scholarly journals TKRD: Trusted kernel rootkit detection for cybersecurity of VMs based on machine learning and memory forensic analysis

2019 ◽  
Vol 16 (4) ◽  
pp. 2650-2667 ◽  
Author(s):  
Xiao Wang ◽  
◽  
Jianbiao Zhang ◽  
Ai Zhang ◽  
Jinchang Ren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning And Memory ◽  
Forensic Analysis ◽  
Rootkit Detection

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.

scholarly journals Botnet Forensic Analysis Using Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Anchit Bijalwan
Keyword(s):  
Machine Learning ◽  
Ensemble Classifier ◽  
Forensic Analysis ◽  
Modus Operandi ◽  
Botnet Detection ◽  
Quality Of Results ◽  
Proposed Model ◽  
Improve Accuracy ◽  
Rapid Pace

Botnet forensic analysis helps in understanding the nature of attacks and the modus operandi used by the attackers. Botnet attacks are difficult to trace because of their rapid pace, epidemic nature, and smaller size. Machine learning works as a panacea for botnet attack related issues. It not only facilitates detection but also helps in prevention from bot attack. The proposed inquisition model endeavors improved quality of results by comprehensive botnet detection and forensic analysis. This scenario has been applied in eight different combinations of ensemble classifier technique to detect botnet evidence. The study is also compared to the ensemble-based classifiers with the single classifier using different parameters. The results exhibit that the proposed model can improve accuracy over a single classifier.

scholarly journals A Kernel Rootkit Detection Approach Based on Virtualization and Machine Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 91657-91666
Author(s):  
Donghai Tian ◽  
Rui Ma ◽  
Xiaoqi Jia ◽  
Changzhen Hu
Keyword(s):  
Machine Learning ◽  
Detection Approach ◽  
Rootkit Detection

scholarly journals Machine Learning Based Automotive Forensic Analysis for Mobile Applications Using Data Mining

2015 ◽  
Vol 16 (2) ◽  
pp. 350
Author(s):  
MD. Hussain Khan ◽  
G. Pradeepini
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Mobile Apps ◽  
Forensic Analysis ◽  
Evaluation Framework ◽  
Security Evaluation ◽  
Analysis Framework ◽  
Security Issues ◽  
The People ◽  
Using Data

<p>Phone is a device which provides communication between the people through voice, text, video etc. Now a day’s people may leave without food but not without using phones. No of operating systems are working with various versions and various security issues are working. Security is very important task in Mobiles and mobile apps. To improve the security status of mobiles, existing methodology is using cloud computing and data mining. Out traditional method is named as MobSafe to identify the mobile apps antagonism or graciousness. In the proposed system, we adopt Android Security Evaluation Framework (ASEF) and Static Android Analysis Framework (SAAF).In this paper, our proposed system works on machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.</p>

scholarly journals Risk-utility tradeoff shapes memory strategies for evolving patterns

2021 ◽  
Author(s):  
Oskar H Schnaack ◽  
Luca Peliti ◽  
Armita Nourmohammad
Keyword(s):  
Machine Learning ◽  
Learning And Memory ◽  
Memory Storage ◽  
Fine Tuning ◽  
Immune Memory ◽  
Memory Strategies ◽  
High Affinity ◽  
General Guideline ◽  
Dynamic Patterns

Keeping a memory of evolving stimuli is ubiquitous in biology, an example of which is immune memory for evolving pathogens. However, learning and memory storage for dynamic patterns still pose challenges in machine learning. Here, we introduce an analytical energy-based framework to address this problem. By accounting for the tradeoff between utility in keeping a high-affinity memory and the risk in forgetting some of the diverse stimuli, we show that a moderate tolerance for risk enables a repertoire to robustly classify evolving patterns, without much fine-tuning. Our approach offers a general guideline for learning and memory storage in systems interacting with diverse and evolving signals.

scholarly journals Machine learning analysis identifies Drosophila Grunge/Atrophin as an important learning and memory gene required for memory retention and social learning

2017 ◽  
Author(s):  
Balint Z Kacsoh ◽  
Casey S. Greene ◽  
Giovanni Bosco
Keyword(s):  
Machine Learning ◽  
Learning And Memory ◽  
High Throughput ◽  
Mushroom Body ◽  
Neuronal Development ◽  
Active Role ◽  
Long Term Memory ◽  
Memory Retention ◽  
Genome Wide Data ◽  
High Throughput Experiments

ABSTRACTHigh throughput experiments are becoming increasingly common, and scientists must balance hypothesis driven experiments with genome wide data acquisition. We sought to predict novel genes involved in Drosophila learning and long-term memory from existing public high-throughput data. We performed an analysis using PILGRM, which analyzes public gene expression compendia using machine learning. We evaluated the top prediction alongside genes involved in learning and memory in IMP, an interface for functional relationship networks. We identified Grunge/Atrophin (Gug/Atro), a transcriptional repressor, histone deacetylase, as our top candidate. We find, through multiple, distinct assays, that Gug has an active role as a modulator of memory retention in the fly and its function is required in the adult mushroom body. Depletion of Gug specifically in neurons of the adult mushroom body, after cell division and neuronal development is complete, suggests that Gug function is important for memory retention through regulation of neuronal activity, and not by altering neurodevelopment. Our study provides a previously uncharacterized role for Gug as a possible regulator of neuronal plasticity at the interface of memory retention and memory extinction.

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