Each of these programs follow a paradigm of Machine Learning known as Reinforcement Learning. If you've never been exposed to reinforcement learning before, the following is a very straightforward analogy for how it works.
CW Brute Force 0,5.rarl
Reinforcement Learning will learn a mapping of states to the optimal action to perform in that state by exploration, i.e. the agent explores the environment and takes actions based off rewards defined in the environment.
This is because we aren't learning from past experience. We can run this over and over, and it will never optimize. The agent has no memory of which action was best for each state, which is exactly what Reinforcement Learning will do for us.
Alright! We began with understanding Reinforcement Learning with the help of real-world analogies. We then dived into the basics of Reinforcement Learning and framed a Self-driving cab as a Reinforcement Learning problem. We then used OpenAI's Gym in python to provide us with a related environment, where we can develop our agent and evaluate it. Then we observed how terrible our agent was without using any algorithm to play the game, so we went ahead to implement the Q-learning algorithm from scratch. The agent's performance improved significantly after Q-learning. Finally, we discussed better approaches for deciding the hyperparameters for our algorithm.
Q-learning is one of the easiest Reinforcement Learning algorithms. The problem with Q-learning however is, once the number of states in the environment are very high, it becomes difficult to implement them with Q table as the size would become very, very large. State of the art techniques uses Deep neural networks instead of the Q-table (Deep Reinforcement Learning). The neural network takes in state information and actions to the input layer and learns to output the right action over the time. Deep learning techniques (like Convolutional Neural Networks) are also used to interpret the pixels on the screen and extract information out of the game (like scores), and then letting the agent control the game.
We have discussed a lot about Reinforcement Learning and games. But Reinforcement learning is not just limited to games. It is used for managing stock portfolios and finances, for making humanoid robots, for manufacturing and inventory management, to develop general AI agents, which are agents that can perform multiple things with a single algorithm, like the same agent playing multiple Atari games. Open AI also has a platform called universe for measuring and training an AI's general intelligence across myriads of games, websites and other general applications.
Simple hypothetical model of a LIM domain pair (shown in green and purple ribbon diagram) bound to CasSD shown in yellow with its secondary structural elements colored in red. Wavy red lines and red arrows represent PPII helices and β-strands, respectively. Hydrogen bonds between LIM domains and CasSD are depicted in blue dotted lines. Mechanical stretching force is represented by gray arrows.
When a tropical disturbance organizes into a tropical depression, the thunderstorms will begin to line up in spiral bands along the inflowing wind. The winds will begin to increase, and eventually the inner bands will close off into an eyewall, surrounding a central calm area known as the eye. This usually happens around the time wind speeds reach hurricane force. When the hurricane reaches its mature stage, eyewall replacement cycles may begin. Each cycle will be accompanied by fluctuations in the strength of the storm. Peak winds may diminish when a new eyewall replaces the old, but then re-strengthen as the new eyewall becomes established.
The idea here is to spread a layer of sunlight absorbing or reflecting particles (such as micro-encapsulated soot, carbon black, or tiny reflectors) at high altitude around a hurricane. This would prevent solar radiation from reaching the surface and cooling it, while at the same time increase the temperature of the upper atmosphere. Being vertically oriented, tropical cyclones are driven by energy differences between the lower and upper layer of the troposphere. Reducing this difference should reduce the forces behind hurricane winds.
Recently Chenoweth and Landsea (2004), re-discovered that a hurricane struck San Diego, California on October 2, 1858. Unprecedented damage was done in the city and was described as the severest gale ever felt to that date nor has it been matched or exceeded in severity since. The hurricane force winds at San Diego are the first and only documented instance of winds of this strength from a tropical cyclone in the recorded history of the state. While climate records are incomplete, 1858 may have been an El Niño year, which would have allowed the hurricane to maintain intensity as it moved north along warmer than usual waters. Today if a Category 1 hurricane made a direct landfall in either San Diego or Los Angeles, damage from such a storm would likely be few to several hundred million dollars. The re-discovery of this storm is relevant to climate change issues and the insurance/emergency management communities risk assessment of rare and extreme events in the region.
Recent research describes two distinct types of Atlantic climate drivers: 1) Internal variability is caused by natural processes within the atmosphere and ocean climate system. 2) External variability is caused by forces outside of the atmosphere/ocean climate system.
Examples of natural internal forces are oceanic oscillations such as ENSO, meridional overturning circulation, and Saharan dust storms that blow mineral dust over the tropical Atlantic. The effects of the El Nino/Southern Oscillation are discussed in another section in detail.
The main difficulty with fighting the bot activity is that bots are very clever and elusive. Bot attacks use different IPs and user agents, and often the data from attempts aimed at a single site login, or even a single server, is not good enough to determine a brute-forcing bot. We have had brute-force prevention system on each of our servers for a long time, but the new AI is much more efficient as it is able to collect and analyze simultaneously the data from all our servers. Based on the results of the analysis it can also automatically apply actions to stop unwanted bots. There are numerous indicators that our AI monitors in order to detect malicious behaviour patterns and block bad traffic. Some of them are:
When assessing LoRa devices' security, like any other RF technology, we must deal with unknown radio parameters and data/payloads we need to understand to complete our mission. Moreover, understanding these parameters and data may help to find interesting issues to exploit (clear-text communication, weak keys, stack protocol vulnerabilities). In this post, we will briefly present LoRa and its different security modes, and then we will focus on RF techniques to detect, demodulate and decode LoRa signals. Additionally, we will introduce some scripts we have made to decode, generate LoRa PHY and MAC payloads, Bruteforce keys and finally, fuzz some protocol stacks.
In addition to these issues with LoRa version 1.0, we also weak keys AppKey and hardcoded AppSKey and NwkSKey. Indeed, in OTAA it is possible to enumerate weak/default AppKey key on Join-request's MIC field and Join-accept payloads. After recovering the AppKey with a brute-force, an attacker may be allowed to impersonate an end device and eavesdrop on communication if he can intercept the whole Join procedure. In ABP mode, it is even worse, as an attacker retrieving the AppSKey and NwkSKey for devices can eavesdrop on the communication anytime he wants.
Naively brute-forcing this missing SF parameter is a way, among others, but we can think about more innovative moves. Indeed, as seen in the table above, LoRa operates with SF from 7 to 12. So the shorted chirp is SF7 with a data rate of 11 kilobits/s, and 12 the longest with a data rate of 250 bits/s. So by consequences, this will affect on time, and this can be observed if we compare two configurations, one with an SF7 and 125 kHz BW (on the left) and our target (on the right):
In OTAA, the Join procedure could be interesting to capture and brute-force MIC fields on Join-request messages. As we saw earlier, this MIC is generated by the AppKey in version 1.0 of LoRaWAN and the NmkKey for 1.1.
In ABP, bruteforce attacks against NwkSKey and AppSKey in version 1.0 can be performed against encrypted data payloads. However, in 1.1 version, bruteforcing would require more computing, as the MIC is generated by dedicated session keys for integrity, especially if we could not be able to recover known fields of an unencrypted Join-accept. 2ff7e9595c
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