Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities

Edge computing is a promising paradigm that enhances the capabilities of cloud computing. In order to continue patronizing the computing services, it is essential to conserve a good atmosphere free from all kinds of security and privacy breaches. The security and privacy issues associated with the edge computing environment have narrowed the overall acceptance of the technology as a reliable paradigm. Many researchers have reviewed security and privacy issues in edge computing, but not all have fully investigated the security and privacy requirements. Security and privacy requirements are the objectives that indicate the capabilities as well as functions a system performs in eliminating certain security and privacy vulnerabilities. The paper aims to substantially review the security and privacy requirements of the edge computing and the various technological methods employed by the techniques used in curbing the threats, with the aim of helping future researchers in identifying research opportunities. This paper investigate the current studies and highlights the following: (1) the classification of security and privacy requirements in edge computing, (2) the state of the art techniques deployed in curbing the security and privacy threats, (3) the trends of technological methods employed by the techniques, (4) the metrics used for evaluating the performance of the techniques, (5) the taxonomy of attacks affecting the edge network, and the corresponding technological trend employed in mitigating the attacks, and, (6) research opportunities for future researchers in the area of edge computing security and privacy

eBiometrics: an enhanced multi-biometrics authentication technique for real-time remote applications on mobile devices

The use of mobile communication devices with advance sensors is growing rapidly. These sensors are enabling functions such as Image capture, Location applications, and Biometric authentication such as Fingerprint verification and Face & Handwritten signature recognition. Such ubiquitous devices are essential tools in today's global economic activities enabling anywhere-anytime financial and business transactions. Cryptographic functions and biometric-based authentication can enhance the security and confidentiality of mobile transactions. Using Biometric template security techniques in real-time biometric-based authentication are key factors for successful identity verification solutions, but are venerable to determined attacks by both fraudulent software and hardware. The EU-funded SecurePhone project has designed and implemented a multimodal biometric user authentication system on a prototype mobile communication device. However, various implementations of this project have resulted in long verification times or reduced accuracy and/or security. This paper proposes to use built-in-self-test techniques to ensure no tampering has taken place on the verification process prior to performing the actual biometric authentication. These techniques utilises the user personal identification number as a seed to generate a unique signature. This signature is then used to test the integrity of the verification process. Also, this study proposes the use of a combination of biometric modalities to provide application specific authentication in a secure environment, thus achieving optimum security level with effective processing time. I.e. to ensure that the necessary authentication steps and algorithms running on the mobile device application processor can not be undermined or modified by an imposter to get unauthorized access to the secure system

Performance Evaluation of Mobile Sensor for Context Awareness User Authentication

With the increase of smart devices and their capacities, their use for different services have also increased. As much as this is an advantage, it has posed additional risks because of the confidential information stored on them. This has increased the need for additional security on these systems. Most of the methods used for user authentication pose certain drawbacks that are either easy to circumvent or cumbersome to use. As a result, multi-level means of authentication is needed to improve the security of mobile devices. Sensors are playing a vital role in the mobile ecosystem to enhance different services. These sensors can be leveraged upon as a solution for user authentication. This research analyzed and evaluated different mobile device sensors for continuous and transparent user authentication. The mobile data used includes gyroscope, accelerometer, linear accelerometer, proximity, gravity, and magnetometer sensors’ data. Using a Feedforward Neural network for data classification after extracting features from the different sensors available in the mobile device; the most effective was selected by evaluating performance of the different sensors. The best sensor, the accelerometer was further experimented on. The experiment showed that smartphone accelerometer sensor exhibits sufficient discriminability, stability, and reliability for active and continuous authentication, by achieving a performance of 6.55% for the best overall EER.With the increase of smart devices and their capacities, their use for different services have also increased. As much as this is an advantage, it has posed additional risks because of the confidential information stored on them. This has increased the need for additional security on these systems. Most of the methods used for user authentication pose certain drawbacks that are either easy to circumvent or cumbersome to use. As a result, multi-level means of authentication is needed to improve the security of mobile devices. Sensors are playing a vital role in the mobile ecosystem to enhance different services. These sensors can be leveraged upon as a solution for user authentication. This research analyzed and evaluated different mobile device sensors for continuous and transparent user authentication. The mobile data used includes gyroscope, accelerometer, linear accelerometer, proximity, gravity, and magnetometer sensors’ data. Using a Feedforward Neural network for data classification after extracting features from the different sensors available in the mobile device; the most effective was selected by evaluating performance of the different sensors. The best sensor, the accelerometer was further experimented on. The experiment showed that smartphone accelerometer sensor exhibits sufficient discriminability, stability, and reliability for active and continuous authentication, by achieving a performance of 6.55% for the best overall EER

Continuous and transparent multimodal authentication: reviewing the state of the art

Individuals, businesses and governments undertake an ever-growing range of activities online and via various Internet-enabled digital devices. Unfortunately, these activities, services, information and devices are the targets of cybercrimes. Verifying the user legitimacy to use/access a digital device or service has become of the utmost importance. Authentication is the frontline countermeasure of ensuring only the authorized user is granted access; however, it has historically suffered from a range of issues related to the security and usability of the approaches. They are also still mostly functioning at the point of entry and those performing sort of re-authentication executing it in an intrusive manner. Thus, it is apparent that a more innovative, convenient and secure user authentication solution is vital. This paper reviews the authentication methods along with the current use of authentication technologies, aiming at developing a current state-of-the-art and identifying the open problems to be tackled and available solutions to be adopted. It also investigates whether these authentication technologies have the capability to fill the gap between high security and user satisfaction. This is followed by a literature review of the existing research on continuous and transparent multimodal authentication. It concludes that providing users with adequate protection and convenience requires innovative robust authentication mechanisms to be utilized in a universal level. Ultimately, a potential federated biometric authentication solution is presented; however it needs to be developed and extensively evaluated, thus operating in a transparent, continuous and user-friendly manner

The Role of Eye Gaze in Security and Privacy Applications: Survey and Future HCI Research Directions

For the past 20 years, researchers have investigated the use of eye tracking in security applications. We present a holistic view on gaze-based security applications. In particular, we canvassed the literature and classify the utility of gaze in security applications into a) authentication, b) privacy protection, and c) gaze monitoring during security critical tasks. This allows us to chart several research directions, most importantly 1) conducting field studies of implicit and explicit gaze-based authentication due to recent advances in eye tracking, 2) research on gaze-based privacy protection and gaze monitoring in security critical tasks which are under-investigated yet very promising areas, and 3) understanding the privacy implications of pervasive eye tracking. We discuss the most promising opportunities and most pressing challenges of eye tracking for security that will shape research in gaze-based security applications for the next decade

Continuous User Authentication Using Multi-Modal Biometrics

It is commonly acknowledged that mobile devices now form an integral part of an individual’s everyday life. The modern mobile handheld devices are capable to provide a wide range of services and applications over multiple networks. With the increasing capability and accessibility, they introduce additional demands in term of security. This thesis explores the need for authentication on mobile devices and proposes a novel mechanism to improve the current techniques. The research begins with an intensive review of mobile technologies and the current security challenges that mobile devices experience to illustrate the imperative of authentication on mobile devices. The research then highlights the existing authentication mechanism and a wide range of weakness. To this end, biometric approaches are identified as an appropriate solution an opportunity for security to be maintained beyond point-of-entry. Indeed, by utilising behaviour biometric techniques, the authentication mechanism can be performed in a continuous and transparent fashion. This research investigated three behavioural biometric techniques based on SMS texting activities and messages, looking to apply these techniques as a multi-modal biometric authentication method for mobile devices. The results showed that linguistic profiling; keystroke dynamics and behaviour profiling can be used to discriminate users with overall Equal Error Rates (EER) 12.8%, 20.8% and 9.2% respectively. By using a combination of biometrics, the results showed clearly that the classification performance is better than using single biometric technique achieving EER 3.3%. Based on these findings, a novel architecture of multi-modal biometric authentication on mobile devices is proposed. The framework is able to provide a robust, continuous and transparent authentication in standalone and server-client modes regardless of mobile hardware configuration. The framework is able to continuously maintain the security status of the devices. With a high level of security status, users are permitted to access sensitive services and data. On the other hand, with the low level of security, users are required to re-authenticate before accessing sensitive service or data

Detecting replay attacks in audiovisual identity verification

We describe an algorithm that detects a lack of correspondence between speech and lip motion by detecting and monitoring the degree of synchrony between live audio and visual signals. It is simple, effective, and computationally inexpensive; providing a useful degree of robustness against basic replay attacks and against speech or image forgeries. The method is based on a cross-correlation analysis between two streams of features, one from the audio signal and the other from the image sequence. We argue that such an algorithm forms an effective first barrier against several kinds of replay attack that would defeat existing verification systems based on standard multimodal fusion techniques. In order to provide an evaluation mechanism for the new technique we have augmented the protocols that accompany the BANCA multimedia corpus by defining new scenarios. We obtain 0% equal-error rate (EER) on the simplest scenario and 35% on a more challenging one