On Efficiency of Distributed Password Recovery

One of the major challenges in digital forensics today is data encryption. Due to the leaked information about unlawful sniffing, many users decided to protect their data by encryption. In case of criminal activities, forensic experts are challenged how to decipher suspect\u27s data that are subject to investigation. A common method how to overcome password-based protection is a brute force password recovery using GPU-accelerated hardware. This approach seems to be expensive. This paper presents an alternative approach using task distribution based on BOINC platform. The cost, time and energy efficiency of this approach is discussed and compared to the GPU-based solution

A Fast and Secure Way to Prevent SQL Injection Attacks using Bitslice Technique and GPU Support

Most of the web applications are associated with database as back-end so there are possibilities of SQL injection attacks (SQLIA) on it. Even SQLIA is among top ten attacks according to Open Web Application Security Project (OWASP) but still approaches are not able to give proper solution to this problem. Numbers of measures are also discovered to overcome this attack, but which measure is more convenient and can also provide fast access to application without compromising the security is also a major concern. Some existing approaches are good in security but they are not efficient to handle large user’s requests. To overcome these two issues at the same moment Bitslice AES encryption and parallel AES encryption using CUDA are used to prevent this attack. Bitslice AES uses a non-standard representation and view the processor as a SIMD computer, i.e. as 64 parallel one bit processors computing the same instruction. As AES round functions are good candidate for parallel computations, AES encryption using CUDA gives tremendous encryptions per second and application response remains constant even if users requests increase

Análisis de herramientas y técnicas de apoyo a la recuperación de información cifrada

El proyecto aborda el problema de la optimización de los procesos de descifrado de evidencias informáticas protegidas con contraseña. En este proyecto se analizan alternativas tecnológicas para la realización de una plataforma de tratamiento masivo de información cifrada utilizando la tecnología GPGPU (General-Purpose Computing on Graphics Processing Units) para procesar datos. Dentro de este contexto, también se estudia la viabilidad de la utilización de esta tecnología GPU dentro de un entorno de virtualización basado en Xen y se adaptan soluciones existentes para poder utilizar la tarjeta gráfica nativa por los huéspedes virtuales. El objetivo final que se persigue es posibilitar la integración de distintas herramientas de descifrado en una misma plataforma con independencia del sistema operativo para el que fueron desarrolladas.The project addresses the problem of decrypting password-protected computer evidences. This project will analyze differents technological alternatives in order to achieve the realization of a decryption platform using the GPGPU technology (General-Purpose Computing on Graphics Processing Units) to process data. Within this context, the project examines the feasibility of using this GPU technology within a virtualization environment based on Xen and adapt existing solutions to use the native graphics card inside the virtual guests. The ultimate goal pursued is to enable the integration of various cracking tools on the same system despite of the operating system for which they were developed

Programming Languages and Systems

This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

Programming Languages and Systems

This open access book constitutes the proceedings of the 31st European Symposium on Programming, ESOP 2022, which was held during April 5-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 21 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. They deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

FACIAL IDENTIFICATION FOR DIGITAL FORENSIC

Forensic facial recognition has become an essential requirement in criminal investigations as a result of the emergence of electronic devices, such as mobile phones and computers, and the huge volume of existing content. Forensic facial recognition goes beyond facial recognition in that it deals with facial images under unconstrained and non-ideal conditions, such as low image resolution, varying facial orientation, poor illumination, a wide range of facial expressions, and the presence of accessories. In addition, digital forensic challenges do not only concern identifying an individual but also include understanding the context, acknowledging the relationships between individuals, tracking, and numbers of advanced questions that help reduce the cognitive load placed on the investigator. This thesis proposes a multi-algorithmic fusion approach by using multiple commercial facial recognition systems to overcome particular weaknesses in singular approaches to obtain improved facial identification accuracy. The advantage of focusing on commercial systems is that they release the forensic team from developing and managing their own solutions and, subsequently, also benefit from state-of-the-art updates in underlying recognition performance. A set of experiments was conducted to evaluate these commercial facial recognition systems (Neurotechnology, Microsoft, and Amazon Rekognition) to determine their individual performance using facial images with varied conditions and to determine the benefits of fusion. Two challenging facial datasets were identified for the evaluation; they represent a challenging yet realistic set of digital forensics scenarios collected from publicly available photographs. The experimental results have proven that using the developed fusion approach achieves a better facial vi identification rate as the best evaluated commercial system has achieved an accuracy of 67.23% while the multi-algorithmic fusion system has achieved an accuracy of 71.6%. Building on these results, a novel architecture is proposed to support the forensic investigation concerning the automatic facial recognition called Facial-Forensic Analysis System (F-FAS). The F-FAS is an efficient design that analyses the content of photo evidence to identify a criminal individual. Further, the F-FAS architecture provides a wide range of capabilities that will allow investigators to perform in-depth analysis that can lead to a case solution. Also, it allows investigators to find answers about different questions, such as individual identification, and identify associations between artefacts (facial social network) and presents them in a usable and visual form (geolocation) to draw a wider picture of a crime. This tool has also been designed based on a case management concept that helps to manage the overall system and provide robust authentication, authorisation, and chain of custody. Several experts in the forensic area evaluated the contributions of theses and a novel approach idea and it was unanimously agreed that the selected research problem was one of great validity. In addition, all experts have demonstrated support for experiments’ results and they were impressed by the suggested F-FAS based on the context of its functions.Republic of Iraq / Ministry of Higher Education and Scientific Research – Baghdad Universit

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

CACIC 2015 : XXI Congreso Argentino de Ciencias de la Computación. Libro de actas

Actas del XXI Congreso Argentino de Ciencias de la Computación (CACIC 2015), realizado en Sede UNNOBA Junín, del 5 al 9 de octubre de 2015.Red de Universidades con Carreras en Informática (RedUNCI