Systems, methods, and devices for determining the location of one or more mobile devices within a vehicle comprising: (a) a controller located within the vehicle and configured to transmit at least two audio signals, a first audio signal directed generally into a driver space within the vehicle and a second audio signal directed generally into a passenger space within the vehicle, and (b) software code stored in memory of the mobile device and having instructions executable by a processor that performs the steps of: (i) detecting the at least two audio signals, (ii) sampling the at least two audio signals for a predetermined period of time; (iii) performing digital signal processing on the sampled at least two audio signals; and (iv) based on the results of the digital signal processing, determining whether the mobile device was located within the driver space of the vehicle during the predetermined period of time.

Driver Identification and Data Collection Systems for Use with Mobile Communication Devices in Vehicles

An important source of cyber-attacks is malware, which proliferates in different forms such as botnets. The botnet malware typically looks for vulnerable devices across the Internet, rather than targeting specific individuals, companies or industries. It attempts to infect as many connected devices as possible, using their resources for automated tasks that may cause significant economic and social harm while being hidden to the user and device. Thus, it becomes very difficult to detect such activity. A considerable amount of research has been conducted to detect and prevent botnet infestation. In this paper, we attempt to create a foundation for an anomaly-based intrusion detection system using a statistical learning method to improve network security and reduce human involvement in botnet detection. We focus on identifying the best features to detect botnet activity within network traffic using a lightweight logistic regression model. The network traffic is processed by Bro, a popular network monitoring framework which provides aggregate statistics about the packets exchanged between a source and destination over a certain time interval. These statistics serve as features to a logistic regression model responsible for classifying malicious and benign traffic. Our model is easy to implement and simple to interpret. We characterized and modeled 8 different botnet families separately and as a mixed dataset. Finally, we measured the performance of our model on multiple parameters using F1 score, accuracy and Area Under Curve (AUC).

Identifying malicious botnet traffic using logistic regression

Many organisms capitalize on their ability to predict the environment to maximize available free energy and reinvest this energy to create new complex structures. This functionality relies on the manipulation of patterns-temporally ordered sequences of data. Here, we propose a framework to describe pattern manipulators-devices that convert thermodynamic work to patterns or vice versa-and use them to build a "pattern engine" that facilitates a thermodynamic cycle of pattern creation and consumption. We show that the least heat dissipation is achieved by the provably simplest devices, the ones that exhibit desired operational behavior while maintaining the least internal memory. We derive the ultimate limits of this heat dissipation and show that it is generally nonzero and connected with the pattern's intrinsic crypticity-a complexity theoretic quantity that captures the puzzling difference between the amount of information the pattern's past behavior reveals about its future and the amount one needs to communicate about this past to optimally predict the future.

/pdf/thermodynamics-of-complexity-and-pattern-manipulation-xooo9ymhqo.pdf

Thermodynamics of complexity and pattern manipulation

1. Introduction. 2. Probability. 3. Combinatorics. 4. Random Variables. 5. Expected Values. 6. Special Distributions. 7. Limit Theorems. Appendix. Bibliography. Answers to Selected Questions. Index.

An introduction to probability and its applications, by Richard J. Larsen and Morris L. Marx. Pp 404. £17·95. 1986. ISBN 0-13-493453-9 (Prentice-Hall)

Botnets are a form of cyber threat responsible for massive Distributed Denial of Service (DDoS) attacks, delivery of malicious payloads like ransomware, and dissemination of spam which might be used for phishing. Botnets are closely associated with the Internet of Things (IoT), particularly IoT devices, which when compromised can become part of a botnet. The incredible increase in IoT devices and the close relationship of botnets to other attacks cause botnets to be a significant source of cyber threat. Because botnets are complex and evolving, their detection and mitigation has remained a challenge. To address that challenge, models have been constructed for simulation and analysis. This paper will examine existing botnet models and their role in improving mitigation.

An Analysis of Botnet Models

We develop a novel approach to detect botnet traffic using hidden Markov models (HMMs). Botnets are becoming a major source of spam, distributed denial-of-service attacks (DDoS) and other cybercrime [21]. Analogs to botnets are likely to become threats to supervisory control and data acquisition (SCADA) and smart grid systems [19]. Malware that spies on and subverts SCADA systems or their SCADA analogs has been found recently [4]. More sabotage and cyberattacks are expected on the energy infrastructure [2], [3]. Attack detection on energy infrastructure is important. In this work, we detect node corruption by the largest botnet in the wild, Zeus. In Zeus, bots are controlled by attackers under a centralized command and control (C&C) infrastructure. We infer hidden Markov models from Zeus botnet traffic timing data. Inter-packet timings are due to botnet behaviors and C&C communication patterns among bots are usually similar. Inferred HMMs detect the botnet communication traffic. Experiment results on real-world traffic data show that this approach accurately differentiates between botnet traffic and normal traffic.

Botnet traffic detection using hidden Markov models

Disclosed are various embodiments for restricting usage of a mobile device when a user is driving a vehicle. In one embodiment, it is determined that a mobile device is in use by a driver of an active vehicle. A functionality of the mobile device is then restricted based at least in part on determining that the mobile device is in use by the driver of the active vehicle. For example, a touch screen of the mobile device may be disabled, and the use of a hands-free interface may be made mandatory.

Restrictions on mobile device usage when driving a vehicle

Network traffic analysis is widely used to infer information from Internet traffic. This is possible even if the traffic is encrypted. Previous work uses traffic characteristics, such as port numbers, packet sizes, and frequency, without looking for more subtle patterns in the network traffic. In this work, we use stochastic grammars, hidden Markov models (HMMs) and probabilistic context-free grammars (PCFGs), as pattern recognition tools for traffic analysis. HMMs are widely used for pattern recognition and detection. We use a HMM inference approach. With inferred HMMs, we use confidence intervals (CI) to detect if a data sequence matches the HMM. To compare HMMs, we define a normalized Markov metric. A statistical test is used to determine model equivalence. Our metric systematically removes the least likely events from both HMMs until the remaining models are statistically equivalent. This defines the distance between models. We extend the use of HMMs to PCFGs, which have more expressive power. We estimate PCFG production probabilities from data. A statistical test is used for detection. We present three applications of HMM and PCFG detection to network traffic analysis. First, we infer the presence of protocol tunneling through Tor (the onion router) anonymization network. The Markov metric quantifies the similarity of network traffic HMMs in Tor to identify the protocol. It also measures communication noise in Tor network. We use HMMs to detect centralized botnet traffic. We infer HMMs from botnet traffic data and detect botnet infections. Experimental results show that HMMs can accurately detect Zeus botnet traffic. To hide their locations better, newer botnets have P2P control structures. Hierarchical P2P botnets contain recursive and hierarchical patterns. We use PCFGs to detect P2P botnet traffic. Experimentation on real-world traffic data shows that PCFGs can accurately differentiate between P2P botnet traffic and normal Internet traffic.

/pdf/network-traffic-analysis-using-stochastic-grammars-4rv2mgc8bp.pdf

Network Traffic Analysis Using Stochastic Grammars

Disclosed are various embodiments for facilitating the encryption of files as well as facilitating requiring a user to employ an authenticator device in order to access a file that is encrypted or otherwise secured. The authenticator device can provide an authenticator code in which a security key used to access a secured file can be embedded. An additional layer of encryption can also be applied in the authenticator code.

Authenticator device facilitating file security

Restricted content of a data file is identified. The restricted content is removed from the data file, and a redacted version of the data file is generated. The restricted content is stored separate from the redacted version of the data file.

Redacting restricted content in files

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