Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data

Android malware detection through generative adversarial networks

© 2019 John Wiley & Sons, Ltd. Mobile and cell devices have empowered end users to tweak their cell phones more than ever and introduce applications just as we used to with personal computers. Android likewise portrays an uprise in mobile devices and personal digital assistants. It is an open-source versatile platform fueling incalculable hardware units, tablets, televisions, auto amusement frameworks, digital boxes, and so forth. In a generally shorter life cycle, Android also has additionally experienced a mammoth development in application malware. In this context, a toweringly large measure of strategies has been proposed in theory for the examination and detection of these harmful applications for the Android platform. These strategies attempt to both statically reverse engineer the application and elicit meaningful information as features manually or dynamically endeavor to quantify the runtime behavior of the application to identify malevolence. The overgrowing nature of Android malware has enormously debilitated the support of protective measures, which leaves the platforms such as Android feeble for novel and mysterious malware. Machine learning is being utilized for malware diagnosis in mobile phones as a common practice and in Android distinctively. It is important to specify here that these systems, however, utilize and adapt the learning-based techniques, yet the overhead of hand-created features limits ease of use of such methods in reality by an end user. As a solution to this issue, we mean to make utilization of deep learning–based algorithms as the fundamental arrangement for malware examination on Android. Deep learning turns up as another way of research that has bid the scientific community in the fields of vision, speech, and natural language processing. Of late, models set up on deep convolution networks outmatched techniques utilizing handmade descriptive features at various undertakings. Likewise, our proposed technique to cater malware detection is by design a deep learning model making use of generative adversarial networks, which is responsible to detect the Android malware via famous two-player game theory for a rock-paper-scissor problem. We have used three state-of-the-art datasets and augmented a large-scale dataset of opcodes extracted from the Android Package Kit bytecode and used in our experiments. Our technique achieves F1 score of 99% with a receiver operating characteristic of 99% on the bytecode dataset. This proves the usefulness of our technique and that it can generally be adopted in real life

Life review in advanced age:qualitative research on the 'start in life' of 90-year-olds in the Lothian Birth Cohort 1921

BACKGROUND: This research report presents findings on ‘start in life’ from a qualitative study of 90-year-olds from the Lothian Birth Cohort 1921. The study aimed to contextualise the LBC1921 cohort in time and place, describe cohort members’ experiences of family and schooling and stimulate further inquiry into the relationships between ‘start in life’ and risk and resilience factors relating to longevity and healthy ageing. Scottish education and family life in the early 1930s are briefly described. METHODS: Life review questionnaire: A qualitative Life Review Questionnaire was developed, requiring free-text handwritten responses. Its ‘Start in Life’ section focused on schooling and family support. Sample: Wave 4 of the Lothian Birth Cohort 1921 involved testing 129 members near to their 90(th) birthday. They reside largely in Edinburgh and its environs. The Life Review Questionnaire was administered to 126 participants, 54 % women. Qualitative analysis: Thematic analysis was the qualitative technique used to categorise, code and extract meaning from questionnaire text. Narratives were extracted from the data to present illustrative stories. RESULTS: Narratives of start in life gave contextual description. Thematic analysis showed LBC1921 members enjoying their schooling, highlighting teachers, academic achievement, school activities and school friendships. Personal qualities, family circumstances and aspects of schooling sometimes hindered educational performance. Family life was recalled mostly with warmth and parents were often portrayed as valuing education and supporting learning and development. Family adversity from poverty, parental illness and parental death was often mitigated by support from parents (or the remaining parent). Overall, most cohort members believed that they had got off to a good ‘start in life’. CONCLUSIONS: This qualitative investigation of ‘start in life’ adds context and richness to quantitative investigations of the sizeable LBC1921 cohort, stimulating fresh insights and hypotheses into the relationship between child risk and resilience factors that may influence ageing. It demonstrates the utility and wider application of the Life Review Questionnaire. Although the surviving cohort is not representative of their childhood peers, their words provide insight into the processes of weaving experience and memory into a rich texture of meanings that may help create wellbeing across a lifetime

Motives behind ddos attacks

Behind everything we do in our daily lives there is a reason for doing it. This paper looks into the motives behind DDoS attacks as a form of cyberattack along with attacker personas. This paper also investigates DDoS attacks technically and it is suggested that there is a need for a socio-technical approach to these attacks to investigate why they occur and the reasoning the attacker(s) could have for launching these attacks. This paper finds several motives behind DDoS attacks and discusses the profiles that attackers can be sorted into. Also discussed are the motives that attacker profiles can have for launching DDoS attacks. Although mitigation techniques are in place to control the damage a DDoS attack can cause to a company, if the motives can be addressed first, these attacks could be prevented. With the use of case studies, visualisations and tables, the motives behind DDoS attacks and attacker personas are presented