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The majority of Android smartphone apps are free. When an application is used, advertisements are displayed in order to generate revenue. Adware-related advertising fraud costs billions of dollars each year. Adware is a form of advertising-supported software, that turns into malware when it automatically installs additional malware and adware on an infected device, steals user data, and exposes other vulnerabilities. Better techniques for detecting adware are needed due to the evolution of increasingly sophisticated evasive malware, particularly adware. Even though significant work has been done in the area of malware detection, the adware family has received very little attention. This paper presents a deep learning-based scheme called MadDroid to detect malicious Android adware based on static features. Moreover, this paper delivers a novel dataset that consists of malicious Adware and benign applications and an optimised Convolutional neural network (CNN) for detecting Adware infected by malware based on the permissions of the applications. The results indicate an average classification rate that is higher than previous work for individual adware family classification in terms of well-known evaluation metrics. [ABSTRACT FROM AUTHOR]

With the continued growth and popularity of blockchain-based cryptocurrencies there is a parallel growth in illegal mining to earn cryptocurrency. Since mining for cryptocurrencies requires high computational resource; malicious actors have resorted to using malicious file downloads and other methods to illegally use a victim's system to mine for cryptocurrency without them knowing. This process is known as host-based cryptojacking and is gradually becoming one of the most popular cyberthreats in recent years. There are some proposed traditional machine learning methods to detect host-based cryptojacking but only a few have proposed using deep-learning models for detection. This paper presents a novel approach, dubbed CryptoJackingModel. This approach is a deep-learning host-based cryptojacking detection model that will effectively detect evolving host-based cryptojacking techniques and reduce false positives and false negatives. The approach has an overall accuracy of 98% on a dataset of 129,380 samples and a low performance overhead making it highly scalable. This approach will be an improvement of current countermeasures for detecting, mitigating, and preventing cryptojacking. [ABSTRACT FROM AUTHOR]

The majority of Virtual Private Networks (VPNs) fail when it comes to protecting our privacy. If we are using a VPN to protect our online privacy, many of the well-known VPNs are not secure to use. When examined closely, VPNs can appear to be perfect on the surface but still be a complete privacy and security disaster. Some VPNs will steal our bandwidth, infect our computers with malware, install secret tracking libraries on our devices, steal our personal data, and leave our data exposed to third parties. Generally, Android users should be cautious when installing any VPN software on their devices. As a result, it is important to identify malicious VPNs before downloading and installing them on our Android devices. This paper provides an optimised deep learning neural network for identifying fake VPNs, and VPNs infected by malware based on the permissions of the apps, as well as a novel dataset of malicious and benign Android VPNs. Experimental results indicate that our proposed classifier identifies malicious VPNs with high accuracy, while it outperforms other standard classifiers in terms of evaluation metrics such as accuracy, precision, and recall. [ABSTRACT FROM AUTHOR]

Lower vertebrates, including fish, can rapidly alter skin lightness through changes in melanin concentration and melanosomes' mobility according to various factors, which include background color, light intensity, ambient temperature, social context, husbandry practices and acute or chronic stressful stimuli. Within this framework, the determination of skin chromaticity parameters in fish species is estimated either in specific areas using colorimeters or at the whole animal level using image processing and analysis software. Nevertheless, the accurate quantification of melanin content or melanophore coverage in fish skin is quite challenging as a result of the laborious chemical analysis and the typical application of simple optical imaging methods, requiring also to euthanize the fish in order to obtain large skin samples for relevant investigations. Here we present the application of a novel hybrid confocal fluorescence and photoacoustic microscopy prototype for the label-free imaging and quantification of melanin in fish scales samples with high spatial resolution, sensitivity and detection specificity. The hybrid images are automatically processed through optimized algorithms, aiming at the accurate and rapid extraction of various melanin accumulation indices in large datasets (i.e., total melanin content, melanophores' area, density and coverage) corresponding to different fish species and groups. Furthermore, convolutional neural network-based algorithms have been trained using the recorded data towards the classification of different scales' samples with high accuracy. In this context, we demonstrate that the proposed methodology may increase substantially the precision, as well as, simplify and expedite the relevant procedures for the quantification of melanin content in marine organisms. [ABSTRACT FROM AUTHOR]

Android platforms are a popular target for attackers, while many users around the world are victims of Android malwares threatening their private information. Numerous Android anti-malware applications are fake and do not work as advertised because they have been developed either by amateur programmers or by software companies that are not focused on the security aspects of the business. Such applications usually ask for and generally receive non-necessary permissions which at the end collect sensitive information. The rapidly developing fake anti-malware is a serious problem, and there is a need for detection of harmful Android anti-malware. This article delivers a dataset of Android anti-malware, including malicious or benign, and a customized multilayer perceptron neural network that is being used to detect anti-malware based on the permissions of the applications. The results show that the proposed method can detect with very high accuracy fake anti-malware, while it outperforms other standard classifiers in terms of accuracy, precision, and recall. [ABSTRACT FROM AUTHOR]

Lower vertebrates, including fish, can rapidly alter skin lightness through changes in melanin concentration and melanosomes' mobility according to various factors, which include background color, light intensity, ambient temperature, social context, husbandry practices and acute or chronic stressful stimuli. Within this framework, the determination of skin chromaticity parameters in fish species is estimated either in specific areas using colorimeters or at the whole animal level using image processing and analysis software. Nevertheless, the accurate quantification of melanin content or melanophore coverage in fish skin is quite challenging as a result of the laborious chemical analysis and the typical application of simple optical imaging methods, requiring also to euthanize the fish in order to obtain large skin samples for relevant investigations. Here we present the application of a novel hybrid confocal fluorescence and photoacoustic microscopy prototype for the label-free imaging and quantification of melanin in fish scales samples with high spatial resolution, sensitivity and detection specificity. The hybrid images are automatically processed through optimized algorithms, aiming at the accurate and rapid extraction of various melanin accumulation indices in large datasets (i.e., total melanin content, melanophores' area, density and coverage) corresponding to different fish species and groups. Furthermore, convolutional neural network-based algorithms have been trained using the recorded data towards the classification of different scales' samples with high accuracy. In this context, we demonstrate that the proposed methodology may increase substantially the precision, as well as, simplify and expedite the relevant procedures for the quantification of melanin content in marine organisms. [ABSTRACT FROM AUTHOR]

Background: In fish, minimally invasive blood sampling is widely used to monitor physiological stress with blood plasma biomarkers. As fish blood cells are nucleated, they might be a source a potential new markers derived from 'omics technologies. We modified the epiGBS (epiGenotyping By Sequencing) technique to explore changes in genome-wide cytosine methylation in the red blood cells (RBCs) of challenged European sea bass (Dicentrarchus labrax), a species widely studied in both natural and farmed environments. Results: We retrieved 501,108,033 sequencing reads after trimming, with a mean mapping efficiency of 73.0% (unique best hits). Minor changes in RBC methylome appeared to manifest after the challenge test and a familyeffect was detected. Only fifty-seven differentially methylated cytosines (DMCs) close to 51 distinct genes distributed on 17 of 24 linkage groups (LGs) were detected between RBCs of pre- and post-challenge individuals. Thirty-seven of these genes were previously reported as differentially expressed in the brain of zebrafish, most of them involved in stress coping differences. While further investigation remains necessary, few DMC-related genes associated to the Brain Derived Neurotrophic Factor, a protein that favors stress adaptation and fear memory, appear relevant to integrate a centrally produced stress response in RBCs. Conclusion: Our modified epiGBS protocol was powerful to analyze patterns of cytosine methylation in RBCs of D. labrax and to evaluate the impact of a challenge using minimally invasive blood samples. This study is the first approximation to identify epigenetic biomarkers of exposure to stress in fish. [ABSTRACT FROM AUTHOR]

Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks in terms of risk management. The proposed method has been experimentally evaluated and validated, with the results showing that it is both practical and effective. [ABSTRACT FROM AUTHOR]

Personal data have become a central asset for multiple enterprise applications and online services offered by private companies, public organisations or a combination of both. The sensitivity of such data and the continuously growing legislation that accompanies their management dictate the development of methods that allow the development of more secure, trustworthy software systems with focus on privacy protection. The contribution of this paper is the definition of a novel requirements engineering method that supports both early and late requirements specification, giving emphasis on security, privacy and trust. The novelty of our work is that it provides the means for software designers and security experts to analyse the system-to-be from multiple aspects, starting from identifying high-level goals to the definition of business process composition, and elicitation of mechanisms to fortify the system from external threats. The method is supported by two CASE tools. To demonstrate the applicability and usefulness of our work, the paper shows its applications to a real-world case study. [ABSTRACT FROM AUTHOR]

During early animal ontogenesis, a plethora of small non-coding RNAs (sncRNAs) are greatly expressed and have been shown to be involved in several regulatory pathways vital to proper development. The rapid advancements in sequencing and computing methodologies in the last decade have paved the way for the production of sequencing data in a broad range of organisms, including teleost species. Consequently, this has led to the discovery of sncRNAs as well as the potentially novel roles of sncRNA in gene regulation. Among the several classes of sncRNAs, microRNAs (miRNAs) have, in particular, been shown to play a key role in development. The present work aims to identify the miRNAs that play important roles during early European sea bass (Dicentrarchus labrax) development. The European sea bass is a species of high commercial impact in European and especially Mediterranean aquaculture. This study reports, for the first time, the identification and characterization of small RNAs that play a part in the 10 developmental stages (from morula to all fins) of the European sea bass. From 10 developmental stages, more than 135 million reads, generated by next-generation sequencing, were retrieved from publicly available databases as well as newly generated. The analysis resulted in about 2,000 sample grouped reads, and their subsequently annotation revealed that the majority of transcripts belonged to the class of miRNAs followed by small nuclear RNAs and small nucleolar RNAs. The analysis of small RNA expression among the developmental stages under study revealed that miRNAs are active throughout development, with the main activity occurring after the earlier stages (morula and 50% epiboly) and at the later stages (first feeding, flexion, and all fins). Furthermore, investigating miRNAs exclusively expressed in one of the stages unraveled five miRNAs with a higher abundance only in the morula stage (miR-155, miR-430a, d1, d2, and miR-458), indicating possible important key roles of those miRNAs in further embryonic development. An additional target search showed putative miRNA-mRNA interactions with possible direct and indirect regulatory functions of the identified miRNAs. [ABSTRACT FROM AUTHOR]

Socio-economic factors related to management of costal zones and biological parameters related to species-specific requirements set limitations for the rearing location of net-pen sea cages. A cage was designed and installed at 45 m depth based on the REFA Tension Length Cage technology at the cage facility of the Hellenic Centre for Marine Research in Crete. The cage-net module consisted of a net-pen contained within a hexagonal rigid frame to maintain the shape of the net. Vertical beams of the frame glider on six tension legs were placed in a circle. The operation of the cage was evaluated using a group of red porgy ( P agrus pagrus) for a rearing period of 4 months whereas a second group reared in a surface net-pen cage served as control. Growth performance was similar between the experimental groups. The rearing method significantly affected fish skin pigmentation. Individuals reared in the submerged cage had a brighter skin colour and lower skin melatonin content compared with the ones reared in the surface cage. The presented submerged cage may provide a feasible alternative for rearing fish in exposed areas and particularly species requiring specific biological conditions, such as narrow temperature ranges and low light intensity. [ABSTRACT FROM AUTHOR]

Fish display sophisticated skin chromatic properties that are of considerable ecological, physiological and behavioural importance. The aim of study was to investigate the role of important physical parameters (background colour, lighting spectrum, light intensity and water temperature) on skin colour to gain better knowledge of the environmental factors that regulate pigmentation in cultured red porgy, Pagrus pagrus. All tested physical parameters had a significant effect on skin lightness, especially in the dorsal body area; low light intensity, blue spectrum, a water temperature of 19 °C and a white background significantly increased skin lightness. This effect was mediated through changes in melanophore motility and/or skin melanin concentration. None of the examined factors affected skin hue while a significant decrease in skin entire colour index (a combination of hue and chroma) was found only in white background-adapted fish. It is concluded that all tested physical factors are important regulators of skin lightness. [ABSTRACT FROM AUTHOR]