Android Security: A Survey of Issues, Malware Penetration, and Defenses

Smartphones have become pervasive due to the availability of office applications, Internet, games, vehicle guidance using location-based services apart from conventional services such as voice calls, SMSes, and multimedia services. Android devices have gained huge market share due to the open architecture of Android and the popularity of its application programming interface (APIs) in the developer community. Increased popularity of the Android devices and associated monetary benefits attracted the malware developers, resulting in big rise of the Android malware apps between 2010 and 2014. Academic researchers and commercial antimalware companies have realized that the conventional signature-based and static analysis methods are vulnerable. In particular, the prevalent stealth techniques, such as encryption, code transformation, and environment-aware approaches, are capable of generating variants of known malware. This has led to the use of behavior-, anomaly-, and dynamic-analysis-based methods. Since a single approach may be ineffective against the advanced techniques, multiple complementary approaches can be used in tandem for effective malware detection. The existing reviews extensively cover the smartphone OS security. However, we believe that the security of Android, with particular focus on malware growth, study of antianalysis techniques, and existing detection methodologies, needs an extensive coverage. In this survey, we discuss the Android security enforcement mechanisms, threats to the existing security enforcements and related issues, malware growth timeline between 2010 and 2014, and stealth techniques employed by the malware authors, in addition to the existing detection methods. This review gives an insight into the strengths and shortcomings of the known research methodologies and provides a platform, to the researchers and practitioners, toward proposing the next-generation Android security, analysis, and malware detection techniques

Intelligent OS X malware threat detection with code inspection

With the increasing market share of Mac OS X operating system, there is a corresponding increase in the number of malicious programs (malware) designed to exploit vulnerabilities on Mac OS X platforms. However, existing manual and heuristic OS X malware detection techniques are not capable of coping with such a high rate of malware. While machine learning techniques offer promising results in automated detection of Windows and Android malware, there have been limited efforts in extending them to OS X malware detection. In this paper, we propose a supervised machine learning model. The model applies kernel base Support Vector Machine (SVM) and a novel weighting measure based on application library calls to detect OS X malware. For training and evaluating the model, a dataset with a combination of 152 malware and 450 benign were is created. Using common supervised Machine Learning algorithm on the dataset, we obtain over 91% detection accuracy with 3.9% false alarm rate. We also utilize Synthetic Minority Over-sampling Technique (SMOTE) to create three synthetic datasets with different distributions based on the refined version of collected dataset to investigate impact of different sample sizes on accuracy of malware detection. Using SMOTE datasets we could achieve over 96% detection accuracy and false alarm of less than 4%. All malware classification experiments are tested using cross validation technique. Our results reflect that increasing sample size in synthetic datasets has direct positive effect on detection accuracy while increases false alarm rate in compare to the original dataset

Effect of titanium dioxide (TiO₂) nanoparticle coating on the detection performance of microfiber knot resonator sensors for relative humidity measurement

In this study, the sensitivity and the linearity of the un-coated and TiO2-coated microfiber knot resonator (MKR) have been analyzed. The MKR is very sensitive to humidity changes since its refractive index is strongly humidity dependent. As a result, shifts occur in the resonance wavelength and there are also changes in output power. The un-coated MKR showed a sensitivity of 1.3 pm/%RH, in terms of the resonance wavelength, and a sensitivity of 0.0626 dB/%RH for the transmitted output power. The sensitivity increased greatly after the deposition of a porous TiO2 nanoparticle coating on the MKR. The TiO2-coated MKR showed an improved sensitivity of 2.5 pm/%RH, with respect to the resonance wavelength, and 0.0836 dB/%RH for the transmitted output power. This MKR sensor has the potential for use in a variety of humidity sensing applications

In-situ 3D Micro-sensor Model using Embedded Plasmonic Island for Biosensors

The design of the microsensor system for biosensors using the plasmonic island is proposed. The sensor head is formed by the stacked layers of silicon-graphene-gold materials. The dual-mode operations of the sensor can be performed using the relationship of the changes between the electron mobility and optical phase, where the exciting environment can be light intensity (phase), electrical transient, heat, pressure, flavour and smoke, The change in light phase (intensity) in silicon and conductivity (mobility) in gold layers cause change in the output measurands. The design and simulation interpretation of the sensor is presented. The sensor manipulation using the MCM arrangement is simulated and interpreted for biosensor applications 3D imaging can also be applied to the MCM function, where the 3D in situ sensor function is possible. The sensor sensitivity of 2.0 × 10−21 cm2 V−1 s−1 (mW)−1 via simulation is obtained

ESCAPADE: Encryption-type-ransomeware: system call based pattern detection

Encryption-type ransomware has risen in prominence lately as the go-to malware for threat actors aiming to compromise Android devices. In this paper, we present a ransomware detection technique based on behaviours observed in the system calls performed by the malware. We identify and present some common high-level system call behavioural patterns targeted at encryption-type ransomware and evaluate these patterns. We further present our repeatable and extensible methodology for extracting the system call log and patterns

Syk: a new player in the field of breast cancer

Breast tumor development and progression are thought to occur through a complex, multistep process, including oncogene activation (eg HER2/neu) and mutation or loss of tumor suppressor genes (eg p53). Determining the function of genetic alterations in breast carcinoma tumorigenesis and metastasis has been the focus of intensive research efforts for several decades. One group of proteins that play a critical role in breast cancer cell signaling pathways are tyrosine kinases. Overexpression of the tyrosine kinase HER2/neu is observed in many human breast cancers and is positively correlated with enhanced tumorigenesis [1]. Recently, another tyrosine kinase, Syk, has been implicated as an important inhibitor of breast cancer cell growth and metastasis [2]. This recent finding was unexpected, since Syk function has been predominantly linked to hematopoietic cell signaling, and is discussed further in this commentary

Lung Adenocarcinoma and Squamous Cell Carcinoma Gene Expression Subtypes Demonstrate Significant Differences in Tumor Immune Landscape

INTRODUCTION: Molecular subtyping of lung adenocarcinoma (AD) and lung squamous cell carcinoma (SCC) reveal biologically diverse tumors that vary in their genomic and clinical attributes. METHODS: Published immune cell signatures and several lung AD and SCC gene expression data sets, including The Cancer Genome Atlas, were used to examine immune response in relation to AD and SCC expression subtypes. Expression of immune cell populations and other immune related genes, including CD274 molecule gene (CD274) (programmed death ligand 1), was investigated in the tumor microenvironment relative to the expression subtypes of the AD (terminal respiratory unit, proximal proliferative, and proximal inflammatory) and SCC (primitive, classical, secretory, and basal) subtypes. RESULTS: Lung AD and SCC expression subtypes demonstrated significant differences in tumor immune landscape. The proximal proliferative subtype of AD demonstrated low immune cell expression among ADs whereas the secretory subtype showed elevated immune cell expression among SCCs. Tumor expression subtype was a better predictor of immune cell expression than CD274 (programmed death ligand 1) in SCC tumors but was a comparable predictor in AD tumors. Nonsilent mutation burden was not correlated with immune cell expression across subtypes; however, major histocompatibility complex class II gene expression was highly correlated with immune cell expression. Increased immune and major histocompatibility complex II gene expression was associated with improved survival in the terminal respiratory unit and proximal inflammatory subtypes of AD and in the primitive subtype of SCC. CONCLUSIONS: Molecular expression subtypes of lung AD and SCC demonstrate key and reproducible differences in immune host response. Evaluation of tumor expression subtypes as potential biomarkers for immunotherapy should be investigated