Malware Detection in Android Operating System

Smartphone applications became very popular nowadays as they provide useful functionalities to our daily lives over ordinary voice services. They offer a small but powerful computing platform where intelligent algorithms can be coded into some live‐saving products which profoundly impact our daily lifestyles. A mobile fatigue detection system which is proposed in this study is an important life‐saving application running on smartphone. Mobile detection system faces technological challenges such as: (i) embracing relatively low‐resolution images in image recognition; (ii) supporting fast response time given a low‐power CPU in comparison to a desktop computer; and (iii) demanding for high prediction accuracy by light‐weight machine learning algorithms, as the software programs embedded in smartphones is resource constrained. Solutions with respect to indoor facial profiling system which are mainly based on progressive locating method for eye detection are discussed in this study. Acceptable experimental results in terms of eye detection rate and driver fatigue detection in different situations are presented.

Service-oriented mobile malware detection system based on mining strategies

Mobile devices implementing Android operating systems inherently encourage opportunities to create and implement malicious software. This opportunity increases as dissemination of the Android OS to standalone devices, such as cameras, increases. The problem intensifies when these devices utilize cloud storage service capabilities. Previous security and forensics research is focused on Android malware detection, data leakage and operating system modifications. In this research, malware was developed and implemented on a camera running an Android Operating System. The execution of this malware successfully transferred images to a secondary cloud location without notifying the user about the data transfer or raising noticeable indicators with an industry accepted mobile device analysis toolkit. The research contribution is an initial empirical analysis of the viability of software to divert an image and store it in a secondary cloud storage environment without alerting digital forensics software about the malicious nature of the malware.

This paper proposes a mobile system for real-time fetal heart rate (FHR) monitoring and fetal distress detection. In the proposed system, FHR values are collected using machine vision techniques and analyzed using a pattern matching approach. When a fetal distress is detected, a medical alarm will automatically notify medical experts through a mobile GSM network. Through the proposed system, not only a pregnant woman can track the healthy status of her baby, but also medical experts can allocate medical resources in time. The implementation result shows that the developed mobile system can be a helpful application for medical management.

With the rapid advancements of electronics, the mobile operating system can accommodate various applications, which greatly facilitates people's everyday life.With a user group of more than 2 billion, the Android platform provides a diverse ecosystem for developing and publishing all sorts of applications.Although Google's official application store, Google Play, contains over 2 million apps, such a huge market also attracts hackers to make profits through distributing malware.Mobile malware has rocketed since 2009.As reported by Broadcom Inc., an industry-leading security company, 2017 witnessed an increase of new mobile malware strains, com-pared with the year of 2016.Additionally, more profitdriven malware emerged with the growth of underground markets.Due to the fragmentation problem of the Android plat-form, Android has long been the most targeted operating system suffering from attacks.To keep pace with the cutting-edge antimalware countermeasures adopted by cybersecurity businesses, malware developers have abused high-level obfuscation, virtual environment recognition, conditional execution (logic bomb), run-time payload dropping, etc., to fool their opponents (i.e., security defending products and reverse engineering tools).These techniques are usually more obvious to trace during the evolution and diversification of a malware family.In this thesis, we take a close look into both recent Android trojans and one specific family of Android banking trojan, that infiltrates banking applications to steal credentials or trick victims to type in their usernames and passwords through displaying fake login interfaces.The results indicate that Android Trojans evolves towards possessing more malicious capabilities and more diverse permutations without losing their core design, which would cause more limitations and ineffectiveness for modern security solutions.

In most nuclear and radiological emergencies involving the release of radioactive materials to environment, it is important that data on the geographic distribution of potentially hazardous radioactive materials be quickly presented to the authorities. The mobile radiation detection system makes it possible to measure radioactive materials in the environment at random places because of its easy mobility from place to place. The purpose of the work is to develop a mobile radiation detection system to measure gamma exposure rate, radioactive material on the ground and airborne radioactive particulate in the environment quickly. In this work, we have developed a carborne survey equipment system consisting of three gamma ray detectors, one airbome radioactive particulate sampler, a GPS (Global Positioning System), meteorological sensors, a data acquisition system and an operation software. Performance of the carborne survey equipment has been successfully demonstrated to measure iodine and argon release from medical isotope production facility on Serpong in western Java, Indonesia. Received: 04 October 2014; Revised:16 March 2015; Accepted: 23 March 2015

Mobile devices have evolved from simple devices, which are used for a phone call and SMS messages to smartphone devices that can run third party applications. Nowadays, malicious software, which is also known as malware, imposes a larger threat to these mobile devices. Recently, many news items were posted about the increase of the Android malware. There were a lot of Android applications pulled from the Android Market because they contained malware. The vulnerabilities of those Applications or Android operating systems are being exploited by the attackers who got the capability of penetrating into the mobile systems without user authorization causing compromise the confidentiality, integrity and availability of the applications and the user. This paper, it gave an update to the work done in the project. Moreover, this paper focuses on the Android Operating System and aim to detect existing Android malware. It has a dataset that contained 104 malware samples. This Paper chooses several malware from the dataset and attempting to analyze them to understand their installation methods and activation. In addition, it evaluates the most popular existing anti-virus software to see if these 104 malware could be detected.

Here we present a method for trace detection of explosives in the gas phase using novel surface enhanced Raman scattering (SERS) spectroscopy substrates. Novel substrates that produce an exceptionally large enhancement of the Raman effect were used to amplify the Raman signal of explosives molecules adsorbed onto the substrate. The substrates were fabricated in a cleanroom process which only requires two steps to produce well controlled nano-sized high aspect ratio metal pillars. These substrates had superior chemical sensing performance in addition to a more cost effective fabrication process compared to existing commercial substrates. Therefore it is believed that these novel substrates will be able to make SERS more applicable in mobile explosives detection systems to be deployed in for example landmine clearance actions.

A Wireless Health Monitoring System based on Android Operating System

We validated a novel mobile detection system to evaluate reactive hyperemia using the radionuclide plethysmography technique. Twenty-six subjects underwent simultaneously radionuclide plethysmography with strain gauge plethysmography. Strain gauge and radionuclide methods showed excellent reproducibility with intraclass correlation coefficients of 0.96 and 0.89 respectively. There was also a good correlation of flows between the two methods during reactive hyperemia (r = 0.87). We conclude that radionuclide plethysmography using this mobile detection system is a non-invasive alternative to assess forearm blood flow and its dynamic variations during reactive hyperemia.

Accurately identifying shielded gamma-ray sources remains a challenge to spectroscopic identification algorithms. In this paper, a data-driven approach for modeling spectral contributions from shielded sources using Non-negative Matrix Factorization (NMF) is examined. When performed on gamma-ray spectra, NMF yields spectral basis vectors that can be used to approximate various gamma-ray sources. This approach for generating source representations using NMF allows for more accurate estimates of source contributions in gamma-ray spectra in the presence of source shielding, ultimately resulting in enhanced identification capabilities for shielded sources over methods that do not account for the effects of shielding. Likelihood ratio tests are performed using NMF-based spectral models for performing simultaneous detection and identification, and this paper introduces a method for analytically computing thresholds used in hypothesis testing. Performance of this method is characterized by injecting simulated source gamma-ray spectra into background spectra measured from a mobile detection system with six 2" × 4" × 16" NaI(Tl) scintillation detectors. Specifically, background spectra injected with 60Co and 137Cs shielded by 50 mm of steel show an improvement in detection performance over an NMF-based template matching identification approach that does not account for spectral variation due to source attenuation. The methods discussed here are general enough to be broadly applicable to gamma-ray spectroscopic analysis, and can be used in applications relying on template matching.

Currently the android platform is that the quickest growing hand-held OS package. And it’s become the foremost desirable and viable objective of malevolent applications. Android malware growth has been increasing dramatically in conjunction with increasing the variety and guiltiness of their developing techniques. Mobile malware is therefore pernicious and, on the increase, consequently having a quick and reliable detection system is important for the users. Subtle Android malware use detection shunning techniques so as to cover their malicious activities from analysis tools. During this analysis, a brand-new detection and characterization system for investigation significant deviations within the network behaviour of a smart-phone application is planned. The most goal of the planned system is to guard mobile device users and cellular infrastructure corporations from malicious applications simply nine traffic feature measurements. The planned system isn’t solely ready to observe the malicious or masquerading apps, however may also determine them as general malware or specific malware (i.e. adware) on a mobile device. The planned methodology showed the common accuracy 94% for 5 classifiers namely; Random Forest (RF), K-Nearest Neighbour (KNN), decision Tree (DT), Random Tree (RT) and Regression (R) etc. We have a tendency to conjointly provide a tagged dataset of mobile malware traffic with a lot of applications includes benign and twelve completely different families of each adware and general malware. Recent substantial analysis on machine learning algorithms analyse options from mischievous application and use those preferences to catalogue and discover unknown malicious applications. This study condenses the progression of malware detection techniques supported machine learning algorithms centred on the Android Operating systems.

The Android operating system, dominating over 85% of the mobile market through open-source flexibility, suffers from intrinsic vulnerabilities. The APK(Android Package Kit) parsability and Smali code modifiability enable attackers to decompile applications via tools. This facilitates widespread repackaging-malicious actors inject payloads or tamper with functionality, redistributing counterfeit applications(apps) through third-party markets. These practices cause dual damage. Developers face code theft and revenue diversion, while users endure privacy leaks, financial fraud, and device compromise. Consequently, accurate repackaging detection has become critical. This paper reviews recent progress in repackaging detection techniques for Android applications. We first outline the fundamental characteristics of Android apps and then examine detection methods based on code analysis and resource similarity. Frequently used Android app datasets and evaluation metrics for measuring the effectiveness of repackaging detection methods are also summarized. Finally, we discuss the development trends of repackaging detection techniques and identify future research directions, with the aim of providing meaningful insights and guidance for researchers in this domain.

A vehicle-mounted mobile mid-infrared dual-gas sensor system for simultaneous detection of methane (CH₄) and ethane (C₂H₆) was developed using two interband cascade lasers (ICLs) for targeting the absorption line of CH₄ at 3038.5 cm^-1 and the line of C₂H₆ at 2996.88 cm^-1, respectively. Two multi-pass gas cells (MPGCs) with an absorption path length of 26.4 m were utilized. The detection limit (1σ) of CH₄ using wavelength modulation spectroscopy (WMS) is ~ 4.1 parts-per-billion by volume (ppbv) with a 0.5 s averaging time, and the detection limit (1σ) of C₂H₆ is ~ 2.9 ppbv with a 0.5 s averaging time. Vehicle-mounted mobile measurement ability of the dual-gas sensor was enabled by a remote monitoring system based on an embedded Linux processor and a cloud server. Atmospheric CH₄ and C₂H₆ monitoring was performed for 48 hours on the Jilin University campus to evaluate the performance of the sensor system. Vehicle-mounted mobile campaigns were carried out both on the Jilin University campus and in the Changchun Area for dual-gas concentration mapping. The sensor system shows the merits of remote monitoring and mobile gas sensing ability in field applications.

Scutellariabaicalensis Georgi, an important perennial herb, is widely distributed and used all over the world. The root of S. baicalensis (Radix Scutellaria) is rich in flavonoids with a variety of bioactive effects and is widely used in clinic. The different geographical and climatic conditions of different cultivated areas of S. baicalensis lead to the differences of the main components in Radix Scutellaria. The main objective of this study was to evaluate the difference of flavonoid content in Radix Scutellaria from different cultivated areas in China. The mobile phase system, elution gradient, detection wavelength, and other chromatographic conditions for high-performance liquid chromatography-diode array detection (HPLC-DAD) determination of 8 flavonoids in Radix Scutellaria were optimized. The contents of flavonoids in 38 samples of Radix Scutellaria collected from seven main genuine cultivated areas were determined, and the correlation between the content, cultivated area, and the biological activities of Radix Scutellaria was compared. The results implied that baicalin, wogonoside, and baicalein were the three main flavonoids with the highest contents in Radix Scutellaria. The content of flavonoids in different cultivated areas was very different, which had significant regionality and was closely related to the natural conditions of various places. The antioxidant and antitumor activities of the extract of Radix Scutellaria were closely related to the content of flavonoids, and high contents of baicalin, wogonoside, and baicalein positively improved biological activities.