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Hou, Dai, Han, Hao, Novak, Ed.  2020.  TAES: Two-factor Authentication with End-to-End Security against VoIP Phishing. 2020 IEEE/ACM Symposium on Edge Computing (SEC). :340—345.
In the current state of communication technology, the abuse of VoIP has led to the emergence of telecommunications fraud. We urgently need an end-to-end identity authentication mechanism to verify the identity of the caller. This paper proposes an end-to-end, dual identity authentication mechanism to solve the problem of telecommunications fraud. Our first technique is to use the Hermes algorithm of data transmission technology on an unknown voice channel to transmit the certificate, thereby authenticating the caller's phone number. Our second technique uses voice-print recognition technology and a Gaussian mixture model (a general background probabilistic model) to establish a model of the speaker to verify the caller's voice to ensure the speaker's identity. Our solution is implemented on the Android platform, and simultaneously tests and evaluates transmission efficiency and speaker recognition. Experiments conducted on Android phones show that the error rate of the voice channel transmission signature certificate is within 3.247 %, and the certificate signature verification mechanism is feasible. The accuracy of the voice-print recognition is 72%, making it effective as a reference for identity authentication.
Cesconetto, Jonas, Silva, Luís A., Valderi Leithardt, R. Q., Cáceres, María N., Silva, Luís A., Garcia, Nuno M..  2020.  PRIPRO:Solution for user profile control and management based on data privacy. 2020 15th Iberian Conference on Information Systems and Technologies (CISTI). :1—6.
Intelligent environments work collaboratively, bringing more comfort to human beings. The intelligence of these environments comes from technological advances in sensors and communication. IoT is the model developed that allows a wide and intelligent communication between devices. Hardware reduction of IoT devices results in vulnerabilities. Thus, there are numerous concerns regarding the security of user information, since mobile devices are easily trackable over the Internet. Care must be taken regarding the information in user profiles. Mobile devices are protected by a permission-based mechanism, which limits third-party applications from accessing sensitive device resources. In this context, this work aims to present a proposal for materialization of application for the evolution of user profiles in intelligent environments. Having as parameters the parameters presented in the proposed taxonomy. The proposed solution is the development of two applications, one for Android devices, responsible for allowing or blocking some features of the device. And another in Cloud, responsible for imposing the parameters and privacy criteria, formalizing the profile control module (PRIPRO - PRIvacy PROfiles).
Qian, Kai, Dan Lo, Chia-Tien, Guo, Minzhe, Bhattacharya, Prabir, Yang, Li.  2012.  Mobile security labware with smart devices for cybersecurity education. IEEE 2nd Integrated STEM Education Conference. :1—3.

Smart mobile devices such as smartphones and tablets have become an integral part of our society. However, it also becomes a prime target for attackers with malicious intents. There have been a number of efforts on developing innovative courseware to promote cybersecurity education and to improve student learning; however, hands-on labs are not well developed for smart mobile devices and for mobile security topics. In this paper, we propose to design and develop a mobile security labware with smart mobile devices to promote the cybersecurity education. The integration of mobile computing technologies and smart devices into cybersecurity education will connect the education to leading-edge information technologies, motivate and engage students in security learning, fill in the gap with IT industry need, and help faculties build expertise on mobile computing. In addition, the hands-on experience with mobile app development will promote student learning and supply them with a better understanding of security knowledge not only in classical security domains but also in the emerging mobile security areas.

Al-Dhaqm, A., Razak, S. A., Ikuesan, R. A., Kebande, V. R., Siddique, K..  2020.  A Review of Mobile Forensic Investigation Process Models. IEEE Access. 8:173359—173375.
Mobile Forensics (MF) field uses prescribed scientific approaches with a focus on recovering Potential Digital Evidence (PDE) from mobile devices leveraging forensic techniques. Consequently, increased proliferation, mobile-based services, and the need for new requirements have led to the development of the MF field, which has in the recent past become an area of importance. In this article, the authors take a step to conduct a review on Mobile Forensics Investigation Process Models (MFIPMs) as a step towards uncovering the MF transitions as well as identifying open and future challenges. Based on the study conducted in this article, a review of the literature revealed that there are a few MFIPMs that are designed for solving certain mobile scenarios, with a variety of concepts, investigation processes, activities, and tasks. A total of 100 MFIPMs were reviewed, to present an inclusive and up-to-date background of MFIPMs. Also, this study proposes a Harmonized Mobile Forensic Investigation Process Model (HMFIPM) for the MF field to unify and structure whole redundant investigation processes of the MF field. The paper also goes the extra mile to discuss the state of the art of mobile forensic tools, open and future challenges from a generic standpoint. The results of this study find direct relevance to forensic practitioners and researchers who could leverage the comprehensiveness of the developed processes for investigation.
Shah, P. R., Agarwal, A..  2020.  Cybersecurity Behaviour of Smartphone Users Through the Lens of Fogg Behaviour Model. 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA). :79—82.

It is now a fact that human is the weakest link in the cybersecurity chain. Many theories from behavioural science like the theory of planned behaviour and protection motivation theory have been used to investigate the factors that affect the cybersecurity behaviour and practices of the end-user. In this paper, the researchers have used Fogg behaviour model (FBM) to study factors affecting the cybersecurity behaviour and practices of smartphone users. This study found that the odds of secure behaviour and practices by respondents with high motivation and high ability were 4.64 times more than the respondents with low motivation and low ability. This study describes how FBM may be used in the design and development of cybersecurity awareness program leading to a behaviour change.

Sai, C. C., Prakash, C. S., Jose, J., Mana, S. C., Samhitha, B. K..  2020.  Analysing Android App Privacy Using Classification Algorithm. 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184). :551–555.
The interface permits the client to scan for a subjective utility on the Play Store; the authorizations posting and the protection arrangement are then routinely recovered, on all events imaginable. The client has then the capability of choosing an interesting authorization, and a posting of pertinent sentences are separated with the guide of the privateer's inclusion and introduced to them, alongside a right depiction of the consent itself. Such an interface allows the client to rapidly assess the security-related dangers of an Android application, by utilizing featuring the pertinent segments of the privateer's inclusion and by introducing helpful data about shrewd authorizations. A novel procedure is proposed for the assessment of privateer's protection approaches with regards to Android applications. The gadget actualized widely facilitates the way toward understanding the security ramifications of placing in 1/3 birthday celebration applications and it has just been checked in a situation to feature troubling examples of uses. The gadget is created in light of expandability, and correspondingly inclines in the strategy can without trouble be worked in to broaden the unwavering quality and adequacy. Likewise, if your application handles non-open or delicate individual information, it would be ideal if you also allude to the extra necessities in the “Individual and Sensitive Information” territory underneath. These Google Play necessities are notwithstanding any prerequisites endorsed by method for material security or data assurance laws. It has been proposed that, an individual who needs to perform the establishment and utilize any 1/3 festival application doesn't perceive the significance and which methods for the consents mentioned by method for an application, and along these lines sincerely gives all the authorizations as a final product of which unsafe applications furthermore get set up and work their malevolent leisure activity in the rear of the scene.
Khan, A., Chefranov, A. G..  2020.  A Captcha-Based Graphical Password With Strong Password Space and Usability Study. 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). :1—6.

Security for authentication is required to give a superlative secure users' personal information. This paper presents a model of the Graphical password scheme under the impact of security and ease of use for user authentication. We integrate the concept of recognition with re-called and cued-recall based schemes to offer superior security compared to existing schemes. Click Symbols (CS) Alphabet combine into one entity: Alphanumeric (A) and Visual (V) symbols (CS-AV) is Captcha-based password scheme, we integrate it with recall-based n ×n grid points, where a user can draw the shape or pattern by the intersection of the grid points as a way to enter a graphical password. Next scheme, the combination of CS-AV with grid cells allows very large password space ( 2.4 ×104 bits of entropy) and provides reasonable usability results by determining an empirical study of memorable password space. Proposed schemes support most applicable platform for input devices and promising strong resistance to shoulder surfing attacks on a mobile device which can be occurred during unlocking (pattern) the smartphone.

Yerima, S. Y., Alzaylaee, M. K..  2020.  Mobile Botnet Detection: A Deep Learning Approach Using Convolutional Neural Networks. 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1—8.

Android, being the most widespread mobile operating systems is increasingly becoming a target for malware. Malicious apps designed to turn mobile devices into bots that may form part of a larger botnet have become quite common, thus posing a serious threat. This calls for more effective methods to detect botnets on the Android platform. Hence, in this paper, we present a deep learning approach for Android botnet detection based on Convolutional Neural Networks (CNN). Our proposed botnet detection system is implemented as a CNN-based model that is trained on 342 static app features to distinguish between botnet apps and normal apps. The trained botnet detection model was evaluated on a set of 6,802 real applications containing 1,929 botnets from the publicly available ISCX botnet dataset. The results show that our CNN-based approach had the highest overall prediction accuracy compared to other popular machine learning classifiers. Furthermore, the performance results observed from our model were better than those reported in previous studies on machine learning based Android botnet detection.

Gamba, J., Rashed, M., Razaghpanah, A., Tapiador, J., Vallina-Rodriguez, N..  2020.  An Analysis of Pre-installed Android Software. 2020 IEEE Symposium on Security and Privacy (SP). :1039—1055.

The open-source nature of the Android OS makes it possible for manufacturers to ship custom versions of the OS along with a set of pre-installed apps, often for product differentiation. Some device vendors have recently come under scrutiny for potentially invasive private data collection practices and other potentially harmful or unwanted behavior of the preinstalled apps on their devices. Yet, the landscape of preinstalled software in Android has largely remained unexplored, particularly in terms of the security and privacy implications of such customizations. In this paper, we present the first large- scale study of pre-installed software on Android devices from more than 200 vendors. Our work relies on a large dataset of real-world Android firmware acquired worldwide using crowd-sourcing methods. This allows us to answer questions related to the stakeholders involved in the supply chain, from device manufacturers and mobile network operators to third- party organizations like advertising and tracking services, and social network platforms. Our study allows us to also uncover relationships between these actors, which seem to revolve primarily around advertising and data-driven services. Overall, the supply chain around Android's open source model lacks transparency and has facilitated potentially harmful behaviors and backdoored access to sensitive data and services without user consent or awareness. We conclude the paper with recommendations to improve transparency, attribution, and accountability in the Android ecosystem.

Kaftannikov, I. L., Kozlova, A. V., Khlyzov, A. D..  2020.  Prototype of a Li-Fi Communication System for Data Exchange Between Mobile Devices. 2020 Global Smart Industry Conference (GloSIC). :192—198.

This paper deals with the design and development of a Li-Fi (light fidelity) simplex communication system for data exchange between Android mobile devices. Li-Fi is an up-to-date technology in the modern world, since it uses visible light for data exchange, allowing for high-speed communication. The paper includes a brief review of Li-Fi technology, a review of the literature used, and a study of technological methods for implementing such systems, based on scientific sources. We propose the algorithms for data exchange, packet formation, and encryption-decryption. The paper presents the developed mobile application and the transceiver device, the development results, as well as experiments with the developed prototype. The results show that Li-Fi technology is workable and is a good alternative to existing communication methods.

Krasnov, A. N., Prakhova, M. Y., Novikova, U. V..  2020.  Ensuring Cybersecurity of Data Transmission in Limited Energy Consumption Networks. 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon). :1—5.

In the northern gas fields, most data are transmitted via wireless networks, which requires special transmission security measures. Herewith, the gas field infrastructure dictates cybersecurity modules to not only meet standard requirements but also ensure reduced energy consumption. The paper discusses the issue of building such a module for a process control system based on the RTP-04M recorder operating in conjunction with an Android-based mobile device. The software options used for the RSA and Diffie-Hellman data encryption and decryption algorithms on both the RTP-04M and the Android-based mobile device sides in the Keil μVision4 and Android Studio software environments, respectively, have shown that the Diffie-Hellman algorithm is preferable. It provides significant savings in RAM and CPU resources and power consumption of the recorder. In terms of energy efficiency, the implemented programs have been analyzed in the Android Studio (Android Profiler) and Simplicity Studio (Advanced Energy Monitor) environments. The integration of this module into the existing software will improve the field's PCS cybersecurity level due to protecting data transmitted from third-party attacks.

Kalaiyarasi, G., Balaji, K., Narmadha, T., Naveen, V..  2020.  E-Voting System In Smart Phone Using Mobile Application. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). :1466—1469.

The development in the web technologies given growth to the new application that will make the voting process very easy and proficient. The E-voting helps in providing convenient, capture and count the votes in an election. This project provides the description about e-voting using an Android platform. The proposed e-voting system helps the user to cast the vote without visiting the polling booth. The application provides authentication measures in order to avoid fraud voters using the OTP. Once the voting process is finished the results will be available within a fraction of seconds. All the casted vote count is encrypted using AES256 algorithm and stored in the database in order to avoid any outbreaks and revelation of results by third person other than the administrator.

Wang, Y., Gao, W., Hei, X., Mungwarama, I., Ren, J..  2020.  Independent credible: Secure communication architecture of Android devices based on TrustZone. 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). :85—92.

The development of mobile internet has brought convenience to people, but the openness and diversity of mobile Internet make it face the security threat of communication privacy data disclosure. In this paper, a trusted android device security communication method based on TrustZone is proposed. Firstly, Elliptic Curve Diffie-Hellman (ECDH) key agreement algorithm is used to make both parties negotiate the session key in the Trusted Execution Environment (TEE), and then, we stored the key safely in the TEE. Finally, TEE completes the encryption and decryption of the transmitted data. This paper constructs a secure communication between mobile devices without a trusted third party and analyzes the feasibility of the method from time efficiency and security. The experimental results show that the method can resist malicious application monitoring in the process of data encryption and ensures the security of the session key. Compared with the traditional scheme, it is found that the performance of the scheme is not significantly reduced.

Goswami, U., Wang, K., Nguyen, G., Lagesse, B..  2020.  Privacy-Preserving Mobile Video Sharing using Fully Homomorphic Encryption. 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). :1—3.

Increased availability of mobile cameras has led to more opportunities for people to record videos of significantly more of their lives. Many times people want to share these videos, but only to certain people who were co-present. Since the videos may be of a large event where the attendees are not necessarily known, we need a method for proving co-presence without revealing information before co-presence is proven. In this demonstration, we present a privacy-preserving method for comparing the similarity of two videos without revealing the contents of either video. This technique leverages the Similarity of Simultaneous Observation technique for detecting hidden webcams and modifies the existing algorithms so that they are computationally feasible to run under fully homomorphic encryption scheme on modern mobile devices. The demonstration will consist of a variety of devices preloaded with our software. We will demonstrate the video sharing software performing comparisons in real time. We will also make the software available to Android devices via a QR code so that participants can record and exchange their own videos.

Siddiquie, K., Shafqat, N., Masood, A., Abbas, H., Shahid, W. b.  2020.  Profiling Vulnerabilities Threatening Dual Persona in Android Framework. 2019 International Conference on Advances in the Emerging Computing Technologies (AECT). :1—6.

Enterprises round the globe have been searching for a way to securely empower AndroidTM devices for work but have spurned away from the Android platform due to ongoing fragmentation and security concerns. Discrepant vulnerabilities have been reported in Android smartphones since Android Lollipop release. Smartphones can be easily hacked by installing a malicious application, visiting an infectious browser, receiving a crafted MMS, interplaying with plug-ins, certificate forging, checksum collisions, inter-process communication (IPC) abuse and much more. To highlight this issue a manual analysis of Android vulnerabilities is performed, by using data available in National Vulnerability Database NVD and Android Vulnerability website. This paper includes the vulnerabilities that risked the dual persona support in Android 5 and above, till Dec 2017. In our security threat analysis, we have identified a comprehensive list of Android vulnerabilities, vulnerable Android versions, manufacturers, and information regarding complete and partial patches released. So far, there is no published research work that systematically presents all the vulnerabilities and vulnerability assessment for dual persona feature of Android's smartphone. The data provided in this paper will open ways to future research and present a better Android security model for dual persona.

Guri, M..  2020.  CD-LEAK: Leaking Secrets from Audioless Air-Gapped Computers Using Covert Acoustic Signals from CD/DVD Drives. 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). :808—816.

Air-gapped networks are isolated from the Internet, since they store and process sensitive information. It has been shown that attackers can exfiltrate data from air-gapped networks by sending acoustic signals generated by computer speakers, however this type of covert channel relies on the existence of loudspeakers in the air-gapped environment. In this paper, we present CD-LEAK - a novel acoustic covert channel that works in constrained environments where loudspeakers are not available to the attacker. Malware installed on a compromised computer can maliciously generate acoustic signals via the optical CD/DVD drives. Binary information can then be modulated over the acoustic signals and be picked up by a nearby Internet connected receiver (e.g., a workstation, hidden microphone, smartphone, laptop, etc.). We examine CD/DVD drives and discuss their acoustical characteristics. We also present signal generation and detection, and data modulation and demodulation algorithms. Based on our proposed method, we developed a transmitter and receiver for PCs and smartphones, and provide the design and implementation details. We examine the channel and evaluate it on various optical drives. We also provide a set of countermeasures against this threat - which has been overlooked.

Ge, X., Pan, Y., Fan, Y., Fang, C..  2019.  AMDroid: Android Malware Detection Using Function Call Graphs. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :71—77.

With the rapid development of the mobile Internet, Android has been the most popular mobile operating system. Due to the open nature of Android, c countless malicious applications are hidden in a large number of benign applications, which pose great threats to users. Most previous malware detection approaches mainly rely on features such as permissions, API calls, and opcode sequences. However, these approaches fail to capture structural semantics of applications. In this paper, we propose AMDroid that leverages function call graphs (FCGs) representing the behaviors of applications and applies graph kernels to automatically learn the structural semantics of applications from FCGs. We evaluate AMDroid on the Genome Project, and the experimental results show that AMDroid is effective to detect Android malware with 97.49% detection accuracy.

Fan, M., Luo, X., Liu, J., Wang, M., Nong, C., Zheng, Q., Liu, T..  2019.  Graph Embedding Based Familial Analysis of Android Malware using Unsupervised Learning. 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE). :771—782.

The rapid growth of Android malware has posed severe security threats to smartphone users. On the basis of the familial trait of Android malware observed by previous work, the familial analysis is a promising way to help analysts better focus on the commonalities of malware samples within the same families, thus reducing the analytical workload and accelerating malware analysis. The majority of existing approaches rely on supervised learning and face three main challenges, i.e., low accuracy, low efficiency, and the lack of labeled dataset. To address these challenges, we first construct a fine-grained behavior model by abstracting the program semantics into a set of subgraphs. Then, we propose SRA, a novel feature that depicts the similarity relationships between the Structural Roles of sensitive API call nodes in subgraphs. An SRA is obtained based on graph embedding techniques and represented as a vector, thus we can effectively reduce the high complexity of graph matching. After that, instead of training a classifier with labeled samples, we construct malware link network based on SRAs and apply community detection algorithms on it to group the unlabeled samples into groups. We implement these ideas in a system called GefDroid that performs Graph embedding based familial analysis of AnDroid malware using unsupervised learning. Moreover, we conduct extensive experiments to evaluate GefDroid on three datasets with ground truth. The results show that GefDroid can achieve high agreements (0.707-0.883 in term of NMI) between the clustering results and the ground truth. Furthermore, GefDroid requires only linear run-time overhead and takes around 8.6s to analyze a sample on average, which is considerably faster than the previous work.

SAADI, C., kandrouch, i, CHAOUI, H..  2019.  Proposed security by IDS-AM in Android system. 2019 5th International Conference on Optimization and Applications (ICOA). :1—7.

Mobile systems are always growing, automatically they need enough resources to secure them. Indeed, traditional techniques for protecting the mobile environment are no longer effective. We need to look for new mechanisms to protect the mobile environment from malicious behavior. In this paper, we examine one of the most popular systems, Android OS. Next, we will propose a distributed architecture based on IDS-AM to detect intrusions by mobile agents (IDS-AM).

Byrne, K., Marín, C..  2018.  Human Trust in Robots When Performing a Service. 2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). :9—14.

The presence of robots is becoming more apparent as technology progresses and the market focus transitions from smart phones to robotic personal assistants such as those provided by Amazon and Google. The integration of robots in our societies is an inevitable tendency in which robots in many forms and with many functionalities will provide services to humans. This calls for an understanding of how humans are affected by both the presence of and the reliance on robots to perform services for them. In this paper we explore the effects that robots have on humans when a service is performed on request. We expose three groups of human participants to three levels of service completion performed by robots. We record and analyse human perceptions such as propensity to trust, competency, responsiveness, sociability, and team work ability. Our results demonstrate that humans tend to trust robots and are more willing to interact with them when they autonomously recover from failure by requesting help from other robots to fulfil their service. This supports the view that autonomy and team working capabilities must be brought into robots in an effort to strengthen trust in robots performing a service.

Qian, K., Parizi, R. M., Lo, D..  2018.  OWASP Risk Analysis Driven Security Requirements Specification for Secure Android Mobile Software Development. 2018 IEEE Conference on Dependable and Secure Computing (DSC). :1—2.
The security threats to mobile applications are growing explosively. Mobile apps flaws and security defects open doors for hackers to break in and access sensitive information. Defensive requirements analysis should be an integral part of secure mobile SDLC. Developers need to consider the information confidentiality and data integrity, to verify the security early in the development lifecycle rather than fixing the security holes after attacking and data leaks take place. Early eliminating known security vulnerabilities will help developers increase the security of apps and reduce the likelihood of exploitation. However, many software developers lack the necessary security knowledge and skills at the development stage, and that's why Secure Mobile Software Development education is very necessary for mobile software engineers. In this paper, we propose a guided security requirement analysis based on OWASP Mobile Top ten security risk recommendations for Android mobile software development and its traceability of the developmental controls in SDLC. Building secure apps immune to the OWASP Mobile Top ten risks would be an effective approach to provide very useful mobile security guidelines.
Jaiswal, M., Malik, Y., Jaafar, F..  2018.  Android gaming malware detection using system call analysis. 2018 6th International Symposium on Digital Forensic and Security (ISDFS). :1—5.
Android operating systems have become a prime target for attackers as most of the market is currently dominated by Android users. The situation gets worse when users unknowingly download or sideload cloning applications, especially gaming applications that look like benign games. In this paper, we present, a dynamic Android gaming malware detection system based on system call analysis to classify malicious and legitimate games. We performed the dynamic system call analysis on normal and malicious gaming applications while applications are in execution state. Our analysis reveals the similarities and differences between benign and malware game system calls and shows how dynamically analyzing the behavior of malicious activity through system calls during runtime makes it easier and is more effective to detect malicious applications. Experimental analysis and results shows the efficiency and effectiveness of our approach.
Maksutov, A. A., Dmitriev, S. O., Lysenkov, V. I., Valter, D. A..  2018.  Mobile bootloader with security features. 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :335—338.
Modern mobile operating systems store a lot of excessive information that can be used against its owner or organization, like a call history or various system logs. This article describes a universal way of preventing any mobile operating system or application from saving its data in device's internal storage without reducing their functionality. The goal of this work is creation of a software that solves the described problem and works on the bootloading stage. A general algorithm of the designed software, along with its main solutions and requirements, is presented in this paper. Hardware requirement, software testing results and general applications of this software are also listed in this paper.
Xylogiannopoulos, Konstantinos F., Karampelas, Panagiotis, Alhajj, Reda.  2019.  Text Mining for Malware Classification Using Multivariate All Repeated Patterns Detection. 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). :887—894.

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.

Dagelić, Ante, Perković, Toni, Čagalj, Mario.  2019.  Location Privacy and Changes in WiFi Probe Request Based Connection Protocols Usage Through Years. 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech). :1–5.
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.