Forensics Writer Identification using Text Mining and Machine Learning

Constant technological growth has resulted in the danger and seriousness of cyber-attacks, which has recently unmistakably developed in various institutions that have complex Information Technology (IT) infrastructure. For instance, for the last three (3) years, the most horrendous instances of cybercrimes were perceived globally with enormous information breaks, fake news spreading, cyberbullying, crypto-jacking, and cloud computing services. To this end, various agencies improvised techniques to curb this vice and bring perpetrators, both real and perceived, to book in relation to such serious cybersecurity issues. Consequently, Forensic Writer Identification was introduced as one of the most effective remedies to the concerned issue through a stylometry application. Indeed, the Forensic Writer Identification is a complex forensic science technology that utilizes Artificial Intelligence (AI) technology to safeguard, recognize proof, extraction, and documentation of the computer or digital explicit proof that can be utilized by the official courtroom, especially, the investigative officers in case of a criminal issue or just for data analytics. This research\u27s fundamental objective was to scrutinize Forensic Writer Identification technology aspects in twitter authorship analytics of various users globally and apply it to reduce the time to find criminals by providing the Police with the most accurate methodology. As well as compare the accuracy of different techniques. The report shall analytically follow a logical literature review that observes the vital text analysis techniques. Additionally, the research applied agile text mining methodology to extract and analyze various Twitter users\u27 texts. In essence, digital exploration for appropriate academics and scholarly artifacts was affected in various online and offline databases to expedite this research. Forensic Writer Identification for text extraction, analytics have recently appreciated reestablished attention, with extremely encouraging outcomes. In fact, this research presents an overall foundation and reason for text and author identification techniques. Scope of current techniques and applications are given, additionally tending to the issue of execution assessment. Results on various strategies are summed up, and a more inside and out illustration of two consolidated methodologies are introduced. By encompassing textural, algorithms, and allographic, emerging technologies are beginning to show valuable execution levels. Nevertheless, user acknowledgment would play a vital role with regards to the future of technology. To this end, the goal of coming up with a project proposal was to come up with an analytical system that would automate the process of authorship identification methodology in various Web 2.0 Technologies aspects globally, hence addressing the contemporary cybercrime issues

Towards assessing information privacy in microblogging online social networks. The IPAM framework

Les xarxes socials en línia incorporen diferents formes de comunicació interactiva com serveis de microblogs, compartició de fitxers multimèdia o xarxes de contactes professionals. En els últims anys han augmentat els escàndols públics en relació amb pràctiques qüestionables de la indústria de les xarxes socials pel que fa a la privacitat. Així, doncs, cal una avaluació efectiva i eficient del nivell de privacitat en les xarxes socials en línia. El focus de la present tesi és la construcció d'un esquema (IPAM) per a identificar i avaluar el nivell de privacitat proporcionat per les xarxes socials en línia, en particular per als serveis de microblogs. L'objectiu d'IPAM és ajudar els usuaris a identificar els riscos relacionats amb les seves dades. L'esquema també permet comparar el nivell de protecció de la privacitat entre diferents sistemes analitzats, de manera que pugui ser també utilitzat per proveïdors de servei i desenvolupadors per a provar i avaluar els seus sistemes i si les tècniques de privacitat usades són eficaces i suficients.Las redes sociales en línea incorporan diferentes formas de comunicación interactiva como servicios de microblogueo, compartición de ficheros multimedia o redes de contactos profesionales. En los últimos años han aumentado los escándalos públicos relacionados con prácticas cuestionables de la industria de las redes sociales en relación con la privacidad. Así pues, es necesaria una evaluación efectiva y eficiente del nivel de privacidad en las redes sociales en línea. El foco de la presente tesis es la construcción de un esquema (IPAM) para identificar y evaluar el nivel de privacidad proporcionado por las redes sociales en línea, en particular para los servicios de microblogueo. El objetivo de IPAM es ayudar a los usuarios a identificar los riesgos relacionados con sus datos. El esquema también permite comparar el nivel de protección de la privacidad entre diferentes sistemas analizados, de modo que pueda ser también utilizado por proveedores de servicio y desarrolladores para probar y evaluar sus sistemas y si las técnicas de privacidad usadas son eficaces y suficientes.Online social networks (OSNs) incorporate different forms of interactive communication, including microblogging services, multimedia sharing and business networking, among others. In recent years there has been an increase in the number of privacy-related public scandals involving questionable data handling practices in OSNs. This situation calls for an effective and efficient evaluation of the privacy level provided by such services. In this thesis, we take initial steps towards developing an information privacy assessment framework (IPAM framework) to compute privacy scores for online social networks in general, and microblogging OSNs in particular. The aim of the proposed framework is to help users identify personal data-related risks and how their privacy is protected when using one OSN or another. The IPAM framework also allows for a comparison between different systems' privacy protection level. This gives system providers, not only an idea of how they are positioned in the market vis-à-vis their competitors, but also recommendations on how to enhance their services

Supporting Large Scale Communication Systems on Infrastructureless Networks Composed of Commodity Mobile Devices: Practicality, Scalability, and Security.

Infrastructureless Delay Tolerant Networks (DTNs) composed of commodity mobile devices have the potential to support communication applications resistant to blocking and censorship, as well as certain types of surveillance. In this thesis we study the utility, practicality, robustness, and security of these networks. We collected two sets of wireless connectivity traces of commodity mobile devices with different granularity and scales. The first dataset is collected through active installation of measurement software on volunteer users' own smartphones, involving 111 users of a DTN microblogging application that we developed. The second dataset is collected through passive observation of WiFi association events on a university campus, involving 119,055 mobile devices. Simulation results show consistent message delivery performances of the two datasets. Using an epidemic flooding protocol, the large network achieves an average delivery rate of 0.71 in 24 hours and a median delivery delay of 10.9 hours. We show that this performance is appropriate for sharing information that is not time sensitive, e.g., blogs and photos. We also show that using an energy efficient variant of the epidemic flooding protocol, even the large network can support text messages while only consuming 13.7% of a typical smartphone battery in 14 hours. We found that the network delivery rate and delay are robust to denial-of-service and censorship attacks. Attacks that randomly remove 90% of the network participants only reduce delivery rates by less than 10%. Even when subjected to targeted attacks, the network suffered a less than 10% decrease in delivery rate when 40% of its participants were removed. Although structurally robust, the openness of the proposed network introduces numerous security concerns. The Sybil attack, in which a malicious node poses as many identities in order to gain disproportionate influence, is especially dangerous as it breaks the assumption underlying majority voting. Many defenses based on spatial variability of wireless channels exist, and we extend them to be practical for ad hoc networks of commodity 802.11 devices without mutual trust. We present the Mason test, which uses two efficient methods for separating valid channel measurement results of behaving nodes from those falsified by malicious participants.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120779/1/liuyue_1.pd

Real-time road traffic events detection and geo-parsing

Indiana University-Purdue University Indianapolis (IUPUI)In the 21st century, there is an increasing number of vehicles on the road as well as a limited road infrastructure. These aspects culminate in daily challenges for the average commuter due to congestion and slow moving traffic. In the United States alone, it costs an average US driver $1200 every year in the form of fuel and time. Some positive steps, including (a) introduction of the push notification system and (b) deploying more law enforcement troops, have been taken for better traffic management. However, these methods have limitations and require extensive planning. Another method to deal with traffic problems is to track the congested area in a city using social media. Next, law enforcement resources can be re-routed to these areas on a real-time basis. Given the ever-increasing number of smartphone devices, social media can be used as a source of information to track the traffic-related incidents. Social media sites allow users to share their opinions and information. Platforms like Twitter, Facebook, and Instagram are very popular among users. These platforms enable users to share whatever they want in the form of text and images. Facebook users generate millions of posts in a minute. On these platforms, abundant data, including news, trends, events, opinions, product reviews, etc. are generated on a daily basis. Worldwide, organizations are using social media for marketing purposes. This data can also be used to analyze the traffic-related events like congestion, construction work, slow-moving traffic etc. Thus the motivation behind this research is to use social media posts to extract information relevant to traffic, with effective and proactive traffic administration as the primary focus. I propose an intuitive two-step process to utilize Twitter users' posts to obtain for retrieving traffic-related information on a real-time basis. It uses a text classifier to filter out the data that contains only traffic information. This is followed by a Part-Of-Speech (POS) tagger to find the geolocation information. A prototype of the proposed system is implemented using distributed microservices architecture

Detection of Hateful Comments on Social Media

Social media usage has grown tremendously in the contemporary communication landscape. Along with its numerous benefits, some users abuse the channels by spreading hatred, far from the intended purpose of building connections on a personal level. To date, an empirical method for detecting, quantifying, and categorizing hateful comments on social networks comprehensively and proactively is still lacking. Besides, majority of the cases remain unreported due to social confounders such as fear of victimization and the psychological implications of hateful comments, leading to a situation whereby, the detrimental effect of the situation is underestimated. The ill-defined situation in the growing online space impedes progress towards developing mechanisms and policies to mitigate the harmful effects of hate on social media, ultimately reducing the effectiveness of the platforms as effective communication tools. This proposal suggests Naïve Bayes classifier as a novel approach for detecting and classifying hateful social media comments to bridge this gap. Data set was taken from set provided by Kaggle and consisted of 30,000 Tweets. From the results of the use of this method, it was calculated that Bayes method is 62.75% accurate, which is not satisfactory. However, to bridge accuracy gap, nural algorithm was used which gain an improved accuracy of 87%

Cyber Security

This open access book constitutes the refereed proceedings of the 16th International Annual Conference on Cyber Security, CNCERT 2020, held in Beijing, China, in August 2020. The 17 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized according to the following topical sections: access control; cryptography; denial-of-service attacks; hardware security implementation; intrusion/anomaly detection and malware mitigation; social network security and privacy; systems security

Emotional Tendency Analysis of Twitter Data Streams

The web now seems to be an alive and dynamic arena in which billions of people across the globe connect, share, publish, and engage in a broad range of everyday activities. Using social media, individuals may connect and communicate with each other at any time and from any location. More than 500 million individuals across the globe post their thoughts and opinions on the internet every day. There is a huge amount of information created from a variety of social media platforms in a variety of formats and languages throughout the globe. Individuals define emotions as powerful feelings directed toward something or someone as a result of internal or external events that have a personal meaning. Emotional recognition in text has several applications in human-computer interface and natural language processing (NLP). Emotion classification has previously been studied using bag-of words classifiers or deep learning methods on static Twitter data. For real-time textual emotion identification, the proposed model combines a mix of keyword-based and learning-based models, as well as a real-time Emotional Tendency Analysi

A Trust Management Framework for Decision Support Systems

In the era of information explosion, it is critical to develop a framework which can extract useful information and help people to make “educated” decisions. In our lives, whether we are aware of it, trust has turned out to be very helpful for us to make decisions. At the same time, cognitive trust, especially in large systems, such as Facebook, Twitter, and so on, needs support from computer systems. Therefore, we need a framework that can effectively, but also intuitively, let people express their trust, and enable the system to automatically and securely summarize the massive amounts of trust information, so that a user of the system can make “educated” decisions, or at least not blind decisions. Inspired by the similarities between human trust and physical measurements, this dissertation proposes a measurement theory based trust management framework. It consists of three phases: trust modeling, trust inference, and decision making. Instead of proposing specific trust inference formulas, this dissertation proposes a fundamental framework which is flexible and can be adapted by many different inference formulas. Validation experiments are done on two data sets: the Epinions.com data set and the Twitter data set. This dissertation also adapts the measurement theory based trust management framework for two decision support applications. In the first application, the real stock market data is used as ground truth for the measurement theory based trust management framework. Basically, the correlation between the sentiment expressed on Twitter and stock market data is measured. Compared with existing works which do not differentiate tweets’ authors, this dissertation analyzes trust among stock investors on Twitter and uses the trust network to differentiate tweets’ authors. The results show that by using the measurement theory based trust framework, Twitter sentiment valence is able to reflect abnormal stock returns better than treating all the authors as equally important or weighting them by their number of followers. In the second application, the measurement theory based trust management framework is used to help to detect and prevent from being attacked in cloud computing scenarios. In this application, each single flow is treated as a measurement. The simulation results show that the measurement theory based trust management framework is able to provide guidance for cloud administrators and customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resources according to trust information, and managing the trade-off between the degree of redundancy and the cost of resources