Cloud Computing Security: Hardware-Based Attacks and Countermeasures

Montasari, Reza; Daneshkhah, Alireza; Jahankhani, Hamid; Hosseinian-Far, Amin

doi:10.1007/978-3-030-60425-7_6

Cited by 6 publications

(4 citation statements)

References 38 publications

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“…Private browsing modes, also known as incognito mode or privacy mode, are built-in features in popular web browsers that claim to protect users' privacy by not storing browsing history, cookies, or other temporary files. Montasari and Peltola [8]…”

Section: Browser Forensicsmentioning

confidence: 97%

Forensic analysis of iOS binary cookie files

Studiawan

2024

Journal of Forensic Sciences

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AbstractiPhone operating system (iOS) devices utilize binary cookies as a data storage tool, encoding user‐specific information within an often‐neglected element of smartphone analysis. This binary format contains details such as cookie flags, expiration, and creation dates, domain, and value of the cookie. These data are invaluable for forensic investigations. This study presents a comprehensive methodology to decode and extract valuable data from these files, enhancing the ability to recover user activity information from iOS devices. This paper provides an in‐depth forensic investigation into the structure and function of iOS binary cookie files. Our proposed forensic technique includes a combination of reverse engineering and custom‐built Python scripts to decode the binary structure. The results of our research demonstrate that these cookie files can reveal an array of important digital traces, including user preferences, visited websites, and timestamps of online activities. It concludes that the forensic analysis of iOS binary cookie files can be a tool for forensic investigators and cybersecurity professionals. In the rapidly evolving domain of digital forensics, this research contributes to our understanding of less‐explored data sources within iOS devices and their potential value in investigative contexts.

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Section: Browser Forensicsmentioning

confidence: 97%

Forensic analysis of iOS binary cookie files

Studiawan

2024

Journal of Forensic Sciences

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“…The emergence of deep learning technology has revolutionised the field of multimedia forensics [137], [138], prompting the need for innovative and timely solutions. Although a multitude of research efforts and forensic tools have been dedicated to detecting anomalies, such as lighting variations [139], shadow inconsistencies [140], and colour illuminations [141], they now face the challenges brought on by the sophistication of deep learning [137].…”

Section: Deepfake Detectionmentioning

confidence: 99%

A Survey on the Detection and Impacts of Deepfakes in Visual, Audio, and Textual Formats

Mubarak,

Alsboui,

Alshaikh

et al. 2023

IEEE Access

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In the rapidly evolving digital landscape, the generation of fake visual, audio, and textual content poses a significant threat to society's trust, political stability, and integrity of information. The generation process has been enhanced and simplified using Artificial Intelligence techniques, which have been termed deepfake. Although significant attention has been paid to visual and audio deepfakes, there is also a burgeoning need to consider text-based deepfakes. Due to advancements in natural language processing and large language models, the potential of manipulating textual content to reshape online discourse and misinformation has increased. This study comprehensively examines the multifaceted nature and impacts of deep-fake-generated media. This work explains the broad implications of deepfakes in social, political, economic, and technological domains. State-of-the-art detection methodologies for all types of deepfake are critically reviewed, highlighting the need for unified, real-time, adaptable, and generalised solutions. As the challenges posed by deepfakes intensify, this study underscores the importance of a holistic approach that intertwines technical solutions with public awareness and legislative action. By providing a comprehensive overview and establishing a framework for future exploration, this study seeks to assist researchers, policymakers, and practitioners in navigating the complexities of deepfake phenomena.

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“…Improving the efficiency of memory encryption in multi-processor systems considers the most trusted physically attacked chip. Security measures such as confidentiality and integrity are essential beyond the chip [19]. A suitable counter cache technique can be implemented using the consistency protocol.…”

Section: A Security Issues In Cache Memorymentioning

confidence: 99%

Security and Privacy of Blockchain-Based Single-Bit Cache Memory Architecture for IoT Systems

et al. 2022

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This paper provides an overview of blockchain technology's security and privacy features, as well as an overview of IoT-based cache memory and single-bit six transistor static random-access memory cell sense amplifier architecture. Each chip's memory is used for recorded as blocks, which are encrypted and used as a blockchain for other memory devices. The architectures comprise of the circuit of write driver, six transistor static random access memory cells, and sense amplifiers such as current differential sense amplifier, charge transfer differential sense amplifier, and voltage latch sense amplifier. Furthermore, different parameters such as the number of transistors, sensing delay, and power consumption have been analyzed for varying resistance values (i.e., R=42.3 and R=42.3K ). Apart from that, power reduction techniques such as dual sleep, forced stack, sleep transistor, and sleep stack are used to optimize power consumption. These power reduction techniques are applied over different blocks of architecture, such as six transistors static random access memory cell and sense amplifier to optimize power consumption of the architecture. The conclusion arises that a single-bit six transistor static random access memory cell with power reduction dual sleep technique voltage latch sense amplifier with power reduction dual sleep technique in architecture consumes 11.65µW of power and has 33 transistors which are lowest from other architectures.INDEX TERMS Circuit of write driver (CWD), sense amplifier (SA), current differential sense amplifier (CDSA), charge transfer differential sense amplifier (CTDSA), voltage latch sense amplifier (VLSA), six transistor static random-access memory (6T-SRAM).

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Cloud Computing Security: Hardware-Based Attacks and Countermeasures

Cited by 6 publications

References 38 publications

Forensic analysis of iOS binary cookie files

Forensic analysis of iOS binary cookie files

A Survey on the Detection and Impacts of Deepfakes in Visual, Audio, and Textual Formats

Security and Privacy of Blockchain-Based Single-Bit Cache Memory Architecture for IoT Systems

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