Cyber-attacks are rarely out of the headlines. We know state actors, terrorists, and criminals can leverage cyber-means to target the digital infrastructures of our societies. We have also learned that, insofar as our societies grow dependent on digital technologies, they become more vulnerable to cyber-attacks. There is no shortage of examples, ranging from the 2007 attacks against Estonia digital services and 2008 cyber-attack against a nuclear power plant in Georgia to WannaCry and NotPetya, two ransomware attacks that encrypted data and demanded ransom payments, and the ransomware cyber-attack on the US Colonial Pipeline, a US oil pipeline system that provides fuel to South-eastern States. My work focuses mostly on state vs state cyber-attacks.

Losses to the tune of billions are experienced due to the rise of cyber-attacks in the automotive industry, and they are becoming progressively worse as more auto manufacturers join the autonomy space. Industry experts argue that autonomy is the future of the automotive industry, mainly because driverless cars are safer, more comfortable, and more convenient than regular cars. However, there is a downside to this technology: susceptibility to cyber-attacks. Attacks range from physical to long-range digital attacks. As we know, a new cyber-attack vector is born every time new development occurs in the tech space.

With developing of computation tools in the last years, data analysis methods to find insightful information are becoming more common among industries and researchers. This paper is the first part of the times series analysis of New England electricity price and demand to find anomaly in the data. In this paper time-series stationary criteria to prepare data for further times-series related analysis is investigated. Three main analysis are conducted in this paper, including moving average, moving standard deviation and augmented Dickey-Fuller test. The data used in this paper is New England big data from 9 different operational zones. For each zone, 4 different variables including day-ahead (DA) electricity demand, price and real-time (RT) electricity demand price are considered.

Researchers from the Massachusetts Institute of Technology have created an AI system that can predict a cyberattack before it happens in 85% of incidents. Analyst-driven systems rely on rules created by people and consequently can't detect attacks that don't adhere to those rules, whereas machine-learning systems rely on anomaly detection, which tends to generate false positives that have to be investigated by people.MIT researchers have announced that they've concocted a new artificial intelligence system capable of successfully detecting 85% of cyber-attacks. Part of the challenge of merging human- and computer-based threat detection has been the manual labeling of data for algorithms.The system has been tested on 3.6 billion log lines or pieces of data that reveal major system activities triggered by millions of users over a period of three months. It then reports this activity to a human analyst who can then judge if there's an actual attack.With that feedback, it takes on board whether or not it should be classifying the events as attacks or not, then refines its internal models.According to Engadget, Kaylan Veermachaneni, co-creator of the system, said that one should think of the new system as a virtual analyst. In the near future the industry and federal regulators will need to figure out a balance between the need of cyber security and protecting consumers' privacy. This method often leads to false positives, meaning that humans doubt the reliability of the system and are forced to go back and check all the results anyway.And the more data it analyses, the more accurate it becomes.

MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), together with researchers from security firm PatternEx, has revealed a new AI (Artificial Intelligence) system called AI2, which can detect 85 percent of cyber-attacks, with false positives rates five times smaller than existing solutions. The new system doesn't rely entirely on artificial intelligence (AI), but also on user input, something that researchers call analyst intuition (AI), hence its name of AI2. Researchers said they fed AI2 with over 3.6 billion lines of log files, allowing the system to scan the content with unsupervised machine-learning techniques. At the end of each day, the system presents its findings to a human operator, who then confirms or dismisses security alerts. This human feedback is then incorporated into AI2's learning system and used the next day for analyzing new logs. After their tests had concluded, MIT and PatternEx researchers said AI2 achieved an 85 percent accuracy rate in detecting cyber-attacks, which is 2.92 times better than similar automated cyber-attack detection systems used today.

A new artificial intelligence (AI) system being developed at MIT's Computer Science and Artificial Intelligence Laboratory is being trained by researchers to aid humans in identifying potential cyber-attacks. Typically, when trying to pinpoint possible attacks, analysts are required to sift through massive amounts of data to find abnormalities and discrepancies--a method that is time-consuming and tedious. Anchored on the idea that AI never gets tired, the new computer based method means that humans can identify cyber-attacks more efficiently. AI2 for instance--MIT's new system, which honed its ability to identify threats after reviewing three months worth of log data from an unidentified ecommerce platform--can review millions of log lines every day. Once it spots something suspicious, a human can then take over and promptly check for possible signs of a security breach.