PREDIKSI POLA KECENDERUNGAN PENYERANGAN WEB SERVER(STUDI KASUS : tif.uin-suska.ac.id)

Log merupakan suatu file yang berisi data atau informasi mengenai seluruh aktifitas pada server baik yang ditimbulkan dari server maupun client aktifitas berupa client mengakses keserver yang menyebabkan error atau aktifitas biasa. Log tif.uin-suska.ac.id terdapat daftar tindakan, penyerangan terhadap log file web server di UIN SUSKA sehingga dapat mengantisipasi apabila ada pengunjung yang mencoba melakukan penyerangan (anomaly detection). Dengan semakin banyaknya data log, terdapat record pada log yang tidak terkait dengan proses serangan, record tersebut disebut dengan false alarm. Untuk mengurangi false alarm yang disebabkan banyaknya data log,maka akan dilakukan filtering dan statistik terhadap log menggunakan metode hidden markov models.Data yang diperoleh sebanyak 728031 baris log terdapat 405591 baris log GET respon code normal dan 39066 baris log respon kode yang dapat di observasi, dan data yang diperoleh dari log website tif.uin-suska.ac.id yang teramati mulai dari tanggal 20 januari sampai dengan 3 agustus 2016, dari penelitian ini diperoleh error 500 sebesar 0.002 %, 404 sebesar 8.93 %, 403 sebesar 1.31 % dari jumlah data keseluruhan dengan precision53 % serta recall82 % dan pola kecenderungan penyerangan, yaitu kecenderungan error berbanding lurus dengan jumlah kunjungan yang menyebabkan error.Kata Kunci : Penyerangan, Log, IP Address, Hidden Markov Model, Websit

Smartphone traffic characteristics and context dependencies

Smartphone traffic contributes a considerable amount to Internet traffic. The increasing popularity of smartphones in recent reports suggests that smartphone traffic has been growing 10 times faster than traffic generated from fixed networks. However, little is known about the characteristics of smartphone traffic. A few recent studies have analyzed smartphone traffic and given some insight into its characteristics. However, many questions remain inadequately answered. This thesis analyzes traffic characteristics and explores some important issues related to smartphone traffic. An application on the Android platform was developed to capture network traffic. A user study was then conducted where 39 participants were given HTC Magic phones with data collection applications installed for 37 days. The collected data was analyzed to understand the workload characteristics of smartphone traffic and study the relationship between participant contexts and smartphone usage. The collected dataset suggests that even in a small group of participants a variety of very different smartphone usage patterns occur. Participants accessed different types of Internet content at different times and under different circumstances. Differences between the usage of Wi-Fi and cellular networks for individual participants are observed. Download-intensive activities occurred more frequently over Wi-Fi networks. Dependencies between smartphone usage and context (where they are, who they are with, at what time, and over which physical interface) are investigated in this work. Strong location dependencies on an aggregate and individual user level are found. Potential relationships between times of the day and access patterns are investigated. A time-of-day dependent access pattern is observed for some participants. Potential relationships between movement and proximity to other users and smartphone usage are also investigated. The collected data suggests that moving participants used map applications more. Participants generated more traffic and primarily downloaded apps when they were alone. The analyses performed in this thesis improve basic understanding and knowledge of smartphone use in different scenarios

An integrated mobile content recommendation system

Many features have been added to mobile devices to assist the user's information consumption. However, there are limitations due to information overload on the devices, hardware usability and capacity. As a result, content filtering in a mobile recommendation system plays a vital role in the solution to this problem. A system that utilises content filtering can recommend content which matches a user's needs based on user preferences with a higher accuracy rate. However, mobile content recommendation systems have problems and limitations related to cold start and sparsity. The problems can be viewed as first time connection and first content rating for non-interactive recommendation systems where information is insufficient to predict mobile content which will match with a user's needs. In addition, how to find relevant items for the content recommendation system which are related to a user's profile is also a concern. An integrated model that combines the user group identification and mobile content filtering for mobile content recommendation was proposed in this study in order to address the current limitations of the mobile content recommendation system. The model enhances the system by finding the relevant content items that match with a user's needs based on the user's profile. A prototype of the client-side user profile modelling is also developed to demonstrate the concept. The integrated model applies clustering techniques to determine groups of users. The content filtering implemented classification techniques to predict the top content items. After that, an adaptive association rules technique was performed to find relevant content items. These approaches can help to build the integrated model. Experimental results have demonstrated that the proposed integrated model performs better than the comparable techniques such as association rules and collaborative filtering. These techniques have been used in several recommendation systems. The integrated model performed better in terms of finding relevant content items which obtained higher accuracy rate of content prediction and predicted successful recommended relevant content measured by recommendation metrics. The model also performed better in terms of rules generation and content recommendation generation. Verification of the proposed model was based on real world practical data. A prototype mobile content recommendation system with client-side user profile has been developed to handle the revisiting user issue. In addition, context information, such as time-of-day and time-of-week, could also be used to enhance the system by recommending the related content to users during different time periods. Finally, it was shown that the proposed method implemented fewer rules to generate recommendation for mobile content users and it took less processing time. This seems to overcome the problems of first time connection and first content rating for non-interactive recommendation systems

Revisión del concepto de televisión social y sus audiencias

Capítulo publicado en el libro: La participación de la audiencia en la televisión: de la audiencia activa a la social, publicado por la AIMC, Asociación para la Investigación de Medios de Comunicación. Las autoras del capítulo son también las coordinadoras de la monografía[Resumen]: En un contexto mediático tan cambiante, se hace necesario abordar las nuevas realidades de la televisión social y la audiencia social desde un punto de vista más teórico. En este capítulo se realizará un repaso de las distintas y cambiantes definiciones de televisión social para desembocar en una nueva definición de las autoras. Para ello se hará referencia a las principales corrientes de análisis de la televisión social procedentes de diversas áreas de conocimiento. Posteriormente se estudia una de las actuales conceptualizaciones de la audiencia activa: la audiencia social. La aproximación al concepto de audiencia social se realiza desde diferentes disciplinas con el fin de explicar las principales dimensiones de la misma a la vez que se trata de profundizar en el comportamiento del espectador social y en las nuevas herramientas de medición de esta audiencia

La participación de la audiencia en la televisión: de la audiencia activa a la social

El libro recoge análisis y reflexiones de varios docentes de distintos centros universitarios de España, en torno a distintos aspectos que caracterizan a la audiencia televisiva actual al hilo de los últimos desarrollos tecnológicos. Se trata de una “hoja de ruta” sobre algunas de las claves que se abren, como dice el título, en las nuevas audiencias televisivas o nuevas formas de relacionarse y “consumir” contenidos audiovisuales que emergen a raíz de las nuevas capacidades tecnológicas y los cambios de índole social que estas introducen. Todas las reflexiones y planteamientos que se vierten en este libro están afectando en el día a día a los dos pilares de nuestra asociación: el sector de la publicidad y los medios de comunicación