Biblio

The widespread use of powerful mobile devices has deeply affected the mix of traffic traversing both the Internet and enterprise networks (with bring-your-own-device policies). Traffic encryption has become extremely common, and the quick proliferation of mobile apps and their simple distribution and update have created a specifically challenging scenario for traffic classification and its uses, especially network-security related ones. The recent rise of Deep Learning (DL) has responded to this challenge, by providing a solution to the time-consuming and human-limited handcrafted feature design, and better clas-sification performance. The counterpart of the advantages is the lack of interpretability of these black-box approaches, limiting or preventing their adoption in contexts where the reliability of results, or interpretability of polices is necessary. To cope with these limitations, eXplainable Artificial Intelligence (XAI) techniques have seen recent intensive research. Along these lines, our work applies XAI-based techniques (namely, Deep SHAP) to interpret the behavior of a state-of-the-art multimodal DL traffic classifier. As opposed to common results seen in XAI, we aim at a global interpretation, rather than sample-based ones. The results quantify the importance of each modality (payload- or header-based), and of specific subsets of inputs (e.g., TLS SNI and TCP Window Size) in determining the classification outcome, down to per-class (viz. application) level. The analysis is based on a publicly-released recent dataset focused on mobile app traffic.

Smart cities and societies are driving unprecedented technological and socioeconomic growth in everyday life albeit making us increasingly vulnerable to infinitely and incomprehensibly diverse threats. Short Message Service (SMS) spam is one such threat that can affect mobile security by propagating malware on mobile devices. A security breach could also cause a mobile device to send spam messages. Many works have focused on classifying incoming SMS messages. This paper proposes a tool to detect spam from outgoing SMS messages, although the work can be applied to both incoming and outgoing SMS messages. Specifically, we develop a system that comprises multiple machine learning (ML) based classifiers built by us using three classification methods – Naïve Bayes (NB), Support Vector Machine (SVM), and Naïve Bayes Multinomial (NBM)- and five preprocessing and feature extraction methods. The system is built to allow its execution in cloud, fog or edge layers, and is evaluated using 15 datasets built by 4 widely-used public SMS datasets. The system detects spam SMSs and gives recommendations on the spam filters and classifiers to be used based on user preferences including classification accuracy, True Negatives (TN), and computational resource requirements.

With fast growing research in the area of application partitioning for offloading, determining which devices to prioritize over the other for mobile code offloading is fundamental. Multiple methods can be adopted using both single-criterion and multiple-criteria strategies. Due to the characteristics of pervasive environments, whereby devices having different computing capability, different level of privacy and security and the mobility nature in such environment makes the decision-making process complex. To this end, this paper proposes a method using a combination of the method Analytic Hierarchy Process (AHP) to calculate weights criteria of participating devices. Next the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is considered to sort in order of priority the participating devices, hence facilitating the decision to opt for which participating device first. An evaluation of the method is also presented.

Mobile data offloading using opportunistic network has recently gained its significance to increase mobile data needs. Such offloaders need to be properly incentivized to encourage more and more users to act as helpers in such networks. The extent of help offered by mobile data offloading alternatives using appropriate incentive mechanisms is significant in such scenarios. The limitation of existing incentive mechanisms is that they are partial in implementation while most of them use third party intervention based derivation. However, none of the papers considers trust as an essential factor for incentive distribution. Although few works contribute to the trust analysis, but the implementation is limited to offloading determination only while the incentive is independent of trust. We try to investigate if trust could be related to the Nash equilibrium based incentive evaluation. Our analysis results show that trust-based incentive distribution encourages more than 50% offloaders to act positively and contribute successfully towards efficient mobile data offloading. We compare the performance of our algorithm with literature based salary-bonus scheme implementation and get optimum incentive beyond 20% dependence over trust-based output.

User identity and personal information remain to be hot targets for attackers. From recent surveys, we can categorize that 65.5% of all cyberattacks in 2018 target user information. Sadly, most of the time, the system's security depends on how secure it is the implementation from the provider-side. One defense technique that the user can take part in is applying a two-factor authentication (2FA) system for their account. However, we observe that state-of-the-art 2FAs have several weaknesses and limitations. In this paper, we propose TwoChain, a blockchain-based 2FA system for web services to overcome those issues. Our implementation facilitates an alternative 2FA system that is more secure, disposable, and decentralized. Finally, we release TwoChain for public use.

Two-factor authentication (2FA) systems implement by verifying at least two factors. A factor is something a user knows (password, or phrase), something a user possesses (smart card, or smartphone), something a user is (fingerprint, or iris), something a user does (keystroke), or somewhere a user is (location). In the existing 2FA system, a user is required to act in order to implement the second layer of authentication which is not very user-friendly. Smart devices (phones, laptops, tablets, etc.) can receive signals from different radio frequency technologies within range. As these devices move among networks (Wi-Fi access points, cellphone towers, etc.), they receive broadcast messages, some of which can be used to collect information. This information can be utilized in a variety of ways, such as establishing a connection, sharing information, locating devices, and, most appropriately, identifying users in range. The principal benefit of broadcast messages is that the devices can read and process the embedded information without being connected to the broadcaster. Moreover, the broadcast messages can be received only within range of the wireless access point sending the broadcast, thus inherently limiting access to those devices in close physical proximity and facilitating many applications dependent on that proximity. In the proposed research, a new factor is used - something that is in the user's environment with minimal user involvement. Data from these broadcast messages is utilized to implement a 2FA scheme by determining whether two devices are proximate or not to ensure that they belong to the same user.

With the development of augmented reality(AR) technology and location-based service (LBS) technology, combining AR with LBS will create a new way of life and socializing. In AR, users may consider the privacy and security of data. In LBS, the leakage of user location privacy is an important threat to LBS users. Therefore, it is very important for privacy management of positioning information and user location privacy to avoid loopholes and abuse. In this review, the concepts and principles of AR technology and LBS would be introduced. The existing privacy measurement and privacy protection framework would be analyzed and summarized. Also future research direction of location privacy protection would be discussed.

In cyber physical systems, cybersecurity and data privacy are among most critical considerations when dealing with communications, processing, and storage of data. Geospatial data and medical data are examples of big data that require seamless integration with computational algorithms as outlined in Industry 4.0 towards adoption of fourth industrial revolution. Healthcare Industry 4.0 is an application of the design principles of Industry 4.0 to the medical domain. Mobile applications are now widely used to accomplish important business functions in almost all industries. These mobile devices, however, are resource poor and proved insufficient for many important medical applications. Resource rich cloud services are used to augment poor mobile device resources for data and compute intensive applications in the mobile cloud computing paradigm. However, the performance of cloud services is undesirable for data-intensive, latency-sensitive mobile applications due increased hop count between the mobile device and the cloud server. Cloudlets are virtual machines hosted in server placed nearby the mobile device and offer an attractive alternative to the mobile cloud computing in the form of mobile edge computing. This paper outlines cybersecurity and data privacy aspects for communications of measured patient data from wearable wireless biosensors to nearby cloudlet host server in order to facilitate the cloudlet based preliminary and essential complex analytics for the medical big data.

The trend of rapid development of mobile technologies has highlighted new challenges and threats regarding the information security by the using cookies in mobile learning (m-learning) systems. In order to overcome these challenges and threats, this paper has identified two main objectives. First, to give a review of most common types to cookies and second, to consider the challenges and threats regarding cookies with aspects that are directly related to issues of security and privacy. With these objectives is possible to bridge security gaps in m-learning systems. Moreover, the identified potential challenges and threats are discussed with the given proposals of pragmatic solutions for their mitigating or reducing. The findings of this research may help students to rise security awareness and security behavior in m-learning systems, and to better understand on-going security challenges and threats in m-learning systems.

Electronic voting systems have enhanced efficiency in student elections management in universities, supporting such elections to become less expensive, logistically simple, with higher accuracy levels as compared to manually conducted elections. However, e-voting systems that are confined to campus hall voting inhibits access to eligible voters who are away from campus. This study examined the challenges of lack of wide access and impersonation of voter in the student elections of 2018 in Kabarak University. The main objective of this study was therefore to upgrade the offline electronic voting system through developing a secure online voting system and deploying the system for use in the 2019 student elections at Kabarak University. The resultant system and development process employed demonstrate the applicability of a secure online voting not only in the higher education context, but also in other democracies where infusion of online access and authentication in the voting processes is a requisite.

Mobile expressive rendering gained increasing popularity among users seeking casual creativity by image stylization and supports the development of mobile artists as a new user group. In particular, neural style transfer has advanced as a core technology to emulate characteristics of manifold artistic styles. However, when it comes to creative expression, the technology still faces inherent limitations in providing low-level controls for localized image stylization. This work enhances state-of-the-art neural style transfer techniques by a generalized user interface with interactive tools to facilitate a creative and localized editing process. Thereby, we first propose a problem characterization representing trade-offs between visual quality, run-time performance, and user control. We then present MaeSTrO, a mobile app for orchestration of neural style transfer techniques using iterative, multi-style generative and adaptive neural networks that can be locally controlled by on-screen painting metaphors. At this, first user tests indicate different levels of satisfaction for the implemented techniques and interaction design.

The Moving Picture Experts Group's Compact Descriptors for Visual Search (MPEG's CDVS) intends to standardize technologies in order to enable an interoperable, efficient, and cross-platform solution for internet-scale visual search applications and services. Among the key technologies within CDVS, we recall the format of visual descriptors, the descriptor extraction process, and the algorithms for indexing and matching. Unfortunately, these steps require precision and computation accuracy. Moreover, they are very time-consuming, as they need running times in the order of seconds when implemented on the central processing unit (CPU) of modern mobile devices. In this paper, to reduce computation times and maintain precision and accuracy, we re-design, for many-cores embedded graphical processor units (GPUs), all main local descriptor extraction pipeline phases of the MPEG's CDVS standard. To reach this goal, we introduce new techniques to adapt the standard algorithm to parallel processing. Furthermore, to reduce memory accesses and efficiently distribute the kernel workload, we use new approaches to store and retrieve CDVS information on proper GPU data structures. We present a complete experimental analysis on a large and standard test set. Our experiments show that our GPU-based approach is remarkably faster than the CPU-based reference implementation of the standard, and it maintains a comparable precision in terms of true and false positive rates.

In this article, we introduce the MACH-2K trust overlay network and its architecture. MACH-2K's objectives are to (a) enhance the resiliency of emergency response and public service networks and (b) help build such networks in places, or at times, where network infrastructure is limited. Resiliency may be enhanced in an economic manner by building new ad hoc networks of private mobile devices and joining these to public service networks at specific trusted points. The major barrier to building resiliency by using private devices is ensuring security. MACH-2K uses device location and communication utility patterns to assign trust to devices, after owner approval. After trust is established, message confidentiality, privacy, and integrity may be implemented by well-known cryptographic means. MACH-2K devices may be then requested to forward or consume different types of messages depending on their current level of trust and utility.

Mobile phones have become nowadays a commodity to the majority of people. Using them, people are able to access the world of Internet and connect with their friends, their colleagues at work or even unknown people with common interests. This proliferation of the mobile devices has also been seen as an opportunity for the cyber criminals to deceive smartphone users and steel their money directly or indirectly, respectively, by accessing their bank accounts through the smartphones or by blackmailing them or selling their private data such as photos, credit card data, etc. to third parties. This is usually achieved by installing malware to smartphones masking their malevolent payload as a legitimate application and advertise it to the users with the hope that mobile users will install it in their devices. Thus, any existing application can easily be modified by integrating a malware and then presented it as a legitimate one. In response to this, scientists have proposed a number of malware detection and classification methods using a variety of techniques. Even though, several of them achieve relatively high precision in malware classification, there is still space for improvement. In this paper, we propose a text mining all repeated pattern detection method which uses the decompiled files of an application in order to classify a suspicious application into one of the known malware families. Based on the experimental results using a real malware dataset, the methodology tries to correctly classify (without any misclassification) all randomly selected malware applications of 3 categories with 3 different families each.

Location privacy is one of most frequently discussed terms in the mobile devices security breaches and data leaks. With the expected growth of the number of IoT devices, which is 20 billions by 2020., location privacy issues will be further brought to focus. In this paper we give an overview of location privacy implications in wireless networks, mainly focusing on user's Preferred Network List (list of previously used WiFi Access Points) contained within WiFi Probe Request packets. We will showcase the existing work and suggest interesting topics for future work. A chronological overview of sensitive location data we collected on a musical festival in years 2014, 2015, 2017 and 2018 is provided. We conclude that using passive WiFi monitoring scans produces different results through years, with a significant increase in the usage of a more secure Broadcast Probe Request packets and MAC address randomizations by the smartphone operating systems.

Over the past few years, botnets have emerged as one of the most serious cybersecurity threats faced by individuals and organizations. After infecting millions of servers and workstations worldwide, botmasters have started to develop botnets for mobile devices. Mobile botnets use different mediums to communicate with their botmasters. Although significant research has been done to detect mobile botnets that use the Internet as their command and control (C&C) channel, little research has investigated SMS botnets per se. In order to fill this gap, in this paper, we first divide SMS botnets based on their characteristics into three families, namely, info stealer, SMS stealer, and SMS spammer. Then, we propose SMSBotHunter, a novel anomaly detection technique that detects SMS botnets using textual and behavioral features and one-class classification. We experimentally evaluate the detection performance of SMSBotHunter by simulating the behavior of human users and SMS botnets. The experimental results demonstrate that most of the SMS messages sent or received by info stealer and SMS spammer botnets can be detected using textual features exclusively. It is also revealed that behavioral features are crucial for the detection of SMS stealer botnets and will improve the overall detection performance.

Because of the high-value data of an enterprise, sophisticated cyber-attacks targeted at enterprise networks have become prominent. Attack graphs are useful tools that facilitate a scalable security analysis of enterprise networks. However, the administrators face difficulties in effectively modelling security problems and making right decisions when constructing attack graphs as their risk assessment experience is often limited. In this paper, we propose an innovative method of security assessment through an ontology- and graph-based approach. An ontology is designed to represent security knowledge such as assets, vulnerabilities, attacks, countermeasures, and relationships between them in a common vocabulary. An efficient algorithm is proposed to generate an attack graph based on the inference ability of the security ontology. The proposed algorithm is evaluated with different sizes and topologies of test networks; the results show that our proposed algorithm facilitates a scalable security analysis of enterprise networks.

We present a security analysis of several gesture CAPTCHA challenges designed to operate on mobiles. Mobile gesture CAPTCHA challenges utilize the accelerometer and the gyroscope inputs from a mobile to allow a human to solve a simple test by physically manipulating the device. We have evaluated the security of gesture CAPTCHA in mobile devices and found them resistant to a range of common automated attacks. Our study has shown that using an accelerometer and the gyroscope readings as an input to solve the CAPTCHA is difficult for malware, but easy for a real user. Gesture CAPTCHA is effective in differentiating between humans and machines.

We present novel CAPTCHA challenges based on user gestures designed for mobile. A gesture CAPTCHA challenge is a security mechanism to prevent malware from gaining access to network resources from mobile. Mobile devices contain a number of sensors that record the physical movement of the device. We utilized the accelerometer and gyroscope data as inputs to our novel CAPTCHAs to capture the physical manipulation of the device. We conducted an experimental study on a group of people. We discovered that younger people are able to solve this type of CAPTCHA challenges successfully in a short amount of time. We found that using accelerometer readings produces issues for some older people.

In this work, we perform security analysis of using an e-mail as a self-service password reset point, and exploit some of the vulnerabilities of e-mail servers' forgotten password reset paths. We perform and illustrate three different attacks on a personal Email account, using a variety of tools such as: public knowledge attainable through social media or public records to answer security questions and execute a social engineering attack, hardware available to the public to perform a man in the middle attack, and free software to perform a brute-force attack on the login of the email account. Our results expose some of the inherent vulnerabilities in using emails as password reset points. The findings are extremely relevant to the security of mobile devices since users' trend has leaned towards usage of mobile devices over desktops for Internet access.

In this paper, we implemented a mobile augmented reality system based on cloud computing. This system uses a mobile device with a camera to capture images of book spines and sends processed features to the cloud. In the cloud, the features are compared with the database and the information of the best matched book would be sent back to the mobile device. The information will then be rendered on the display via augmented reality. In order to reduce the transmission cost, the mobile device is used to perform most of the image processing tasks, such as the preprocessing, resizing, corner detection, and augmented reality rendering. On the other hand, the cloud is used to realize routine but large quantity feature comparisons. Using the cloud as the database also makes the future extension much more easily. For our prototype system, we use an Android smart phone as our mobile device, and Chunghwa Telecoms hicloud as the cloud.

Augmented reality is changing the way its users see the world. Smart augmented-reality glasses, with high resolution Optical Head Mounted display, supplements views of the real-world using video, audio, or graphics projected in front of user's eye. The area of Smart Glasses and heads-up display devices is not a new one, however in the last few years, it has seen an extensive growth in various fields including education. Our work takes advantage of a student's ability to adapt to new enabling technologies to investigate improvements teaching techniques in STEM areas and enhance the effectiveness and efficiency in teaching the new course content. In this paper, we propose to focus on the application of Smart Augmented-Reality Glasses in cybersecurity education to attract and retain students in STEM. In addition, creative ways to learn cybersecurity education via Smart Glasses will be explored using a Discovery Learning approach. This mode of delivery will allow students to interact with cybersecurity theories in an innovative, interactive and effective way, enhancing their overall live experience and experimental learning. With the help of collected data and in-depth analysis of existing smart glasses, the ongoing work will lay the groundwork for developing augmented reality applications that will enhance the learning experiences of students. Ultimately, research conducted with the glasses and applications may help to identify the unique skillsets of cybersecurity analysts, learning gaps and learning solutions.

Mobile Ad hoc Network (MANET) is the collection of mobile devices which could change the locations and configure themselves without a centralized base point. Mobile Ad hoc Networks are vulnerable to attacks due to its dynamic infrastructure. The routing attacks are one among the possible attacks that causes damage to MANET. This paper gives a new method of risk aware response technique which is combined version the Dijkstra's shortest path algorithm and Destination Sequenced Distance Vector (DSDV) algorithm. This can reduce black hole attacks. Dijkstra's algorithm finds the shortest path from the single source to the destination when the edges have positive weights. The DSDV is an improved version of the conventional technique by adding the sequence number and next hop address in each routing table.

Mobile crowdsensing (MCS) is a distributed sensing paradigm that uses a variety of built-in sensors in smart mobile devices to enable ubiquitous acquisition of sensory data from surroundings. However, non-dedicated nature of MCS results in vulnerabilities in the presence of malicious participants to compromise the availability of the MCS components, particularly the servers and participants' devices. In this paper, we focus on Denial of Service attacks in MCS where malicious participants submit illegitimate task requests to the MCS platform to keep MCS servers busy while having sensing devices expend energy needlessly. After reviewing Artificial Intelligence-based security solutions for MCS systems, we focus on a typical location-based and energy-oriented DoS attack, and present a security solution that applies ensemble techniques in machine learning to identify illegitimate tasks and prevent personal devices from pointless energy consumption so as to improve the availability of the whole system. Through simulations, we show that ensemble techniques are capable of identifying illegitimate and legitimate tasks while gradient boosting appears to be a preferable solution with an AUC performance higher than 0.88 in the precision-recall curve. We also investigate the impact of environmental settings on the detection performance so as to provide a clearer understanding of the model. Our performance results show that MCS task legitimacy decisions with high F-scores are possible for both illegitimate and legitimate tasks.

Nowadays, mobile devices have become one of the most popular instruments used by a person on its regular life, mainly due to the importance of their applications. In that context, mobile devices store user's personal information and even more data, becoming a personal tracker for daily activities that provides important information about the user. Derived from this gathering of information, many tools are available to use on mobile devices, with the restrain that each tool only provides isolated information about a specific application or activity. Therefore, the present work proposes a tool that allows investigators to obtain a complete report and timeline of the activities that were performed on the device. This report incorporates the information provided by many sources into a unique set of data. Also, by means of an example, it is presented the operation of the solution, which shows the feasibility in the use of this tool and shows the way in which investigators have to apply the tool.