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In today's article I'm going to explain all about Intrusion detection system in cyber security, confusion matrix, how it is used in IDS, how it is impacting in cyber security with example .So let's get started to this amazing topic. In today's technological world where everything is going to digitalized everything is online now. Along with this the most important thing is data and data security. All activities we do on internet, what we searched,what we post, what we buy, which site we visited all this data is stored in datacenters servers. This all data must be secured from hackers and any kind of data loss.
This article is part of "The age of surveillance," a special report on artificial intelligence. Lawmakers are still figuring out how best to use artificial intelligence. Lawbreakers are doing the same. The malicious use of artificial intelligence is growing. Officials are warning against attacks that use deepfake technology, AI-enhanced "phishing" campaigns and software that guesses passwords based on big data analysis.
Soon, your driver's license may not be enough to get you through airport security in the United States. Oct. 1, 2020 is the deadline for U.S. citizens to have REAL ID-compliant state driver's licenses, a requirement passed by Congress in 2005 in the wake of the Sept. 11, 2001, terrorist attacks. Without a compliant driver's license, those who are 18 and over won't be able to board a domestic flight, unless possessing other specific forms of acceptable identification. The thought behind this was that with standardization, it will become a lot harder to forge documents and gain access to aircraft. While the main idea of REAL ID is to better protect U.S. citizens and their identity, there is controversy over the law.
Two months back, a group of hackers hijacked the facial recognition system by the Chinese government to send fake tax invoices. According to the South China Morning Post report, "Prosecutors in Shanghai said a criminal group duped that platform's identity verification system by using manipulated personal information and high-definition photographs, which were bought from an online black market, so its registered shell company can issue fake tax invoices to clients." The wide availability of image manipulation apps and AI technology has made it possible to successfully exploit and manipulate biometrics to commit frauds. Biometrics is considered one of the best tools to ensure security and detect cybercrimes. The potential of biometrics in authenticating and reducing fraud is imperative and thus it is being widely used in the form of fingerprints, facial recognition, voice recognition, etc.
As the marketing of almost every advanced cybersecurity product will tell you, artificial intelligence is already being used in many products and services that secure computing infrastructure. But you probably haven't heard much about the need to secure the machine learning applications that are becoming increasingly widespread in the services you use day-to-day. Whether we recognize it or not, AI applications are already shaping our consciousness. Machine learning-based recommendation mechanisms on platforms like YouTube, Facebook, TikTok, Netflix, Twitter, and Spotify are designed to keep users hooked to their platforms and engaged with content and ads. These systems are also vulnerable to abuse via attacks known as data poisoning.
The head of the Australian Security Intelligence Organisation (ASIO), Mike Burgess, has lashed out at tech giants for running interference and handing a free pass to Australia's adversaries and "some of the worst people in our society". "Through the use of encryption social media and tech companies are, in effect, creating a maintaining a safe space for terrorists and spies," Burgess told Senate Estimates on Tuesday. "It's extraordinary how corporations that suck up and sell vast amounts of personal data without a warrant or meaningful oversight can cite a right to privacy to impede a counterterrorism investigation by an agency operating with a warrant or rigorous oversight." Unlike his counterparts at the Australian Criminal Intelligence Commission, Burgess did not go so far as to rule out all legitimate reasons for using encryption. "Encryption is a fundamental force for good as a society, we need to be able to shop, bank, and communicate online with confidence. But even a force for good can be hijacked exploited and abused," the director-general said.
The state of the art AI-driven surveillance technology has given spying powers to every camera. We think of surveillance cameras as highly advanced digital eyes, watching over us, or watching out for us. With the help of AI, these cameras now have brains to complement their eyes. While this is good news for public safety, helping police forces and detectives more easily spot crimes and accidents and have a range of scientific and industrial applications, conversely its invasion of privacy. Is there a way we can trick these surveillance algorithms and become "invisible"?
"I have nothing to hide" was once the standard response to surveillance programs utilizing cameras, border checks, and casual questioning by law enforcement. Privacy used to be considered a concept generally respected in many countries with a few changes to rules and regulations here and there often made only in the name of the common good. Things have changed, and not for the better. China's Great Firewall, the UK's Snooper's Charter, the US' mass surveillance and bulk data collection -- compliments of the National Security Agency (NSA) and Edward Snowden's whistleblowing -- Russia's insidious election meddling, and countless censorship and communication blackout schemes across the Middle East are all contributing to a global surveillance state in which privacy is a luxury of the few and not a right of the many. As surveillance becomes a common factor of our daily lives, privacy is in danger of no longer being considered an intrinsic right. Everything from our web browsing to mobile devices and the Internet of Things (IoT) products installed in our homes have the potential to erode our privacy and personal security, and you cannot depend on vendors or ever-changing surveillance rules to keep them intact. Having "nothing to hide" doesn't cut it anymore. We must all do whatever we can to safeguard our personal privacy. Taking the steps outlined below can not only give you some sanctuary from spreading surveillance tactics but also help keep you safe from cyberattackers, scam artists, and a new, emerging issue: misinformation. Data is a vague concept and can encompass such a wide range of information that it is worth briefly breaking down different collections before examining how each area is relevant to your privacy and security. A roundup of the best software and apps for Windows and Mac computers, as well as iOS and Android devices, to keep yourself safe from malware and viruses. Known as PII, this can include your name, physical home address, email address, telephone numbers, date of birth, marital status, Social Security numbers (US)/National Insurance numbers (UK), and other information relating to your medical status, family members, employment, and education. All this data, whether lost in different data breaches or stolen piecemeal through phishing campaigns, can provide attackers with enough information to conduct identity theft, take out loans using your name, and potentially compromise online accounts that rely on security questions being answered correctly. In the wrong hands, this information can also prove to be a gold mine for advertisers lacking a moral backbone.
This paper presents a new network intrusion detection system (NIDS) based on Graph Neural Networks (GNNs). GNNs are a relatively new sub-field of deep neural networks, which have the unique ability to leverage the inherent structure of graph-based data. Training and evaluation data for NIDSs are typically represented as flow records, which can naturally be represented in a graph format. This establishes the potential and motivation for exploring GNNs for the purpose of network intrusion detection, which is the focus of this paper. E-GraphSAGE, our proposed new approach is based on the established GraphSAGE model, but provides the necessary modifications in order to support edge features for edge classification, and hence the classification of network flows into benign and attack classes. An extensive experimental evaluation based on six recent NIDS benchmark datasets shows the excellent performance of our E-GraphSAGE based NIDS in comparison with the state-of-the-art.