AndroDex: Android Dex Images of Obfuscated Malware.

With the emergence of technology and the usage of a large number of smart devices, cyber threats are increasing. Therefore, research studies have shifted their attention to detecting Android malware in recent years. As a result, a reliable and large-scale malware dataset is essential to build effective malware classifiers. In this paper, we have created AndroDex: an Android malware dataset containing a total of 24,746 samples that belong to more than 180 malware families. These samples are based on .dex images that truly reflect the characteristics of malware. To construct this dataset, we first downloaded the APKs of the malware, applied obfuscation techniques, and then converted them into images. We believe this dataset will significantly enhance a series of research studies, including Android malware detection and classification, and it will also boost deep learning classification efforts, among others. The main objective of creating images based on the Android dataset is to help other malware researchers better understand how malware works. Additionally, an important result of this study is that most malware nowadays employs obfuscation techniques to hide their malicious activities. However, malware images can overcome such issues. The main limitation of this dataset is that it contains images based on .dex files that are based on static analysis. However, dynamic analysis takes time, therefore, to overcome the issue of time and space this dataset can be used for the initial examination of any .apk files.

Dingoes, companions in life and death: The significance of archaeological canid burial practices in Australia.

The dingo, also known as the Australian native dog, was introduced in the late Holocene. Dingoes were primarily wild animals but a number resided in Aboriginal people's camps. Traditionally, these individuals were taken from wild litters before weaning and raised by Aboriginal people. It is generally believed that these dingoes were not directly provided for, and upon sexual maturity, returned to reproduce in the wild. However, some died while in the company of people and, were buried in occupation sites. This Australian practice parallels the burial of domestic dogs in many regions of the Asia-Pacific and beyond but has attracted very little research. We explore the historical and archaeological evidence for dingo burial, examining its different forms, chronological and geographic distribution, and cultural significance. Dingoes were usually buried in the same manner as Aboriginal community members and often in areas used for human burial, sometimes alongside people. This practice probably occurred from the time of their introduction until soon after European colonisation. We present a case study of dingo burials from Curracurrang Rockshelter (NSW) which provides insights into the lives of ancient tame dingoes, and suggests that domestication and genetic continuity between successive camp-dwelling generations may have occurred prior to European contact.

Game-Theoretic Decision Support for Cyber Forensic Investigations.

The use of anti-forensic techniques is a very common practice that stealthy adversaries may deploy to minimise their traces and make the investigation of an incident harder by evading detection and attribution. In this paper, we study the interaction between a cyber forensic Investigator and a strategic Attacker using a game-theoretic framework. This is based on a Bayesian game of incomplete information played on a multi-host cyber forensics investigation graph of actions traversed by both players. The edges of the graph represent players' actions across different hosts in a network. In alignment with the concept of Bayesian games, we define two Attacker types to represent their ability of deploying anti-forensic techniques to conceal their activities. In this way, our model allows the Investigator to identify the optimal investigating policy taking into consideration the cost and impact of the available actions, while coping with the uncertainty of the Attacker's type and strategic decisions. To evaluate our model, we construct a realistic case study based on threat reports and data extracted from the MITRE ATT&CK STIX repository, Common Vulnerability Scoring System (CVSS), and interviews with cyber-security practitioners. We use the case study to compare the performance of the proposed method against two other investigative methods and three different types of Attackers.

Emotional Reactions to Cybersecurity Breach Situations: Scenario-Based Survey Study.

BACKGROUND: With the ever-expanding interconnectedness of the internet and especially with the recent development of the Internet of Things, people are increasingly at risk for cybersecurity breaches that can have far-reaching consequences for their personal and professional lives, with psychological and mental health ramifications. OBJECTIVE: We aimed to identify the dimensional structure of emotion processes triggered by one of the most emblematic scenarios of cybersecurity breach, the hacking of one's smart security camera, and explore which personality characteristics systematically relate to these emotion dimensions. METHODS: A total of 902 participants from the United Kingdom and the Netherlands reported their emotion processes triggered by a cybersecurity breach scenario. Moreover, they reported on their Big Five personality traits, as well as on key indicators for resilient, overcontrolling (internalizing problems), and undercontrolling (aggression) personality types. RESULTS: Principal component analyses revealed a clear 3-dimensional structure of emotion processes: emotional intensity, proactive versus fight/flight reactions, and affective versus cognitive/motivational reactions. Regression analyses revealed that more internalizing problems (ß=.33, P<.001), resilience (ß=.22, P<.001), and agreeableness (ß=.12, P<.001) and less emotional stability (ß=-.25, P<.001) have significant predictive value for higher emotional intensity. More internalizing problems (ß=.26, P<.001), aggression (ß=.25, P<.001), and extraversion (ß=.07, P=.01) and less resilience (ß=-.19, P<.001), agreeableness (ß=-.34, P<.001), consciousness (ß=-.19, P<.001), and openness (ß=-.22, P<.001) have significant predictive value for comparatively more fight/flight than proactive reactions. Less internalizing problems (ß=-.32, P<.001) and more emotional stability (ß=.14, P<.001) and aggression (ß=.13, P<.001) have significant predictive value for a comparatively higher salience for cognitive/motivational than affective reactions. CONCLUSIONS: To adequately describe the emotion processes triggered by a cybersecurity breach, two more dimensions are needed over and above the general negative affectivity dimension. This multidimensional structure is further supported by the differential relationships of the emotion dimensions with personality characteristics. The discovered emotion structure could be used for consistent predictions about who is at risk to develop long-term mental well-being issues due to a cybersecurity breach experience.

A Secure Occupational Therapy Framework for Monitoring Cancer Patients' Quality of Life.

Once diagnosed with cancer, a patient goes through a series of diagnosis and tests, which are referred to as "after cancer treatment". Due to the nature of the treatment and side effects, maintaining quality of life (QoL) in the home environment is a challenging task. Sometimes, a cancer patient's situation changes abruptly as the functionality of certain organs deteriorates, which affects their QoL. One way of knowing the physiological functional status of a cancer patient is to design an occupational therapy. In this paper, we propose a blockchain and off-chain-based framework, which will allow multiple medical and ambient intelligent Internet of Things sensors to capture the QoL information from one's home environment and securely share it with their community of interest. Using our proposed framework, both transactional records and multimedia big data can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient's data available from an in-home secure monitoring system for a better understanding of a patient's current or historical states. Finally, we will present our current implementation status, which provides significant encouragement for further development.

Spreading of computer viruses on time-varying networks.

Social networks are the prime channel for the spreading of computer viruses. Yet the study of their propagation neglects the temporal nature of social interactions and the heterogeneity of users' susceptibility. Here, we introduce a theoretical framework that captures both properties. We study two realistic types of viruses propagating on temporal networks featuring Q categories of susceptibility and derive analytically the invasion threshold. We found that the temporal coupling of categories might increase the fragility of the system to cyber threats. Our results show that networks' dynamics and their interplay with users' features are crucial for the spreading of computer viruses.

Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons.

Activity recognition in indoor spaces benefits context awareness and improves the efficiency of applications related to personalised health monitoring, building energy management, security and safety. The majority of activity recognition frameworks, however, employ a network of specialised building sensors or a network of body-worn sensors. As this approach suffers with respect to practicality, we propose the use of commercial off-the-shelf devices. In this work, we design and evaluate an activity recognition system composed of a smart watch, which is enhanced with location information coming from Bluetooth Low Energy (BLE) beacons. We evaluate the performance of this approach for a variety of activities performed in an indoor laboratory environment, using four supervised machine learning algorithms. Our experimental results indicate that our location-enhanced activity recognition system is able to reach a classification accuracy ranging from 92% to 100%, while without location information classification accuracy it can drop to as low as 50% in some cases, depending on the window size chosen for data segmentation.