Munam Ali Shah

Smart Cities: A Survey on Security Concerns

A smart city is developed, deployed and maintained with the help of Internet of Things (IoT). The smart cities have become an emerging phenomena with rapid urban growth and boost in the field of information technology. However, the function and operation of a smart city is subject to the pivotal development of security architectures. The contribution made in this paper is twofold. Firstly, it aims to provide a detailed, categorized and comprehensive overview of the research on security problems and their existing solutions for smart cities. The categorization is based on several factors such as governance, socioeconomic and technological factors. This classification provides an easy and concise view of the security threats, vulnerabilities and available solutions for the respective technologies areas that are proposed over the period 2010-2015. Secondly, an IoT testbed for smart cities architecture, i.e., SmartSantander is also analyzed with respect to security threats and vulnerabilities to smart cities. The existing best practices regarding smart city security are discussed and analyzed with respect to their performance, which could be used by different stakeholders of the smart cities.

Data mining techniques and applications — A decade review

Data mining is also known as Knowledge Discovery in Database (KDD). It is also defined as the process which includes extracting the interesting, interpretable and useful information from the raw data. There are different sources that generate raw data in very large amount. This is the main reason the applications of data mining are increasing rapidly. This paper reviews data mining techniques and its applications such as educational data mining (EDM), finance, commerce, life sciences and medical etc. We group existing approaches to determine how the data mining can be used in different fields. Our categorization specifically focuses on the research that has been published over the period 2007–2017. With this categorization, we present an easy and concise view of different models adapted in the data mining.

Android Malware Detection & Protection: A Survey

Android has become the most popular smartphone operating system. This rapidly increasing adoption of Android has resulted in significant increase in the number of malwares when compared with previous years. There exist lots of antimalware programs which are designed to effectively protect the users’ sensitive data in mobile systems from such attacks. In this paper, our contribution is twofold. Firstly, we have analyzed the Android malwares and their penetration techniques used for attacking the systems and antivirus programs that act against malwares to protect Android systems. We categorize many of the most recent antimalware techniques on the basis of their detection methods. We aim to provide an easy and concise view of the malware detection and protection mechanisms and deduce their benefits and limitations. Secondly, we have forecast Android market trends for the year up to 2018 and provide a unique hybrid security solution and take into account both the static and dynamic analysis an android application.

Load balancing algorithms in cloud computing: A survey of modern techniques

Cloud computing has become popular due to its attractive features. The load on the cloud is increasing tremendously with the development of new applications. Load balancing is an important part of cloud computing environment which ensures that all devices or processors perform same amount of work in equal amount of time. Different models and algorithms for load balancing in cloud computing has been developed with the aim to make cloud resources accessible to the end users with ease and convenience. In this paper, we aim to provide a structured and comprehensive overview of the research on load balancing algorithms in cloud computing. This paper surveys the state of the art load balancing tools and techniques over the period of 2004-2015. We group existing approaches aimed at providing load balancing in a fair manner. With this categorization we provide an easy and concise view of the underlying model adopted by each approach.

Green IoT: An Investigation on Energy Saving Practices for 2020 and Beyond

Internet of Things (IoT) is an emerging concept, which aims to connect billions of devices with each other. The IoT devices sense, collect, and transmit important information from their surroundings. This exchange of very large amount of information amongst billions of devices creates a massive energy need. Green IoT envisions the concept of reducing the energy consumption of IoT devices and making the environment safe. Inspired by achieving a sustainable environment for IoT, we first give the overview of green IoT and the challenges that are faced due to excessive usage of energy hungry IoT devices. We then discuss and evaluate the strategies that can be used to minimize the energy consumption in IoT, such as designing energy efficient datacenters, energy efficient transmission of data from sensors, and design of energy efficient policies. Moreover, we critically analyze the green IoT strategies and propose five principles that can be adopted to achieve green IoT. Finally, we consider a case study of very important aspect of IoT, i.e., smart phones and we provide an easy and concise view for improving the current practices to make the IoT greener for the world in 2020 and beyond.

Survey on security threats of smartphones in Internet of Things

Internet of Things (IoT) is an emerging concept which will interconnect billions of devices (such as smartphones, sensors and other networking devices), to communicate with each other. This interconnection of devices can be beneficial in many ways such as improving location based services and timely coordination. However, this also impose severe security concerns specially on smartphones which contains sensitive data. In this paper, we provide a comprehensive analysis of different security issues in smartphones which are integral part of IoT. We provide an easy and concise overview of different security threats. Our categorization helps in identifying specific security breach and applying relevant countermeasures.

Big data analytics for mitigating broadcast storm in Vehicular Content Centric networks

The focus of the research for big data analytics for sustainability (BDAS) is Vehicular Ad-hoc Networks (VANETs), since it has become the most popular technology for the researchers, academia and the organizations. However, in VANETs most part of the time a well-defined path from a consumer to the provider doesn’t exists due to the intermittent connectivity and mobility. Hence, Vehicular Content Centric Networks (VCCN) has been proposed and has emerged as a future Internet technology. The focus of the VCCN is to retrieve and distribute the content faster than the existing Internet architecture in vehicular environment. In VCCN, consumer broadcasts the interest packet to the provider including the name of the required content. If the provider is not available at one hop neighbor, then the interest packet is sent to the forwarder vehicle, otherwise, provider generates the data packet and sends back to the consumer. Forwarder vehicle receives this interest packet from the consumer and searches in its cache called Content Storage (CS). If the content is not available in its storage the forwarder vehicle rebroadcasts the interest packet within its vicinity and forward the interest to the provider. The provider generates the data packet including the content and sends back to the consumer through selected forwarder vehicle. The VCCN communication paradigm faces several challenges i.e., mobility, interest broadcasting storm, routing, interest/data packet forwarding and forwarder selection. Various routing protocols have already been proposed and implemented to resolve existing problems. However, in our research due to the high mobility and the dynamic network topology in vehicular environment, there is no assurance that the back path is still available followed by the interest message which causes disconnected link problem. Hence, the retrieval of the required data might be effected. Thus, the aim of this paper is to propose a forwarding protocol to mitigate the interest broadcasting storm and the disconnected link problems, keeping in the view of intermittent connectivity and high mobility that will improve the data delivery probability, packet loss ratio, Interest Satisfaction Ratio (ISR) and average End-to-End delay in a highway and an urban scenario of a vehicular environment. Results and demonstrations show that the proposed protocol can meet requirements for BDAS.Abstract The focus of the research for big data analytics for sustainability (BDAS) is Vehicular Adhoc Networks (VANETs), since it has become the most popular technology for the researchers, academia and the organizations. However, in VANETs most part of the time a well-defined path from a consumer to the provider does not exist due to the intermittent connectivity and mobility. Hence, Vehicular Content Centric Networks (VCCNs) has been proposed and has emerged as a future Internet technology. The focus of the VCCNs is to retrieve and distribute the content faster than the existing Internet architecture in vehicular environment. In VCCNs, the consumer broadcasts the interest packet to the provider, including the name of the required content. If the provider is available at one hop neighbour, the interest packet is sent to the provider. The provider generates the data packet and sends back to the consumer. Otherwise, the neighbour vehicle becomes the forwarder vehicle who receives the interest packet from the consumer and searches in its cache called Content Storage (CS). If the content is not available in its storage, the forwarder vehicle rebroadcasts the interest packet within its vicinity forwards the interest packet to the provider. The provider generates the data packet including the content and sends back to the consumer through the selected forwarder vehicle. The VCCNs communication paradigm faces several challenges i.e., mobility, interest broadcast storm, routing, interest or data packet forwarding and forwarder selection. Various routing protocols have already been proposed and implemented in order to resolve the existing problems. However, due to the high mobility and the dynamic network topology in vehicular environment, there is no assurance that the back path is still available followed by the interest message which causes disconnected link problem. Hence, the retrieval of the required data might be effected. Thus, the aim of this paper is to propose a forwarding protocol to mitigating the interest broadcast storm and the disconnected link problems, keeping in the view of intermittent connectivity and high mobility that will improve the data delivery probability, packet loss ratio, Interest Satisfaction Ratio (ISR) and average End-to-End delay in a highway and an urban scenario of a vehicular environment. Results and demonstrations show that the proposed protocol meets all the requirements for BDAS.

A novel model for preserving Location Privacy in Internet of Things

The Internet of Things (IoT) is an Internet-based architecture for the exchange of information and services amongst millions of devices. Due to massive use of IoT for various benefits, there are high security and privacy related issues which need to be addressed. Many techniques have been proposed for preserving location privacy in the context of IoT. Some of these techniques are based on anonymization and others on obfuscation. However, the existing research clearly lags in preserving location privacy in IoT and has common issues such as being specific or restricted to certain location. This research focuses on preserving location privacy in the context of IoT. In this paper, we propose an Enhanced Semantic Obfuscation Technique (ESOT) for preserving location privacy in the IoT. We compare the performance of ESOT with previously developed Semantic Obfuscation Technique (SOT). The results show that ESOT achieves better location privacy and provides a balance between privacy and utility of location services.

SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System

For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the malware developer’s attention. Android allows downloading and installation of apps from other unofficial market places. This gives malware developers an opportunity to put repackaged malicious applications in third-party app-stores and attack the Android devices. A large number of malware analysis and detection systems have been developed which uses static analysis, dynamic analysis, or hybrid analysis to keep Android devices secure from malware. However, the existing research clearly lags in detecting malware efficiently and accurately. For accurate malware detection, multilayer analysis is required which consumes large amount of hardware resources of resource constrained mobile devices. This research proposes an efficient and accurate solution to this problem, named SAMADroid, which is a novel 3-level hybrid malware detection model for Android operating systems. The research contribution includes multiple folds. First, many of the existing Android malware detection techniques are thoroughly investigated and categorized on the basis of their detection methods. Also, their benefits along with limitations are deduced. A novel 3-level hybrid malware detection model for Android operating systems is developed, that can provide high detection accuracy by combining the benefits of the three different levels: 1) Static and Dynamic Analysis; 2) Local and Remote Host; and 3) Machine Learning Intelligence. Experimental results show that SAMADroid achieves high malware detection accuracy by ensuring the efficiency in terms of power and storage consumption.

A Survey on Smartphones Systems for Emergency Management (SPSEM)

Emergency never runs with earlier intimations and indications. In the real world and practical life, detecting and perceiving such emergencies and reporting them are a genuine test and tough challenge. Smartphones Systems for Emergency Management (SPSEM) provide details of existing emergency applications and provide a new direction to overcome the traditional problem having manual intercession and reporting emergencies. In this paper, we provide a comprehensive overview of SPSEM. We elaborate how embedded sensors automate the procedure of emergency crisis detection and responding. Furthermore, we critically evaluate the operations, benefits, limitations, emergency applications and responsiveness in any emergency crisis of different approaches. We provide an easy and concise view of the underlying model adapted by each SPSEM approach. In last, we estimate the future utility and provide an insight of upcoming trends in SPSEM