Expanding the data capacity of QR codes using multiple compression algorithms and base64 encode/decode

The Quick Response (QR) code is an enhancement from one dimensional barcode which was used to store limited capacity of information. The QR code has the capability to encode various data formats and languages. Several techniques were suggested by researchers to increase the data contents. One of the technique to increase data capacity is by compressing the data and encode it with a suitable data encoder. This study focuses on the selection of compression algorithms and use base64 encoder/decoder to increase the capacity of data which is to be stored in the QR code. The result will be compared with common technique to get the efficiency among the selected compression algorithm after the data was encoded with base64 encoder/decoder

Penerapan QR Code pada Aplikasi Lihat.in

Dalam proses ber-internet terdapat suatu tautan url yang berisikan data , Pemendekkan url memerankan peranan penting dalam ber-internet, pemendekkan url digunakan untuk mempermudah serta mengingat sebuah url di media social seperti facebook dan twitter . Aplikasi Short URL adalah sebuah program yang memungkinkan pengguna untuk mengonversi URL panjang menjadi URL pendek dengan karakter yang lebih sedikit. Program ini sangat berguna dalam memudahkan pengguna dalam berbagi link melalui media sosial, email, atau aplikasi pesan singkat. Dalam aplikasi ini, pengguna dapat memasukkan URL panjang dan kemudian program akan menghasilkan URL pendek yang dapat digunakan untuk mengakses halaman web yang sama dengan URL panjang. Pengguna juga dapat melihat statistik penggunaan URL pendek, seperti jumlah klik dan waktu klik terakhir. Aplikasi Short URL dapat membantu mengurangi kesalahan pengetikan pada URL, serta dapat meningkatkan keamanan data pengguna dengan mengenkripsi URL panjang sehingga tidak mudah ditebak. Untuk mencegah data yang bocor digunakan Base64 dalam hal encoding URL, serta algoritma Base64 adalah sebuah program yang memungkinkan pengguna untuk mengonversi URL panjang menjadi URL pendek yang terdiri dari karakter-karakter Base64. Serta efesiensi pada qr code untuk menyingkat waktu dalam melihat short url

Expanding the Data Capacity of QR Codes Using Multiple Compression Algorithms and Base64 Encode/Decode

The Quick Response (QR) code is an enhancement from one dimensional barcode which was used to store limited capacity of information. The QR code has the capability to encode various data formats and languages. Several techniques were suggested by researchers to increase the data contents. One of the technique to increase data capacity is by compressing the data and encode it with a suitable data encoder. This study focuses on the selection of compression algorithms and use base64 encoder/decoder to increase the capacity of data which is to be stored in the QR code. The result will be compared with common technique to get the efficiency among the selected compression algorithm after the data was encoded with base64 encoder/decoder

XSS attack detection based on machine learning

As the popularity of web-based applications grows, so does the number of individuals who use them. The vulnerabilities of those programs, however, remain a concern. Cross-site scripting is a very prevalent assault that is simple to launch but difficult to defend against. That is why it is being studied. The current study focuses on artificial systems, such as machine learning, which can function without human interaction. As technology advances, the need for maintenance is increasing. Those maintenance systems, on the other hand, are becoming more complex. This is why machine learning technologies are becoming increasingly important in our daily lives. This study use supervised machine learning to protect against cross-site scripting, which allows the computer to find an algorithm that can identify vulnerabilities. A large collection of datasets serves as the foundation for this technique. The model will be equipped with functions extracted from datasets that will allow it to learn the model of such an attack by filtering it using common Javascript symbols or possible Document Object Model (DOM) syntax. As long as the research continues, the best conjugate algorithms will be discovered that can successfully fight against cross-site scripting. It will do multiple comparisons between different classification methods on their own or in combination to determine which one performs the best.À medida que a popularidade dos aplicativos da internet cresce, aumenta também o número de indivíduos que os utilizam. No entanto, as vulnerabilidades desses programas continuam a ser uma preocupação para o uso da internet no dia-a-dia. O cross-site scripting é um ataque muito comum que é simples de lançar, mas difícil de-se defender. Por isso, é importante que este ataque possa ser estudado. A tese atual concentra-se em sistemas baseados na utilização de inteligência artificial e Aprendizagem Automática (ML), que podem funcionar sem interação humana. À medida que a tecnologia avança, a necessidade de manutenção também vai aumentando. Por outro lado, estes sistemas vão tornando-se cada vez mais complexos. É, por isso, que as técnicas de machine learning torna-se cada vez mais importantes nas nossas vidas diárias. Este trabalho baseia-se na utilização de Aprendizagem Automática para proteger contra o ataque cross-site scripting, o que permite ao computador encontrar um algoritmo que tem a possibilidade de identificar as vulnerabilidades. Uma grande coleção de conjuntos de dados serve como a base para a abordagem proposta. A máquina virá ser equipada com o processamento de linguagem natural, o que lhe permite a aprendizagem do padrão de tal ataque e filtrando-o com o uso da mesma linguagem, javascript, que é possível usar para controlar os objectos DOM (Document Object Model). Enquanto a pesquisa continua, os melhores algoritmos conjugados serão descobertos para que possam prever com sucesso contra estes ataques. O estudo fará várias comparações entre diferentes métodos de classificação por si só ou em combinação para determinar o que tiver melhor desempenho

Mustererkennungsbasierte Verteidgung gegen gezielte Angriffe

The speed at which everything and everyone is being connected considerably outstrips the rate at which effective security mechanisms are introduced to protect them. This has created an opportunity for resourceful threat actors which have specialized in conducting low-volume persistent attacks through sophisticated techniques that are tailored to specific valuable targets. Consequently, traditional approaches are rendered ineffective against targeted attacks, creating an acute need for innovative defense mechanisms. This thesis aims at supporting the security practitioner in bridging this gap by introducing a holistic strategy against targeted attacks that addresses key challenges encountered during the phases of detection, analysis and response. The structure of this thesis is therefore aligned to these three phases, with each one of its central chapters taking on a particular problem and proposing a solution built on a strong foundation on pattern recognition and machine learning. In particular, we propose a detection approach that, in the absence of additional authentication mechanisms, allows to identify spear-phishing emails without relying on their content. Next, we introduce an analysis approach for malware triage based on the structural characterization of malicious code. Finally, we introduce MANTIS, an open-source platform for authoring, sharing and collecting threat intelligence, whose data model is based on an innovative unified representation for threat intelligence standards based on attributed graphs. As a whole, these ideas open new avenues for research on defense mechanisms and represent an attempt to counteract the imbalance between resourceful actors and society at large.In unserer heutigen Welt sind alle und alles miteinander vernetzt. Dies bietet mächtigen Angreifern die Möglichkeit, komplexe Verfahren zu entwickeln, die auf spezifische Ziele angepasst sind. Traditionelle Ansätze zur Bekämpfung solcher Angriffe werden damit ineffektiv, was die Entwicklung innovativer Methoden unabdingbar macht. Die vorliegende Dissertation verfolgt das Ziel, den Sicherheitsanalysten durch eine umfassende Strategie gegen gezielte Angriffe zu unterstützen. Diese Strategie beschäftigt sich mit den hauptsächlichen Herausforderungen in den drei Phasen der Erkennung und Analyse von sowie der Reaktion auf gezielte Angriffe. Der Aufbau dieser Arbeit orientiert sich daher an den genannten drei Phasen. In jedem Kapitel wird ein Problem aufgegriffen und eine entsprechende Lösung vorgeschlagen, die stark auf maschinellem Lernen und Mustererkennung basiert. Insbesondere schlagen wir einen Ansatz vor, der eine Identifizierung von Spear-Phishing-Emails ermöglicht, ohne ihren Inhalt zu betrachten. Anschliessend stellen wir einen Analyseansatz für Malware Triage vor, der auf der strukturierten Darstellung von Code basiert. Zum Schluss stellen wir MANTIS vor, eine Open-Source-Plattform für Authoring, Verteilung und Sammlung von Threat Intelligence, deren Datenmodell auf einer innovativen konsolidierten Graphen-Darstellung für Threat Intelligence Stardards basiert. Wir evaluieren unsere Ansätze in verschiedenen Experimenten, die ihren potentiellen Nutzen in echten Szenarien beweisen. Insgesamt bereiten diese Ideen neue Wege für die Forschung zu Abwehrmechanismen und erstreben, das Ungleichgewicht zwischen mächtigen Angreifern und der Gesellschaft zu minimieren

Using Botnet Technologies to Counteract Network Traffic Analysis

Botnets have been problematic for over a decade. They are used to launch malicious activities including DDoS (Distributed-Denial-of-Service), spamming, identity theft, unauthorized bitcoin mining and malware distribution. A recent nation-wide DDoS attacks caused by the Mirai botnet on 10/21/2016 involving 10s of millions of IP addresses took down Twitter, Spotify, Reddit, The New York Times, Pinterest, PayPal and other major websites. In response to take-down campaigns by security personnel, botmasters have developed technologies to evade detection. The most widely used evasion technique is DNS fast-flux, where the botmaster frequently changes the mapping between domain names and IP addresses of the C&C server so that it will be too late or too costly to trace the C&C server locations. Domain names generated with Domain Generation Algorithms (DGAs) are used as the \u27rendezvous\u27 points between botmasters and bots. This work focuses on how to apply botnet technologies (fast-flux and DGA) to counteract network traffic analysis, therefore protecting user privacy. A better understanding of botnet technologies also helps us be pro-active in defending against botnets. First, we proposed two new DGAs using hidden Markov models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) which can evade current detection methods and systems. Also, we developed two HMM-based DGA detection methods that can detect the botnet DGA-generated domain names with/without training sets. This helps security personnel understand the botnet phenomenon and develop pro-active tools to detect botnets. Second, we developed a distributed proxy system using fast-flux to evade national censorship and surveillance. The goal is to help journalists, human right advocates and NGOs in West Africa to have a secure and free Internet. Then we developed a covert data transport protocol to transform arbitrary message into real DNS traffic. We encode the message into benign-looking domain names generated by an HMM, which represents the statistical features of legitimate domain names. This can be used to evade Deep Packet Inspection (DPI) and protect user privacy in a two-way communication. Both applications serve as examples of applying botnet technologies to legitimate use. Finally, we proposed a new protocol obfuscation technique by transforming arbitrary network protocol into another (Network Time Protocol and a video game protocol of Minecraft as examples) in terms of packet syntax and side-channel features (inter-packet delay and packet size). This research uses botnet technologies to help normal users have secure and private communications over the Internet. From our botnet research, we conclude that network traffic is a malleable and artificial construct. Although existing patterns are easy to detect and characterize, they are also subject to modification and mimicry. This means that we can construct transducers to make any communication pattern look like any other communication pattern. This is neither bad nor good for security. It is a fact that we need to accept and use as best we can

Um estudo sobre pareamento aproximado para busca por similaridade : técnicas, limitações e melhorias para investigações forenses digitais

Orientador: Marco Aurélio Amaral HenriquesTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A forense digital é apenas um dos ramos da Ciência da Computação que visa investigar e analisar dispositivos eletrônicos na busca por evidências de crimes. Com o rápido aumento da capacidade de armazenamento de dados, é necessário o uso de procedimentos automatizados para lidar com o grande volume de dados disponíveis atualmente, principalmente em investigações forenses, nas quais o tempo é um recurso escasso. Uma possível abordagem para tornar o processo mais eficiente é através da técnica KFF (Filtragem por arquivos conhecidos - Known File Filtering), onde uma lista de objetos de interesse é usada para reduzir/separar dados para análise. Com um banco de dados de hashes destes objetos, o examinador realiza buscas no dispositivo de destino sob investigação por qualquer item que seja igual ao buscado. No entanto, devido a limitações nas funções criptográficas de hash (incapacidade de detectar objetos semelhantes), novos métodos foram projetados baseando-se em funções de Pareamento Aproximado (ou Approximate Matching) (AM). Estas funções aparecem como candidatos para realizar buscas uma vez que elas têm a capacidade de identificar similaridade (no nível de bits) de uma maneira muito eficiente, criando e comparando representações compactas de objetos (conhecidos como resumos). Neste trabalho, apresentamos as funções de Pareamento Aproximado. Mostramos algumas das ferramentas de AM mais conhecidas e apresentamos as Estratégias de Busca por Similaridade baseadas em resumos, capazes de realizar a busca de similaridade (usando AM) de maneira mais eficiente, principalmente ao lidar com grandes conjuntos de dados. Realizamos também uma análise detalhada das estratégias atuais e, dado que as mesmas trabalham somente com algumas ferramentas específicas de AM, nós propomos uma nova abordagem baseada em uma ferramenta diferente que possui boas características para investigações forenses. Além disso, abordamos algumas limitações das ferramentas atuais de AM em relação ao processo de detecção de similaridade, onde muitas comparações apontadas como semelhantes, são de fato falsos positivos; as ferramentas geralmente são enganadas por blocos comuns (dados comuns em muitos objetos diferentes). Ao remover estes blocos dos resumos de AM, obtemos melhorias significativas na detecção de objetos similares. Também apresentamos neste trabalho uma análise teórica detalhada das capacidades de detecção da ferramenta de AM sdhash e propomos melhorias em sua função de comparação, onde a versão aprimorada apresenta uma medida de similaridade (score) mais precisa. Por último, novas aplicações de AM são apresentadas e analisadas: uma de identificação rápida de arquivos por meio de amostragem de dados e outra de identificação eficiente de impressões digitais. Esperamos que profissionais da área forense e de outras áreas relacionadas se beneficiem de nosso estudo sobre AM para resolver seus problemasAbstract: Digital forensics is a branch of Computer Science aiming at investigating and analyzing electronic devices in the search for crime evidence. With the rapid increase in data storage capacity, the use of automated procedures to handle the massive volume of data available nowadays is required, especially in forensic investigations, in which time is a scarce resource. One possible approach to make the process more efficient is the Known File Filter (KFF) technique, where a list of interest objects is used to reduce/separate data for analysis. Holding a database of hashes of such objects, the examiner performs lookups for matches against the target device under investigation. However, due to limitations over cryptographic hash functions (inability to detect similar objects), new methods have been designed based on Approximate Matching (AM). They appear as suitable candidates to perform this process because of their ability to identify similarity (bytewise level) in a very efficient way, by creating and comparing compact representations of objects (a.k.a. digests). In this work, we present the Approximate Matching functions. We show some of the most known AM tools and present the Similarity Digest Search Strategies (SDSS), capable of performing the similarity search (using AM) more efficiently, especially when dealing with large data sets. We perform a detailed analysis of current SDSS approaches and, given that current strategies only work for a few particular AM tools, we propose a new strategy based on a different tool that has good characteristics for forensic investigations. Furthermore, we address some limitations of current AM tools regarding the similarity detection process, where many matches pointed out as similar, are indeed false positives; the tools are usually misled by common blocks (pieces of data common in many different objects). By removing such blocks from AM digests, we obtain significant improvements in the detection of similar data. We also present a detailed theoretical analysis of the capabilities of sdhash AM tool and provide some improvements to its comparison function, where our improved version has a more precise similarity measure (score). Lastly, new applications of AM are presented and analyzed: One for fast file identification based on data samples and another for efficient fingerprint identification. We hope that practitioners in the forensics field and other related areas will benefit from our studies on AM when solving their problemsDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétrica23038.007604/2014-69CAPE