scholarly journals Digital Forensic Implications of Collusion Attacks on the Lightning Network

2017 ◽  
pp. 133-147 ◽  
Author(s):  
Dmytro Piatkivskyi ◽  
Stefan Axelsson ◽  
Mariusz Nowostawski
Keyword(s):  
Collusion Attacks ◽  
Digital Forensic ◽  
Forensic Implications

scholarly journals BitTorrent Sync: First Impressions and Digital Forensic Implications

2014 ◽  
Vol 11 ◽  
pp. S77-S86 ◽  
Author(s):  
Jason Farina ◽  
Mark Scanlon ◽  
M-Tahar Kechadi
Keyword(s):  
First Impressions ◽  
Digital Forensic ◽  
Forensic Implications

scholarly journals Digital forensic implications of ZFS

2009 ◽  
Vol 6 ◽  
pp. S99-S107 ◽  
Author(s):  
Nicole Lang Beebe ◽  
Sonia D. Stacy ◽  
Dane Stuckey
Keyword(s):  
Digital Forensic ◽  
Forensic Implications

Forensic Data Extraction and Analysis of Left Artifacts on emulated Android Phones: A Case Study of Instant Messaging Applications

2017 ◽  
Vol 2 (11) ◽  
pp. 8-16
Author(s):  
Moses Ashawa ◽  
Innocent Ogwuche
Keyword(s):  
Mobile Phones ◽  
Instant Messaging ◽  
Data Extraction ◽  
Digital Evidence ◽  
Cyber Attack ◽  
Forensic Evidence ◽  
Digital Forensic ◽  
Proportional Increase ◽  
Forensic Tools

The fast-growing nature of instant messaging applications usage on Android mobile devices brought about a proportional increase on the number of cyber-attack vectors that could be perpetrated on them. Android mobile phones store significant amount of information in the various memory partitions when Instant Messaging (IM) applications (WhatsApp, Skype, and Facebook) are executed on them. As a result of the enormous crimes committed using instant messaging applications, and the amount of electronic based traces of evidence that can be retrieved from the suspect’s device where an investigation could convict or refute a person in the court of law and as such, mobile phones have become a vulnerable ground for digital evidence mining. This paper aims at using forensic tools to extract and analyse left artefacts digital evidence from IM applications on Android phones using android studio as the virtual machine. Digital forensic investigation methodology by Bill Nelson was applied during this research. Some of the key results obtained showed how digital forensic evidence such as call logs, contacts numbers, sent/retrieved messages, and images can be mined from simulated android phones when running these applications. These artefacts can be used in the court of law as evidence during cybercrime investigation.

scholarly journals Holistic digital forensic readiness framework for IoT-enabled organizations

2020 ◽  
Vol 2 ◽  
pp. 100117
Author(s):  
Victor R. Kebande ◽  
Phathutshedzo P. Mudau ◽  
Richard A. Ikuesan ◽  
H.S. Venter ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Digital Forensic

scholarly journals A Dataset of Photos and Videos for Digital Forensics Analysis Using Machine Learning Processing

Data ◽  
2021 ◽  
Vol 6 (8) ◽  
pp. 87
Author(s):  
Sara Ferreira ◽  
Mário Antunes ◽  
Manuel E. Correia
Keyword(s):  
Machine Learning ◽  
Digital Forensics ◽  
State Of The Art ◽  
Forensic Analysis ◽  
Third Party ◽  
Support Vector ◽  
Multimedia Content ◽  
Digital Forensic ◽  
Video Frames ◽  
Forensic Tools

Deepfake and manipulated digital photos and videos are being increasingly used in a myriad of cybercrimes. Ransomware, the dissemination of fake news, and digital kidnapping-related crimes are the most recurrent, in which tampered multimedia content has been the primordial disseminating vehicle. Digital forensic analysis tools are being widely used by criminal investigations to automate the identification of digital evidence in seized electronic equipment. The number of files to be processed and the complexity of the crimes under analysis have highlighted the need to employ efficient digital forensics techniques grounded on state-of-the-art technologies. Machine Learning (ML) researchers have been challenged to apply techniques and methods to improve the automatic detection of manipulated multimedia content. However, the implementation of such methods have not yet been massively incorporated into digital forensic tools, mostly due to the lack of realistic and well-structured datasets of photos and videos. The diversity and richness of the datasets are crucial to benchmark the ML models and to evaluate their appropriateness to be applied in real-world digital forensics applications. An example is the development of third-party modules for the widely used Autopsy digital forensic application. This paper presents a dataset obtained by extracting a set of simple features from genuine and manipulated photos and videos, which are part of state-of-the-art existing datasets. The resulting dataset is balanced, and each entry comprises a label and a vector of numeric values corresponding to the features extracted through a Discrete Fourier Transform (DFT). The dataset is available in a GitHub repository, and the total amount of photos and video frames is 40,588 and 12,400, respectively. The dataset was validated and benchmarked with deep learning Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) methods; however, a plethora of other existing ones can be applied. Generically, the results show a better F1-score for CNN when comparing with SVM, both for photos and videos processing. CNN achieved an F1-score of 0.9968 and 0.8415 for photos and videos, respectively. Regarding SVM, the results obtained with 5-fold cross-validation are 0.9953 and 0.7955, respectively, for photos and videos processing. A set of methods written in Python is available for the researchers, namely to preprocess and extract the features from the original photos and videos files and to build the training and testing sets. Additional methods are also available to convert the original PKL files into CSV and TXT, which gives more flexibility for the ML researchers to use the dataset on existing ML frameworks and tools.

NLP-based digital forensic investigation platform for online communications

2021 ◽  
Vol 104 ◽  
pp. 102210
Author(s):  
Dongming Sun ◽  
Xiaolu Zhang ◽  
Kim-Kwang Raymond Choo ◽  
Liang Hu ◽  
Feng Wang
Keyword(s):  
Forensic Investigation ◽  
Digital Forensic ◽  
Online Communications

scholarly journals Proof-of-Prestige: A Useful Work Reward System for Unverifiable Tasks

2021 ◽  
Vol 21 (2) ◽  
pp. 1-27
Author(s):  
Michał Król ◽  
Alberto Sonnino ◽  
Mustafa Al-Bassam ◽  
Argyrios G. Tasiopoulos ◽  
Etienne Rivière ◽  
...  
Keyword(s):  
Mobile Application ◽  
Reward System ◽  
Fair Exchange ◽  
Content Dissemination ◽  
Collusion Attacks ◽  
Computational Overhead ◽  
Smart Contract ◽  
Memory Footprint ◽  
Vast Range ◽  
Over Time

As cryptographic tokens and altcoins are increasingly being built to serve as utility tokens, the notion of useful work consensus protocols is becoming ever more important. With useful work consensus protocols, users get rewards after they have carried out some specific tasks useful for the network. While in some cases the proof of some utility or service can be provided, the majority of tasks are impossible to verify reliably. To deal with such cases, we design “Proof-of-Prestige” (PoP)—a reward system that can run directly on Proof-of-Stake (PoS) blockchains or as a smart contract on top of Proof-of-Work (PoW) blockchains. PoP introduces “prestige,” which is a volatile resource that, in contrast to coins, regenerates over time. Prestige can be gained by performing useful work, spent when benefiting from services, and directly translates to users minting power. Our scheme allows us to reliably reward decentralized workers while keeping the system free for the end-users. PoP is resistant against Sybil and collusion attacks and can be used with a vast range of unverifiable tasks. We build a simulator to assess the cryptoeconomic behavior of the system and deploy a full prototype of a content dissemination platform rewarding its participants. We implement the blockchain component on both Ethereum (PoW) and Cosmos (PoS), provide a mobile application, and connect it with our scheme with a negligible memory footprint. Finally, we adapt a fair exchange protocol allowing us to atomically exchange files for rewards also in scenarios where not all the parties have Internet connectivity. Our evaluation shows that even for large Ethereum traces, PoP introduces sub-millisecond computational overhead for miners in Cosmos and less than 0.013$ smart contract invocation cost for users in Ethereum.

scholarly journals An Active and Passive Reputation Method for Secure Wideband Spectrum Sensing Based on Blockchain

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1346
Author(s):  
Xinyu Xie ◽  
Zhuhua Hu ◽  
Min Chen ◽  
Yaochi Zhao ◽  
Yong Bai
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Sampling Rate ◽  
Radio Networks ◽  
Single Node ◽  
Decision Algorithm ◽  
Collusion Attacks ◽  
Wideband Spectrum Sensing

Spectrum is a kind of non-reproducible scarce strategic resource. A secure wideband spectrum sensing technology provides the possibility for the next generation of ultra-dense, ultra-large-capacity communications to realize the shared utilization of spectrum resources. However, for the open collaborative sensing in cognitive radio networks, the collusion attacks of malicious users greatly affect the accuracy of the sensing results and the security of the entire network. To address this problem, this paper proposes a weighted fusion decision algorithm by using the blockchain technology. The proposed algorithm divides the single-node reputation into active reputation and passive reputation. Through the proposed token threshold concept, the active reputation is set to increase the malicious cost of the node; the passive reputation of the node is determined according to the historical data and recent performance of the blockchain. The final node weight is obtained by considering both kinds of reputation. The proposed scheme can build a trust-free platform for the cognitive radio collaborative networks. Compared with the traditional equal-gain combination algorithm and the centralized sensing algorithm based on the beta reputation system, the simulation results show that the proposed algorithm can obtain reliable sensing results with a lower number of assistants and sampling rate, and can effectively resist malicious users’ collusion attacks. Therefore, the security and the accuracy of cooperative spectrum sensing can be significantly improved in cognitive radio networks.

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