MARINE: Man-in-the-middle attack resistant trust model IN connEcted vehicles

Vehicular Ad-hoc NETwork (VANET), a novel technology holds a paramount importance within the transportation domain due to its abilities to increase traffic efficiency and safety. Connected vehicles propagate sensitive information which must be shared with the neighbors in a secure environment. However, VANET may also include dishonest nodes such as Man-in-the-Middle (MiTM) attackers aiming to distribute and share malicious content with the vehicles, thus polluting the network with compromised information. In this regard, establishing trust among connected vehicles can increase security as every participating vehicle will generate and propagate authentic, accurate and trusted content within the network. In this paper, we propose a novel trust model, namely, Man-in-the-middle Attack Resistance trust model IN connEcted vehicles (MARINE), which identifies dishonest nodes performing MiTM attacks in an efficient way as well as revokes their credentials. Every node running MARINE system first establishes trust for the sender by performing multi-dimensional plausibility checks. Once the receiver verifies the trustworthiness of the sender, the received data is then evaluated both directly and indirectly. Extensive simulations are carried out to evaluate the performance and accuracy of MARINE rigorously across three MiTM attacker models and the bench-marked trust model. Simulation results show that for a network containing 35% MiTM attackers, MARINE outperforms the state of the art trust model by 15%, 18%, and 17% improvements in precision, recall and F-score, respectively.N/A

A new method of teaching drama: The pragmatic approach

AbstractPragmatics is defined in Jack Richards, et al., Longman Dictionary of Applied Linguistics (London, Longman, 1985) as follows: The study of the use of language in communication, particularly the relationship between sentences and the contexts and situations in which they are used. Pragmatics includes the study of:(a)how the interpretation and use of utterances depends on knowledge of the real world(b)how speakers use and understand speech acts(c)how the structure of sentences is influenced by the relationship between the speaker and the hearer.Accordingly, this paper aims at applying a pragmatic analysis to dramatic texts to find out whether it is possible for teachers to teach, and students to learn, dramatic texts more effectively by using pragmatic approaches. The pragmatic model used in this study is Leech's Cooperative Principle (CP), together with its maxims and submaxims (Principles of Pragmatics, 1983). This model is applied to Oscar Wilde's play Lady Windermere's Fan (1980) to understand how language is used and understood in communication. The model proves to be a satisfactory mechanism for a pragmatic analysis of dramatic texts. It proves that there is a relation between the linguistic structures of utterances and their intended meanings. It is also responsible for indirectness of utterances

Teeka Tehreek: Increasing EPI Vaccinations in Khyber Pakhtunkhwa, Pakistan

Abstract Globally, Pakistan ranks third among countries with the most unvaccinated and under-vaccinated children. Within the country, there are huge regional disparities, and the Khyber Pakhtunkhwa region (KPK FATA) has one of the lowest vaccination rates in the country. Low vaccination rates contribute to the high burden of childhood infectious diseases in Pakistan that cause 70% of childhood deaths. Addressing the issue requires an understanding of the barriers towards vaccinations; 30% of refusals are due to religious reasons and other misconceptions that are compounded by low literacy rates. Since religion has a critical authority in shaping the population’s beliefs, attitudes, and perceptions towards vaccination, mosques will be used as one of the central components for our program. We will partner with the Council of Islamic Ideology, a religious and constitutional body in Pakistan, who have already released a religious ruling to declare vaccines a religious responsibility. Hence, partnering with them will allow us to integrate local mosques into our program for education and as a venue for vaccine camps to enhance access. On the other hand, we will collaborate with the local hospital to facilitate vaccines and to implement a client recall system to increase vaccine demand. We will train our staff and monitor fidelity through pretest posttest surveys, random observations, and monthly meetings. Formative evaluation measures will include staff recruitment and their attitudes towards vaccines so inform program feasibility. Process evaluation will measure program success such as participant characteristics, vaccines administered, and successful recalls; feedback from staff and participants will also be considered. Outcome evaluations will be conducted using an interrupted time series to measure changes in vaccine acceptability in parents and to see positive changes in attitudes, beliefs, and perceptions towards vaccines. This will provide an understanding for changing social norms towards vaccination and program sustainability. The short-term outcome for our program is to see an increase in vaccine uptake and a decrease in vaccine hesitancy, while the long-term outcome is to see a decrease in childhood infectious diseases

Enterprise API Security and GDPR Compliance:Design and Implementation Perspective

With the advancements in the enterprise-level business development, the demand for new applications and services is overwhelming. For the development and delivery of such applications and services, enterprise businesses rely on Application Programming Interfaces (APIs). In essence, API is a double-edged sword. On one hand, API provides ease of expanding the business through sharing value and utility, but on another hand it raises security and privacy issues. Since the applications usually use APIs to retrieve important data, therefore it is extremely important to make sure that an effective access control and security mechanism are in place , and the data does not fall into wrong hands. In this article, we discuss the current state of the enterprise API security and the role of Machine Learning (ML) in API security. We also discuss the General Data Protection Regulation (GDPR) compliance and its effect on the API security.Comment: 7 page

Machine Learning in IoT Security:Current Solutions and Future Challenges

The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML- and DL-based IoT security

A Crime Data Analysis of Prediction Based on Classification Approaches

تعتبر الجرائم نشاطا غير مشروع بجميع أنواعه يعاقب عليه القانون ويؤثر على نوعية حياة المجتمع وتطوره الاقتصادي. مع الارتفاع الكبير في معدلات الجريمة على مستوى العالم، هناك ضرورة لتحليل بيانات الجريمة لخفض معدل الجريمة. وهذا يشجع الشرطة والأفراد على اتخاذ الإجراءات المطلوبة والحد بشكل أكثر فعالية من الجرائم. الغرض من هذا البحث هو تطوير نماذج تنبؤية يمكن أن تساعد في تحليل أنماط الجريمة وبالتالي دعم جهود منع الجريمة في قسم بوسطن. تم اعتماد عامل الموقع الجغرافي في نموذجنا ، ويرجع ذلك إلى كونه عاملاً مؤثرًا في عدة مواقف ، سواء كان السفر إلى منطقة معينة أو العيش فيها لمساعدة الناس في التعرف بين بيئة آمنة وغير آمنة. يمكن أن يكون الموقع الجغرافي، جنبًا إلى جنب مع الأساليب والتقنيات الجديدة، مفيدًا للغاية في التحقيق في الجرائم. يتركز الهدف على الدراسة المقارنة بين ثلاث خوارزميات تعلم تحت الإشراف. حيث يستخدم التعلم مجموعات البيانات للتدريب، واختبارها للحصول على النتائج المرجوة عليها. تم استخدام خوارزميات التعلم الآلي المختلفة في مجموعة البيانات الخاصة بجرائم مدينة بوسطن، وهي شجرة القرار ونايف بايز والانحدار اللوجستي المصنفات هنا للتنبؤ بنوع الجريمة التي تحدث في المنطقة. تتم مقارنة مخرجات هذه الطرق مع بعضها البعض للعثور على نموذج واحد يناسب هذا النوع من البيانات بأفضل أداء. من النتائج التي تم الحصول عليها، أظهرت شجرة القرار أعلى نتيجة مقارنة بـ نايف بايز والانحدار اللوجستي.Crime is considered as an unlawful activity of all kinds and it is punished by law. Crimes have an impact on a society's quality of life and economic development. With a large rise in crime globally, there is a necessity to analyze crime data to bring down the rate of crime. This encourages the police and people to occupy the required measures and more effectively restricting the crimes. The purpose of this research is to develop predictive models that can aid in crime pattern analysis and thus support the Boston department's crime prevention efforts. The geographical location factor has been adopted in our model, and this is due to its being an influential factor in several situations, whether it is traveling to a specific area or living in it to assist people in recognizing between a secured and an unsecured environment.  Geo-location, combined with new approaches and techniques, can be extremely useful in crime investigation. The aim is focused on comparative study between three supervised learning algorithms. Where learning used data sets to train and test it to get desired results on them. Various machine learning algorithms on the dataset of Boston city crime are Decision Tree, Naïve Bayes and Logistic Regression classifiers have been used here to predict the type of crime that happens in the area. The outputs of these methods are compared to each other to find the one model best fits this type of data with the best performance. From the results obtained, the Decision Tree demonstrated the highest result compared to Naïve Bayes and Logistic Regression