The increasing frequency of mass shootings in the United States has become alarmingly common, prompting discussions about gun control. While gun control in the US involves complex legal issues, cultural factors---particularly ``gun culture''---play a significant but often overlooked role. Although the role of social media in shaping culture is well-documented, the intersection of gun culture and fringe online communities, like 4chan, remains unclear. This gap is particularly concerning given the rise in mass shootings and the online radicalization of some shooters. To address this gap, we explore gun culture on /k/, 4chan's weapons board. More specifically, we employ various NLP techniques to analyze over 4M posts on /k/ and contextualize the discussion within the broader body of theoretical framework of gun culture. Our findings reveal that discussions on /k/ cover a wide array of topics, with a significant focus on law-related discussions---over 17% of gun-related conversations on /k/ revolve around legal matters. Additionally, our analysis uncovers the presence of extreme viewpoints surrounding firearms, often manifesting as gun fetishism. These insights can be valuable for a range of stakeholders including social media platform, in efforts to address content moderation and de-radicalization

The outbreak of the COVID-19 pandemic has changed our lives in unprecedented ways. In the face of the projected catastrophic consequences, most countries have enacted social distancing measures in an attempt to limit the spread of the virus. Under these conditions, the Web has become an indispensable medium for information acquisition, communication, and entertainment. At the same time, unfortunately, the Web is being exploited for the dissemination of potentially harmful and disturbing content, such as the spread of conspiracy theories and hateful speech towards specific ethnic groups, in particular towards Chinese people and people of Asian descent since COVID-19 is believed to have originated from China.

Drivers' identities are essential information that can facilitate a broad range of applications. For example, by understanding who is driving the vehicle when an accident happens, insurance companies could determine the liability and payment in a car accident claim case with high confidence. Another example, pick-up service companies could track the identities of their drivers to ensure that authorized drivers are driving esteemed clients to their destinations. While there are existing studies that can utilize video cameras and dedicated sensors to identify drivers, they either have privacy issues or require additional hardware, which is not practical enough for daily uses. In this paper, we devise a low-cost driver identification system, which can determine drivers' identities by using sensors readily available in wearable devices. Our system captures the unique driving behaviors during pervasive but momentary driving events (i.e., turning at intersections) with motion sensors, which are widely integrated into commodity wearable devices (e.g., smartphones and activity trackers). Toward this end, we extensively analyze people's driving behaviors and identify the critical turning events that capture people's unique behavioral patterns for driver identification. We design a fine-grained turning segmentation method that divides sensor data into critical turning stages (i.e., before, during, and after-turn stages), which provide multiple dimensions of turning behavioral metrics facilitating driver identification. The system extracts unique turning behavior features from time and frequency domains to enable driver identification based on drivers' turning behaviors at different types of turns. Extensive experiments are conducted with 12 drivers and various types of turns in real-road conditions. The results demonstrate that our system can identify drivers with high accuracy and low falsepositive rate based on one single turning event.

Due to the extensive use of smart devices using them to study the driving behaviors has attracted a lot of researchers. This work demonstrates the problem of identifying drivers based on their driving style using smart phones. For this purpose the turns done by the drivers are being studied. Different sensors are embedded in the smart phones which are being used in order to extract some features to distinguish different drivers. Experiments are being done with four drivers and the results show that our system can distinguish them with high accuracy of 92% using only one turn.

Covert channel is one of the most interesting topics in the computer networks security. Covert channel designers are seeking to discover weaknesses in the communications algorithms to use them as the medium for covert transmission. Broadcast nature of wireless channels has provided a favorable environment for the design of the hidden channels. CSMA/CA is used to control channel access in IEEE 802.11 network. Random features of this algorithm can be used to create timing covert channel. The statistical distribution of the free time intervals' duration is used in this paper for covert channel establishment. Hidden messages are sent via manipulating the timing of the overt packets' transmission. Hidden transceiver senses the wireless channel continuously to be adapted to the dynamic network condition and to be less detectable. To increase the covert channel's accuracy, some free interval durations are not used that leads to security degradation. This problem is also covered by a gap covering method that is based on the summation of the channel sensed free time interval distribution and a normal one. Hidden nodes also estimate the number of active nodes and adapt their behavior accordingly to keep their compatibility with the network. The statistical Kolmogorov–Smirnov and regularity tests are used to assess the security of the covert channel. Simulation results show that the proposed covert channel have a high bit rate along with high security. © 2016 The Authors Security and Communication Networks published by John Wiley & Sons Ltd