A survey of security and privacy issues in the Internet of Things from the layered context <s> INTRODUCTION <s> The Internet is continuously changing and evolving. The main communication form of present Internet is human-human. The Internet of Things (IoT) can be considered as the future evaluation of the Internet that realizes machine-to-machine (M2M) learning. Thus, IoT provides connectivity for everyone and everything. The IoT embeds some intelligence in Internet-connected objects to communicate, exchange information, take decisions, invoke actions and provide amazing services. This paper addresses the existing development trends, the generic architecture of IoT, its distinguishing features and possible future applications. This paper also forecast the key challenges associated with the development of IoT. The IoT is getting increasing popularity for academia, industry as well as government that has the potential to bring significant personal, professional and economic benefits. <s> BIB001 </s> A survey of security and privacy issues in the Internet of Things from the layered context <s> INTRODUCTION <s> Internet of Things (IoT) is the interconnection of a large number of resource-constrained devices such as sensors, actuators, and nodes that generate large volumes of data which are then processed into useful actions in areas such as home and building automation, intelligent transportation and connected vehicles, industrial automation, smart healthcare, smart cities, and others. Important challenges remain to fulfill the IoT vision including data provenance and integrity, trust management, identity management, and privacy. We describe how embedded and hardware security approaches can be the basis to address these security challenges. <s> BIB002 </s> A survey of security and privacy issues in the Internet of Things from the layered context <s> INTRODUCTION <s> Modern vehicle systems have evolved from an isolated control system into an interconnected architecture combining software, hardware, and data. Such architecture is specialized into vehicle infotainment system (e.g., SYNC of Ford, iDrive of BMW and MMI of Audi), Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), and vehicle social system which connects to social media networks. These systems hold private and sensitive information such as travel plans, social network messages, login credentials to bank accounts, and so on, which is a lucrative target for malicious attackers. Unfortunately, existing research overlooks the security issues with respect to this highly integrated system. This paper presents security issues across various systems related to modern vehicles through a a holistic and systematic view. We analyze each system components with respect to published attacks in details and present a synthesized body of knowledge. We identify the growing trend where security attacks are launched from the cyber space to vehicle control system via smartphones and vehicle networks. In the foreseeable future, we expect more security attacks both in numbers and in complexity. Knowing this will arise the awareness of vehicle system security and help engineers to build security solutions. <s> BIB003 </s> A survey of security and privacy issues in the Internet of Things from the layered context <s> INTRODUCTION <s> Although smoking prevalence is declining in many countries, smoking related health problems still leads the preventable causes of death in the world. Several smoking intervention mechanisms have been introduced to help smoking cessation. However, these methods are inefficient since they lack in providing real time personalized intervention messages to the smoking addicted users. To address this challenge, the first step is to build an automated smoking behavior detection system. In this study, we propose an accelerometer sensor based non-invasive and automated framework for smoking behavior detection. We built a prototype device to collect data from several participants performing smoking and other five confounding activities. We used three different classifiers to compare activity detection performance using the extracted features from accelerometer data. Our evaluation demonstrates that the proposed approach is able to classify smoking activity among the confounding activities with high accuracy. The proposed system shows the potential for developing a real time automated smoking activity detection and intervention framework. <s> BIB004 </s> A survey of security and privacy issues in the Internet of Things from the layered context <s> INTRODUCTION <s> Abstract Security is a fundamental concern in modern vehicular systems. The advancement in modern vehicular systems provides many benefits such as reducing traffic congestion and improving safety and fuel economy via vehicular networks. However, many vehicular experts in industry take it for granted that Controller Area Network (CAN) bus being the most important part of a vehicle is secured and cannot be easily hacked; furthermore, cyber security experts have limited exposure to CAN bus. This paper provides in-depth explanation of CAN bus and feasible scenarios where a vehicle is no longer safe after its CAN bus is compromised. Furthermore, the inclusion of CAN bus attacking codes in a mobile malware is cost-effective for the malicious attackers but very challenging for vehicle engineers to mitigate the security risks. Based on a variety of security attacks, we analyze potential attacks and their impact on the safety of the vehicle users. A number of vulnerabilities and attack scenarios are exposed which allows malicious attackers to hamper the vehicular control systems and cause harm to the vehicle even the passengers. In this paper, we do not report on our own implementation of attacks on real vehicles; our aim is to motivate further research to improve the security of the modern vehicular systems. <s> BIB005 </s> A survey of security and privacy issues in the Internet of Things from the layered context <s> INTRODUCTION <s> Java code is easy to be decompiled, and third-party SO files are used frequently by developers to improve development efficiency. Therefore, more and more core functions of Android applications are implemented in the native layer. However, there is neither comprehensive security research work nor automated security analysis tools on Android native layer, especially for third-party SO files that are dynamically loaded within the applications. To solve this problem, SoProtector, a novel and effective system is proposed to defend against the privacy leaks, which mainly analyzes the data stream between two levels: application and Native layers. In addition, SoProtector includes a real-time monitor to detect malicious functions in binary code. Our evaluation using 3400 applications has demonstrated that SoProtector can detect more sources, sinks and smudges than most static analysis tools; And it detects and effectively blocks more than 82% of applications that dynamically load malicious third-party SO files with low performance overhead. <s> BIB006 </s> A survey of security and privacy issues in the Internet of Things from the layered context <s> INTRODUCTION <s> It is well known that blink, yawn, and heart rate changes give clue about a human’s mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject’s facial region are analyzed to identify the physiological sources that are mixed in each image. We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations. An overall decision is made by analyzing the separated source signals in parallel to determine the driver’s driving state. The robustness of the proposed method is tested under various illumination contexts and a variety of head motion modes. Experiments on 15 subjects show that the multi-channel SOBI presents a promising framework to accurately detect drowsiness by merging multi-physiological information in a less complex way. <s> BIB007 </s> A survey of security and privacy issues in the Internet of Things from the layered context <s> INTRODUCTION <s> Although smoking prevalence is declining in many countries, smoking related health problems still leads the preventable causes of death in the world. Several smoking intervention mechanisms have been introduced to help smoking cessation such as counselling program, motivational interview and pharmacotherapy. However, these methods lack providing real time personalized intervention messages to the smoking addicted users. The challenge is to develop an automated smoking behavior detection. We address this challenge by proposing a non-invasive sensor based automated framework for smoking behavior detection. We used a wristband based accelerometer and gyroscope sensors to detect smoking activities, differentiating with the closely confounding activities. We extract several features using learning algorithms and the empirical results with our participants show good accuracy in detecting the smoking activity in terms of precision, recall, and Flscore. <s> BIB008
The Internet of Things (IoT) has been a noteworthy research topic for more than a decade and was first mentioned by Kevin Ashton in 1998 in a presentation where he used the term to link the idea of RFID to the Internet  . The general definition for the IoT includes heterogeneous devices and the interconnection of these uniquely identifiable objects. The IoT can be defined as a network of internet-connected physical devices that gather data using embedded sensors and are able to communicate with each other. This would enable any device to communicate with any other device thereby creating a smart ecosystem. It is a noteworthy technological revolution that has refreshed the present Internet foundation to an idea of substantially more propelled computing system in which the physical objects will be pervasively connected and could be addressed uniquely 2 . IoT is a collection of heterogeneous technologies that function together. IoT devices are equipped with various components such as actuators, embedded sensors, processors, RFID and transceivers for acuity, concurrence and connection. The core purpose of the IoT is to enable heterogeneous devices connect to the Internet and exchange information in a reliable manner BIB001 . The security of network and protection of data should meet essential standards and basic principles of integrity, authentication, availability, authorization and confidentiality of user's information  . Human life has been enriched with the availability of smart and intelligent IoT devices in hospitals, homes, transportation systems and aged care facilities, etc.  BIB007 BIB004 8, BIB008 . IoT unquestionably has a huge potential for adaptability and guarantees an extraordinary future. However, it is inherently prone to threats that can compromise the security and privacy of the users, which could in turn influence its practical development  BIB005    BIB006 BIB003 . Eventually confidentiality, integrity and availability of the data will be diminished and as a result clients will be hesitant to accept this technology BIB002 . The large number of devices which are being connected to the Internet is rapidly proliferating and this may eventually lead to an all pervasive IoT enabled global Internet architecture. A study by Gartner estimates around 25 billion uniquely addressable objects are likely to be connected to this 19 with a decent number of these being appliances, therefore there will be an expansive open door for hackers to utilize these gadgets to their own advantage through malicious emails, "denial of service" attacks and by other malicious means such as unsafe worms or Trojans. Therefore, security concerns are noteworthy part that has to be well studied before developing more advanced Internet of Things (IoT) systems. Our survey paper reviews and analyses the security and privacy issues in IoT. The rest of the paper is as follows: Section 2 contains the background and related work in IoT. In section 3, we describe the general protocol architecture of the IoT. In section 4, we examine the security issues at each layer of the IoT protocol stack and identify corresponding threats and attacks that can manifest at each layer. In section 5, we address the major challenges in securing IoT and discuss the security services requirements in IoT. In section 6, we provide an overview of existing approaches to securing the IoT. We discuss some of the research directions in section 7. Finally, in section 8 we provide some concluding remarks.