Covert channels can be used to circumvent system and network policies by establishing communications that have not been considered in the design of the computing system. We construct a covert channel between different computing systems that utilizes audio modulation demodulation to exchange data between the computing systems over the air medium. The underlying network stack is based on a communication system that was originally designed for robust underwater communication. We adapt the communication system to implement covert and stealthy communications by utilizing the ultrasonic frequency range. We further demonstrate how the scenario of covert acoustical communication over the air medium can be extended to multi-hop communications and even to wireless mesh networks. A covert acoustical mesh network can be conceived as a meshed botnet or malnet that is accessible via inaudible audio transmissions. Different applications of covert acoustical mesh networks are presented, including the use for remote keylogging over multiple hops. It is shown that the concept of a covert acoustical mesh network renders many conventional security concepts useless, as acoustical communications are usually not considered. Finally, countermeasures against covert acoustical mesh networks are discussed, including the use of lowpass filtering in computing systems and a host-based intrusion detection system for analyzing audio input and output in order to detect any irregularities.
As @cite_0 pointed out, mobile cyber-physical system applications might be present on common sensory devices (e.g., on smartphones). Malware infected devices might be connected to form a botnet (as described by Dagon, Gu and Lee @cite_18 ). With smartphones and similar devices, such a botnet might be built upon one of many network interfaces present in such devices and even on input and output sensors as we describe in this article, although we do not specifically experiment with smartphones and focus at interconnecting laptops. A near-field communication botnet approach based on bluetooth is presented by Singh, Jain, Traynor and Lee @cite_9 . We, instead, use audio communications for our botnet-like approach in near-field communications to construct a covert acoustical mesh network consisting of malware infected drones.
@cite_0 @cite_9 @cite_18
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Malware targeting mobile phones is being studied with increasing interest by the research community. While such attention has previously focused on viruses and worms, many of which use near-field communications in order to propagate, none have investigated whether more complex malware such as bot-nets can effectively operate in this environment. In this paper, we investigate the challenges of constructing and maintaining mobile phone-based botnets communicating nearly exclusively via Bluetooth. Through extensive large-scale simulation based on publicly available Bluetooth traces, we demonstrate that such a malicious infrastructure is possible in many areas due to the largely repetitive nature of human daily routines. In particular, we demonstrate that command and control messages can propagate to approximately 2 3 of infected nodes within 24 hours of being issued by the botmaster. We then explore how traditional defense mechanisms can be modified to take advantage of the same information to more effectively mitigate such systems. In so doing, we demonstrate that mobile phone-based botnets are a realistic threat and that defensive strategies should be modified to consider them. The proliferation of mobile computing devices has enabled immense opportunities for everyday users. At the same time, however, this has opened up new, and perhaps more severe, possibilities for attacks. In this paper, we explore a novel generation of mobile malware that exploits the rich variety of sensors available on current mobile devices. Two properties distinguish the proposed malware from the existing state-of-the-art. First, in addition to the misuse of the various traditional services available on modern mobile devices, this malware can be used for the purpose of targeted context-aware attacks. Second, this malware can be commanded and controlled over context-aware, out-of-band channels as opposed to a centralized infrastructure. These communication channels can be used to quickly reach out to a large number of infected devices, while offering a high degree of undetectability. In particular, unlike traditional network-based communication, the proposed sensing-enabled channels cannot be detected by monitoring the cellular or wireless communication networks. To demonstrate the feasibility of our proposed attack, we present different flavors of command and control channels based on acoustic, visual, magnetic and vibrational signaling. We further build and test a proof-of-concept Android application implementing many such channels. Botnets are now recognized as one of the most serious security threats. In contrast to previous malware, botnets have the characteristic of a command and control (C&C) channel. Botnets also often use existing common protocols, e.g., IRC, HTTP, and in protocol-conforming manners. This makes the detection of botnet C&C a challenging problem. In this paper, we propose an approach that uses network-based anomaly detection to identify botnet C&C channels in a local area network without any prior knowledge of signatures or C&C server addresses. This detection approach can identify both the C&C servers and infected hosts in the network. Our approach is based on the observation that, because of the pre-programmed activities related to C&C, bots within the same botnet will likely demonstrate spatial-temporal correlation and similarity. For example, they engage in coordinated communication, propagation, and attack and fraudulent activities. Our prototype system, BotSniffer, can capture this spatial-temporal correlation in network traffic and utilize statistical algorithms to detect botnets with theoretical bounds on the false positive and false negative rates. We evaluated BotSniffer using many real-world network traces. The results show that BotSniffer can detect real-world botnets with high accuracy and has a very low false positive rate. Mobile robots and autonomous vehicles rely on multi-modal sensor setups to perceive and understand their surroundings. Aside from cameras, LiDAR sensors represent a central component of state-of-the-art perception systems. In addition to accurate spatial perception, a comprehensive semantic understanding of the environment is essential for efficient and safe operation. In this paper we present a novel deep neural network architecture called LiLaNet for point-wise, multi-class semantic labeling of semi-dense LiDAR data. The network utilizes virtual image projections of the 3D point clouds for efficient inference. Further, we propose an automated process for large-scale cross-modal training data generation called Autolabeling, in order to boost semantic labeling performance while keeping the manual annotation effort low. The effectiveness of the proposed network architecture as well as the automated data generation process is demonstrated on a manually annotated ground truth dataset. LiLaNet is shown to significantly outperform current state-of-the-art CNN architectures for LiDAR data. Applying our automatically generated large-scale training data yields a boost of up to 14 percentage points compared to networks trained on manually annotated data only.