve Steganography called DSoBMP, together with guideline materials that have been published in four international peer-reviewed journals, including Springer and used as a stepping stone to collaborate in a worldwide book publication

Protecting against eavesdropping on mobile phones to snip data with Information Security Awareness and Steganography principles

Eavesdropping on Mobile Devices is the primary concern here. Since the mobility of computers, including laptops, tablets, PDAs and smartphone, are demanding and criminals now start to target these devices as the usage is more than desktops. This initial research is focusing on drawing attention to this topic. Let set how to combat mobile interception by criminals tends to investigate whether steganography applications can benefit digital criminals’ interception on such devices. The concentration is on mobile phones interception by criminals to steal personal data; therefore, it consists of developing a framework, mechanism and algorithm to prevent it. The anticipated implications imply Legal and Ethical Issues. Everyone should familiarise and follow this carefully to make sure it does not cause any particular privacy concerns to general individuals. Only personal and authorised devices were used to test the technical work produced by this research

A novel hybrid method for effective identification and extraction of digital evidence masked by steganographic techniques in WAV and MP3 files

Anti-forensics techniques, particularly steganography and cryptography, have become increasingly pressing issues affecting current digital forensics practices. This paper advances the automation of hidden evidence extraction in audio files by proposing a novel multi-approach method. This method facilitates the correlation between unprocessed artefacts, indexed and live forensics analysis, and traditional steganographic and cryp- tographic detection techniques. In this work, we opted for experimental research methodology in the form of a quantitative analysis of the efficiency of the proposed automation in detecting and extracting hidden artefacts in WAV and MP3 audio files. This comparison is made against standard industry systems. This work advances the current automation in extracting evidence hidden by cryptographic and steganographic techniques during forensic investigations. The proposed multi-approach demonstrates a clear enhancement in terms of cover- age and accuracy, notably on large audio files (MP3 and WAV), where manual forensic analysis is complex, time-consuming and requires significant expertise. Nonetheless, the proposed multi-approach automation may occasionally produce false positives (detecting steganography where none exists) or false negatives (failing to detect steganography that is present). However, it strikes a good balance between efficiently and effectively detecting hidden evidence, minimising false negatives and validating its reliability

Improving steganographic capacity using distributed steganography over BMP

Our research area tackles the improvement of private data security using our proposed Steganographic method called DSoBMP-I (Distributed Steganography over BMP faze I) to improve the issues of low capacity, high detectability and distortion. The methodology consists of a new distributed steganographic approach to minimise the main weaknesses of today’s methods, including Discrete Cosine Transform, where the capacity, detection and distortion need an upgrade to accommodate secure steganography for our data protection. The proposed prototype approach that evolved after a few experiments using our distributed steganographic method, where secret data is secured into a set of BMP files (as it is proven more reliable), originates from a raw file that is not necessarily a BMP at the start. After applying a layer of encryption for extra security using two different methods such as RC4 & RSA, comparing the two encryption techniques for their agility and extra security to address the issue of low capacity and better security using the DSoBMP-I method, all deriving from the supplied image. The overall achievement was improved capacity that doubles as the set of BMP images increases, less distortion and detectability as secret data stays among different files

Comparative analysis of malware detection response times across Android versions: an emphasis on the "Hoverwatch" application

As Android-based smartphones become increasingly prevalent in our digital age, they also become inviting targets for malicious applications. Our research demystifies malware detection, with a special focus on response times across multiple Android versions. We began by examining the vast influence of Android and the need for robust malware detection in today's market. To understand the threat landscape, we investigated application-based threats and their impact on users and provided an encompassing review of the current malware detection methodologies, which include static, dynamic, and hybrid techniques. Our study centres around comprehensive tests conducted on distinct Android emulators, with the intent to measure malware detection response time. We used a known malicious app, "Hoverwatch," for this experiment. Our findings reveal notable disparities in detection times, emphasizing the need for constant advancements in defence systems and in a number of test cases the need for improvements in real-time detection capabilities. This research offers insights into the effectiveness of current Android malware detection methods, stressing response times. We underscore the necessity for regular updates and system enhancements to combat the evolving threat environment