Biblio

Developers of mobile applications rely on the trust of their customers. On the one hand the requirement exists to create feature-rich and secure apps, which adhere to privacy standards to not deliberately disclose user information. On the other hand the development process must be streamlined to reduce costs. Here third-party libraries come into play. Inclusion of many, possibly nested libraries pose security risks, app-creators are often not aware of. This paper presents a way to combine free open-source tools to support developers in checking their application that it does not induce security issues by using third-party libraries. The tools FlowDroid, Frida, and mitm-proxy are used in combination in a simple and viable way to perform checks to identify privacy leaks of third-party apps. Our proposed setup and configuration empowers average app developers to preserve user privacy without being dedicated security experts and without expensive external advice.

Session-based recommendation is the task of predicting user actions during short online sessions. The user is considered to be anonymous in this setting, with no past behavior history available. Predicting anonymous users' next actions and their preferences in the absence of historical user behavior information is valuable from a cybersecurity and aerospace perspective, as cybersecurity measures rely on the prompt classification of novel threats. Our offered solution builds upon the previous representation learning work originating from natural language processing, namely BERT, which stands for Bidirectional Encoder Representations from Transformers (Devlin et al., 2018). In this paper we propose CyberBERT, the first deep session-based recommender system to employ bidirectional transformers to model the intent of anonymous users within a session. The session-based setting lends itself to applications in threat recognition, through monitoring of real-time user behavior using the CyberBERT architecture. We evaluate the efficiency of this dynamic state method using the Windows PE Malware API sequence dataset (Catak and Yazi, 2019), which contains behavior for 7107 API call sequences executed by 8 classes of malware. We compare the proposed CyberBERT solution to two high-performing benchmark algorithms on the malware dataset: LSTM (Long Short-term Memory) and transformer encoder (Vaswani et al., 2017). We also evaluate the method using the YOOCHOOSE 1/64 dataset, which is a session-based recommendation dataset that contains 37,483 items, 719,470 sessions, and 31,637,239 clicks. Our experiments demonstrate the advantage of a bidirectional architecture over the unidirectional approach, as well as the flexibility of the CyberBERT solution in modelling the intent of anonymous users in a session. Our system achieves state-of-the-art measured by F1 score on the Windows PE Malware API sequence dataset, and state-of-the-art for P@20 and MRR@20 on YOOCHOOSE 1/64. As CyberBERT allows for user behavior monitoring in the absence of behavior history, it acts as a robust malware classification system that can recognize threats in aerospace systems, where malicious actors may be interacting with a system for the first time. This work provides the backbone for systems that aim to protect aviation and aerospace applications from prospective third-party applications and malware.

In recent years, one of the important and interesting tasks has become the protection of civilian and military objects from unmanned aerial vehicles (UAVs) carrying a potential threat. To solve this problem, it is required to detect UAVs and activate protective systems. UAVs can be represented as aerodynamic objects of the monoplane or multicopter type with acoustic fingerprints. In this paper we consider algorithm for UAV acoustic detection and recognition system. Preliminary results of analysis of experimental data show effectiveness of proposed approach.

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.

As technology is developing exponentially and the world is moving towards automation, the resources have to be transferred through the internet which requires routers to connect networks and forward bundles (information). Due to the vulnerability of routers the data and resources have been hacked. The vulnerability of routers is due to minimum authentication to the network shared, some technical attacks on routers, leaking of passwords to others, single passwords. Based on the study, the solution is to maximize authentication of the router by embedding an application that monitors the user entry based on MAC address of the device, the password is frequently changed and that encrypted password is sent to a user and notifies the admin about the changes. Thus, these routers provide high-level security to the forward data through the internet.