Biblio

Ransomware is one of the fastest growing threats to internet security. New Ransomware attacks happen around the globe, on a weekly basis. These attacks happen to individual users and groups, from almost any type of business. Many of these attacks involve Ransomware as a service, where one attacker creates a template Malware, which can be purchased and modified by other attackers to perform specific actions. The District Ransomware was a less well-known strain. This work focuses on statically and dynamically analyzing the District Ransomware and presenting the results.

Recently, graph-based malware similarity analysis has been widely used in the field of malware detection. However, the wide application of code obfuscation, polymorphism, and deformation changes the structure of malicious code, which brings great challenges to the malware similarity analysis. To solve these problems, in this paper, we present a new approach to malware similarity analysis based on the network control structure graph (NCSG). This method analyzed the behavior of malware by application program interface (API) association and constructed NCSG. The graph could reflect the command-and-control(C&C) logic of malware. Therefore, it can resist the interference of code obfuscation technology. The structural features extracted from NCSG will be used as the basis of similarity analysis for training the detection model. Finally, we tested the dataset constructed from five known malware family samples, and the experimental results showed that the accuracy of this method for malware variation analysis reached 92.75%. In conclusion, the malware similarity analysis based on NCSG has a strong application value for identifying the same family of malware.

Through analysis of sessions in which files were created and downloaded on three Cowrie SSH/Telnet honeypots, we find that IoT botnets are by far the most common source of malware on connected systems with weak credentials. We detail our honeypot configuration and describe a simple method for listing near-identical malicious login sessions using edit distance. A large number of IoT botnets attack our honeypots, but the malicious sessions which download botnet software to the honeypot are almost all nearly identical to one of two common attack patterns. It is apparent that the Mirai worm is still the dominant botnet software, but has been expanded and modified by other hackers. We also find that the same loader devices deploy several different botnet malware strains to the honeypot over the course of a 40 day period, suggesting multiple botnet deployments from the same source. We conclude that Mirai continues to be adapted but can be effectively tracked using medium interaction honeypots such as Cowrie.

Dynamic strain sensing is necessary for high-temperature harsh-environment applications, including powerplants, oil wells, aerospace, and metal manufacturing. Monitoring dynamic strain is important for structural health monitoring and condition-based maintenance in order to guarantee safety, increase process efficiency, and reduce operation and maintenance costs. Sensing in high-temperature (HT), harsh-environments (HE) comes with challenges including mounting and packaging, sensor stability, and data acquisition and processing. Wireless sensor operation at HT is desirable because it reduces the complexity of the sensor connection, increases reliability, and reduces costs. Surface acoustic wave resonators (SAWRs) are compact, can operate wirelessly and battery-free, and have been shown to operate above 1000°C, making them a potential option for HT HE dynamic strain sensing. This paper presents wirelessly interrogated SAWR dynamic strain sensors operating around 288.8MHz at room temperature and tested up to 400°C. The SAWRs were calibrated with a high-temperature wired commercial strain gauge. The sensors were mounted onto a tapered-type Inconel constant stress beam and the assembly was tested inside a box furnace. The SAWR sensitivity to dynamic strain excitation at 25°C, 100°C, and 400°C was .439 μV/με, 0.363μV/με, and .136 μV/με, respectively. The experimental outcomes verified that inductive coupled wirelessly interrogated SAWRs can be successfully used for dynamic strain sensing up to 400°C.

Self-assembled semiconductor quantum dots possess an intrinsic geometric symmetry due to the crystal periodic structure. In order to systematically analyze the symmetric properties of quantum dots' bound states resulting only from geometric confinement, we apply group representation theory. We label each bound state for two kinds of popular quantum dot shapes: pyramid and half ellipsoid with the irreducible representation of the corresponding symmetric groups, i.e., C4v and C2v, respectively. Our study completes all the possible irreducible representation cases of groups C4v and C2v. Using the character theory of point groups, we predict the selection rule for electric dipole induced transitions. We also investigate the impact of quantum dot aspect ratio on the symmetric properties of the state wavefunction. This research provides a solid foundation to continue exploring quantum dot symmetry reduction or broken phenomena because of strain, band-mixing and shape irregularity. The results will benefit the researchers who are interested in quantum dot symmetry related effects such as absorption or emission spectra, or those who are studying quantum dots using analytical or numerical simulation approaches.

Despite the continuous shrinking of the transistor dimensions, advanced modeling tools going beyond the ballistic limit of transport are still critically needed to ensure accurate device investigations. For that purpose we present here a straight-forward approach to include phonon confinement effects into dissipative quantum transport calculations based on the effective mass approximation (EMA) and the k·p method. The idea is to scale the magnitude of the deformation potentials describing the electron-phonon coupling to obtain the same low-field mobility as with full-band simulations and confined phonons. This technique is validated by demonstrating that after adjusting the mobility value of n- and p-type silicon nanowire transistors, the resulting EMA and k·p I-V characteristics agree well with those derived from full-band studies.

Humans can easily find themselves in high cost situations where they must choose between suggestions made by an automated decision aid and a conflicting human decision aid. Previous research indicates that humans often rely on automation or other humans, but not both simultaneously. Expanding on previous work conducted by Lyons and Stokes (2012), the current experiment measures how trust in automated or human decision aids differs along with perceived risk and workload. The simulated task required 126 participants to choose the safest route for a military convoy; they were presented with conflicting information from an automated tool and a human. Results demonstrated that as workload increased, trust in automation decreased. As the perceived risk increased, trust in the human decision aid increased. Individual differences in dispositional trust correlated with an increased trust in both decision aids. These findings can be used to inform training programs for operators who may receive information from human and automated sources. Examples of this context include: air traffic control, aviation, and signals intelligence.

Humans can easily find themselves in high cost situations where they must choose between suggestions made by an automated decision aid and a conflicting human decision aid. Previous research indicates that humans often rely on automation or other humans, but not both simultaneously. Expanding on previous work conducted by Lyons and Stokes (2012), the current experiment measures how trust in automated or human decision aids differs along with perceived risk and workload. The simulated task required 126 participants to choose the safest route for a military convoy; they were presented with conflicting information from an automated tool and a human. Results demonstrated that as workload increased, trust in automation decreased. As the perceived risk increased, trust in the human decision aid increased. Individual differences in dispositional trust correlated with an increased trust in both decision aids. These findings can be used to inform training programs for operators who may receive information from human and automated sources. Examples of this context include: air traffic control, aviation, and signals intelligence.

Resilience in the information sciences is notoriously difficult to define much less to measure. But in mechanical engineering, the resilience of a substance is mathematically well-defined as an area under the stress-strain curve. We combined inspiration from mechanics of materials and axioms from queuing theory in an attempt to define resilience precisely for information systems. We first examine the meaning of resilience in linguistic and engineering terms and then translate these definitions to information sciences. As a general assessment of our approach's fitness, we quantify how resilience may be measured in a simple queuing system. By using a very simple model we allow clear application of established theory while being flexible enough to apply to many other engineering contexts in information science and cyber security. We tested our definitions of resilience via simulation and analysis of networked queuing systems. We conclude with a discussion of the results and make recommendations for future work.