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AI is the new battleground, according to a report released by SophosLabs this week. The 2020 Threat Report highlights a growing battle between cybercriminals and security companies as smart automation technologies continue to evolve.

Security companies are using machine learning technology to spot everything from malware to phishing email, but data scientists are figuring out ways to game the system. According to the report, researchers are conceiving new attacks to thwart the AI models used to protect modern networks… attacks which are starting to move from the academic space into attackers’ toolkits.

One such approach involves adapting malware and emails with extra data that make them seem benign to machine learning systems. Another replicates the training models that security companies use to create their AI algorithms, using them to better understand the kinds of properties that the machine learning models target. That lets attackers tailor malicious files to bypass AI protections.

The other big AI-related worry is generative AI, which uses neural networks to create realistic human artefacts like pictures, voices, and text. Also known as deepfakes, these are likely to improve and present more problems to humans who can’t tell the difference. Sophos predicts that in the coming years, we’ll see deepfakes lead to more automated social engineering attacks – a phenomenon that it calls ‘wetware’ attacks.

Automation is already a growing part of the attack landscape, warns the threat report. Attackers are exploiting automated tools to evade detection, it says, citing ‘living off the land’ as a particular threat. This sees attackers using common legitimate tools ranging from the nmap network scanning product to Microsoft’s PowerShell in their quest to move laterally through victims’ networks, escalating their privileges and stealing data under the radar.

Online criminals are also tying up admin resources with decoy malware, which they can drop liberally throughout a victim’s infrastructure, the report warns. This malware carries benign payloads, enabling them to misdirect admins while they furtively drop the real payloads.

The third weapon in the attackers’ automated arsenal are potentially unwanted applications (PUAs). Unlike the benign malware decoys, PUAs often don’t garner much attention because they aren’t classified as malware. Yet attackers can still program them to activate automatically and deliver damaging payloads at a time of their choosing, Sophos warns.

Automated attacks also pose a threat to machines exposing specific ports online, the report points out. By way of example, Sophos uses computers with public-facing remote desktop protocol (RDP) ports, which are common targets for brute-force password attacks. This is just one example of what the company calls ‘internet background radiation’ – the constant hubbub of online activity that contains an ocean of malicious traffic.

But while AI is the threat of tomorrow and automated technologies present real and present dangers, we shouldn’t ignore infections from the past. Malware that swept the internet years ago still highlights inherent insecurities across large swathes of online infrastructure. The report singles out ‘zombie’ WannaCry infections that are still lurking on many networks. These infections, based on variants of the original malware, show that there are still vast quantities of unpatched machines online.

The same goes for Mirai, the IoT-based botnet that swept the world in 2016 and still exists today. SophosLabs has seen Mirai-infected networks launching attacks on database servers using sophisticated strings of commands that can take over an entire system.

The report highlighted plenty of other threats, including a growing diversity of attacks on smartphone owners. Attackers are resorting to everything from SIMjacking to adware and ‘fleecing’ apps that charge exorbitant amounts for legitimate assets of very little value.

As technology evolves at a breakneck pace, one thing is certain: the creativity of the cybercrime community will continue to evolve with it. However, while companies may fret over tomorrow’s technologically sophisticated threats, the first place to begin any cybersecurity effort is with basic steps such as software patching, strict access policies, proper system and network monitoring, and user education. Measures like these don’t need sophisticated AI practitioners to implement, and they can save many headaches down the line.

Google has released a data set of thousands of deepfake videos that it produced using paid, consenting actors in order to help researchers in the ongoing work of coming up with detection methods.

In order for researchers to train and test automated detection tools, they need to feed them a whole lot of deepfakes to scrutinize. Google is helping by making its dataset available to researchers, who can use it to train algorithms that spot deepfakes.

The data set, available on Github, contains more than 3,000 deepfake videos. Google said on its artificial intelligence (AI) blog that the hyperrealistic videos, created in collaboration with its Jigsaw technology incubator, have been incorporated into the Technical University of Munich and the University Federico II of Naples’ new FaceForensics benchmark – an effort that Google co-sponsors.

To produce the videos, Google used 28 actors, placing pairs of them in quotidian settings: hugging, talking, expressing emotion and the like.

To transform their faces, Google used publicly available, state-of-the-art, automatic deepfake algorithms: Deepfakes, Face2Face, FaceSwap and NeuralTextures. You can read more about those algorithms in this white paper from the FaceForensics team. In January 2019, the academic team, led by a researcher from the Technical University of Munich, created another data set of deepfakes, FaceForensics++, by performing those four common face manipulation methods on nearly 1,000 YouTube videos.

Google added to those efforts with another method that does face manipulation using a family of dueling computer programs known as generative adversarial networks (GANs): machine learning systems that pit neural networks against each other in order to generate convincing photos of people who don’t exist. Google also added the Neural Textures image manipulation algorithm to the mix.

Buuz keyboard free download for pc. Yet another data set of deepfakes is in the works, this one from Facebook. Earlier this month, it announced that it was launching a $10m deepfake detection project.

It will, as the name DeepFake Detection Challenge suggests, help people detect deepfakes. Like Google, Facebook’s going to make the data set available to researchers.

An arms race

Return of the highlanders margaret mallory epub file. This is, of course, an ongoing battle. As recently as last month, when we heard about mice being pretty good at detecting deepfake audio, that meant that the critters were close to the median accuracy of 92% for state of the art detection algorithms: algorithms that detect unusual head movements or inconsistent lighting, or, in shoddier deepfakes, which spot subjects who don’t blink. (The US Defense Advanced Research Projects Agency [DARPA] has found that a lack of blinking, at least as of the circa August 2018 state of the technology’s evolution, was a giveaway.)

In spite of the current, fairly high detection rate, we need all the help we can get to withstand the ever more sophisticated fakes that are coming. Deep fake technology is evolving at breakneck speed, and just because detection is fairly reliable now doesn’t mean it’s going to stay that way. Thus was difficult-to-detect impersonation a “significant” topic at this year’s Black Hat and Def Con conferences, as the BBC reported last month.

We’re already seeing GANs reportedly used to create what an AP investigation recently suggested was a deepfake LinkedIn profile of a comely young woman who was suspiciously well-connected to people in power.

Forensic experts easily spotted 30-year-old “Katie Jones” as a deepfake. That was fairly recent: the story was published in June. Then, we got DeepNude, an app that also used GANs and which appeared to have advanced the technology all that much further, plus put it into an app that anybody could use to generate a deepfake within 30 seconds.

This isn’t Google’s first contribution to the field of unmasking fakes: in January, it released a database of synthetic speech to help out with fake audio detection. Google says that it also plans to add to its deepfake dataset as deepfake generation technology evolves:

We firmly believe in supporting a thriving research community around mitigating potential harms from misuses of synthetic media, and today’s release of our deepfake dataset in the FaceForensics benchmark is an important step in that direction.