Trend Micro envisages a new era as cybercriminals use machine learning to create advanced threats

Cybersecurity companies use machine learning technology to enhance threat detection capabilities that help fortify organizations’ defense against malware, exploit kits, phishing emails, and even previously unknown threats, Trend Micro revealed. 

The Capgemini Research Institute conducted a study on the usage of machine learning for security and found that of 850 senior executive respondents based in 10 countries, around 20 percent started using the technology before 2019, and about 60 percent will be using it by year’s end.

The use of machine learning in cybersecurity — not to mention in many other fields across various industries — has proven to be beneficial. This technology, however, is also at risk of being used by threat actors. While widespread machine learning weaponization may still be far off, research concerning this area, particularly the use of deepfake technology to extort and misinform, have recently become a topic of interest for the IT community and the general public.

To get a clearer picture of what is possible and what is already a reality with regard to machine learning-powered cyberthreats, research on machine learning-powered malware is still surprisingly scarce, considering that some experts have long considered it as a type of threat that can possess advanced capabilities. In fact, only one PoC of such a threat has been publicized, which was unveiled at Black Hat USA 2018. 

IBM presented a variant named DeepLocker that can deploy untraceable malicious applications within a benign data payload. The malware variant is supported by deep neural networks (DNN) or deep learning, a form of machine learning. The use of DNN disguises the malware’s conditions, which are pieces of information that security solutions need to detect malicious payload.

DeepLocker is designed to hide until it detects a specific victim. In the demonstration, DeepLocker was seen stealthily waiting for a specific action that will trigger its ransomware payload. The action that triggered the payload was the body movement of a targeted victim when he/she directly looked at a laptop webcam, which is operated by a webcam application embedded with malicious code. The application of machine learning in this attack can be considered limited, but it showed how malware variants can be highly evasive and targeted when infused with machine learning.

Experts are increasingly warning the public about deepfake videos, which are fake or altered clips that contain hyperrealistic images. Produced from generative adversarial networks (GANs) that generate new images from existing datasets of images, deepfake videos can challenge people’s perception of realities, confusing our ability to discern what is true from false.

Deepfake technology is mostly used in videos involving pornography, political propaganda, and satire. As for the impact of these videos, a Medium article published in May claimed that there were about 10,000 deepfake videos online, with House Speaker Nancy Pelosi and Hollywood star Scarlett Johansson being two of their popular subjects/victims.

Regarding the use of this technology in profit-driven cybercrime, it can be surmised that deepfake videos may be used to create a new variation of business email compromise (BEC) or CEO fraud. In this variation of the scheme, a deepfake video can be used as one of its social engineering components to further deceive victims.

But the first reported use of deepfake technology in CEO fraud came in the form of audio. In September, a scam involving a deepfake audio was used to trick a U.K.-based executive into wiring US$243,000 to a fraudulently set-up account. The victim company’s insurance firm stated that the voice heard on the phone call was able to imitate not only the voice of the executive being spoofed, but also the tonality, punctuation, and accent of the latter.

Brute force and social engineering methods are old but popular techniques that cybercriminals use to steal passwords and hack user accounts. New ways to do this could be inadvertently aided by user information shared on social media – some still embed publicly shared information into their account passwords. Additionally, machine learning research on password cracking is an area of concern that users and enterprises should closely pay attention to.

Back in 2017, one of the early proofs of machine learning’s susceptibility to abuse was publicized in the form of PassGAN — a program that can generate high-quality password guesses. Using a GAN of two machine learning systems, experts from the Stevens Institute of Technology, New Jersey, USA, were able to use the program to guess more user account passwords than popular password cracking tools HashCat and John the Ripper.

To compare PassGAN with HashCat and John the Ripper, the developers fed their machine learning system more than 32 million passwords collected from the 2010 RockYou data breach, and let it generate millions of new passwords. Subsequently, it attempted to use these passwords to crack a hashed list of passwords taken from the 2016 LinkedIn data breach.

The results came back with PassGAN generating 12 percent of the passwords in the LinkedIn set, while the other tools generated between 6 percent and 23 percent. But when PassGAN and Hashcat were combined, 27 percent  of the passwords from the LinkedIn set were cracked. If cybercriminals are able to devise a similar or enhanced version of this methodology, it could be a potentially reliable way to hijack user accounts.

Adversarial machine learning is a technique that threat actors can use to cause a machine learning model to malfunction. They can do so by crafting adversarial samples, which are modified input fed to the machine learning system to mess up its ability to predict accurately. In essence, this technique — also called an adversarial attack — turns the machine learning system against itself and the organization running it.

This method has been proven capable of causing machine learning models for security to perform poorly, for example, by making them produce higher false positive rates. They can do this by injecting malware samples that are similar to benign files to poison machine learning training sets.

Machine learning models used for security can also be tricked using infected benign Portable Executable (PE) files or a benign source code compiled with malicious code. This technique can make a malware sample appear benign to models, preventing security solutions from accurately detecting it as malicious since its structure is still mostly comprised of the original benign file.

When it comes to dealing with advanced password cracking tools such as the machine learning-powered PassGAN, users and organizations can move towards two-factor authentication schemes to reduce their reliance on passwords. One approach to this is using a one-time password (OTP) — an automatically generated string of characters that authenticates the user for a single login session or transaction.

Meanwhile, technologies are continuously being developed to defend against deepfakes. To detect deepfake videos, experts from projects initiated by the Pentagon and SRI International are feeding samples of real and deepfake videos to computers. This way, computers can be trained to detect fakes. 

To detect deepfake audio, experts are training computers to recognize visual inconsistencies. And as for the platforms where deepfakes can creep in, Facebook, Google, and Amazon, among other organizations, are joining forces to detect them via the DeepFake Detection Challenge (DFDC) — a project that invites people around the world to build technologies that can help detect deepfakes and other forms of manipulated media.

Adversarial attacks, on the other hand, can be prevented by making machine learning systems more robust. This can be done in two steps: First, by spotting potential security holes early on in its design phase and making every parameter accurate, and second, by retraining models via generating adversarial samples and using them to enhance the efficiency of the machine learning system. 

Reducing the attack surface of the machine learning system can also ward off adversarial attacks. Since cybercriminals modify samples in order to probe a machine learning system, cloud-based solutions, such as products with Trend Micro XGen security, can be used to detect and block malicious probing.

Governments and private organizations, particularly cybersecurity companies, should anticipate a new era where cybercriminals use advanced technologies such as machine learning to power their attacks. As they have done in the past, cybercriminals will continue to develop more advanced and new forms of threats to be one step ahead. In this light, technologies for combating these threats should likewise continue to evolve. 

However, while it would be a good choice to implement a tailor-fit technology to detect such threats, a multilayered security defense (one that combines a variety of technologies) and the consistent application of cybersecurity best practices are still the most effective ways to defend against a wide range of threats.

TigerGraph Cloud improves graph database-as-a-service with improved performance and productivity

TigerGraph announced new functionality and performance for TigerGraph Cloud, its distributed native graph database-as-a-service, is an intuitive way to build and run applications that work with highly connected and complex datasets. 

TigerGraph’s latest distributed system and high availability enhancements help enterprises leverage advanced analytics on graph at scale, which requires larger and more varied dataset combinations, which means more variables and relationships to analyze, explore and test to make machine learning (ML) and artificial intelligence (AI) better.

“To survive and thrive in today’s business world, enterprises should accelerate their approach to AI and ML applications, both of which benefit from graph analytics, as this helps identify new patterns across the data sources, create new models and better algorithms, using data at scale,” said Todd Blaschka, COO, TigerGraph. “TigerGraph Cloud’s enhanced services provide our customers with an even better platform for analytical and transactional processing. Users can start for free and expand to a distributed production system as their applications scale. Starting a distributed graph system with high availability is simply choosing the number of machines and the number of replicas on TigerGraph Cloud.”

  

TigerGraph Cloud delivers on one promise: graph analytics is the way forward. Until now, organizations had to rely on data scientists, developers and architects to design their graph-based data analysis solutions. TigerGraph Cloud has addressed this innovation gap with an easy-to-use, cloud-based graph service that makes graph database and analytics accessible to everyone; the solution is the only distributed graph service with high availability. 

TigerGraph Cloud provides the ideal cloud-based service to model, search, and traverse relationships for analytical, transactional and real-time workloads. Users can start for free, then as their data expands they can expand across the cloud.  At the same time, with TigerGraph’s ability to do SQL-like database computation along with ACID-compliant transactions, users benefit with a lower TCO compared to other analytic products.

TigerGraph Cloud allows users to get started in minutes, build a proof-of-concept model in hours and deploy a solution to production in days. It eliminates the need to set up, configure or manage servers, schedule backups or look for security vulnerabilities. Also, TigerGraph offers a free tier of TigerGraph Cloud in perpetuity that enables data scientists, developers, business analysts, students and other enthusiasts to experience this technology’s unique power to handle real-world data challenges. 

With this version of TigerGraph Cloud that includes configuration for distributed graphs and replica instances for high availability, as well as the ability to leverage EFS for backup and restore. 

In addition to elastic, pay-only-for-what-you-use pricing, subscribers can provision distributed TigerGraph services for large production datasets; have the choice to deploy high availability TigerGraph services; and have more TigerGraph Cloud starter kits to choose from for fast application development, including new neural networks, cybersecurity and payment fraud detection kits. Starter kits are built with sample graph data schema, dataset, and queries focused on specific use cases such as fraud detection, real-time recommendation, machine learning, and explainable AI.

Going into 2020, Microsoft Azure will soon be one of the backend options for users of TigerGraph Cloud, making it the first truly cloud neutral graph database-as-a-service in the market.

Intel releases new class of AI hardware from cloud to edge to boost AI development, deployment and performance

Intel has updated its artificial intelligence (AI) offerings with new products designed to accelerate AI system development and deployment from cloud to edge. Intel demonstrated its Intel Nervana Neural Network Processors (NNP) for training (NNP-T1000) and inference (NNP-I1000) — Intel’s initial purpose-built ASICs for complex deep learning with incredible scale and efficiency for cloud and data center customers. 

Intel also revealed its next-generation Intel Movidius Vision Processing Unit (VPU) for edge media, computer vision and inference applications.

These products further strengthen Intel’s portfolio of AI solutions, which is expected to generate more than $3.5 billion in revenue in 2019. The broadest in breadth and depth in the industry, Intel’s AI portfolio helps customers enable AI model development and deployment at any scale from massive clouds to tiny edge devices, and everything in between.

Now in production and being delivered to customers, the new Intel Nervana NNPs are part of a systems-level AI approach offering a full software stack developed with open components and deep learning framework integration for maximum use.

The Intel Nervana NNP-T strikes the right balance between computing, communication and memory, allowing near-linear, energy-efficient scaling from small clusters up to the largest pod supercomputers. 

The Intel Nervana NNP-I is power- and budget-efficient and ideal for running intense, multimodal inference at real-world scale using flexible form factors. Both products were developed for the AI processing needs of AI customers like Baidu and Facebook.

“We are excited to be working with Intel to deploy faster and more efficient inference compute with the Intel Nervana Neural Network Processor for inference and to extend support for our state-of-the-art deep learning compiler, Glow, to the NNP-I,” said Misha Smelyanskiy, director, AI System Co-Design at Facebook.

Additionally, Intel’s next-generation Intel Movidius VPU, scheduled to be available in the first half of 2020, incorporates unique, highly efficient architectural advances that are expected to deliver leading performance — more than 10 times the inference performance as the previous generation — with up to six times the power efficiency of competitor processors. 

Intel also announced its new Intel DevCloud for the Edge, which along with the Intel Distribution of OpenVINO toolkit, addresses a key pain point for developers — allowing them to try, prototype and test AI solutions on a broad range of Intel processors before they buy hardware.

With most of the world running some part of its AI on Intel Xeon scalable processors, Intel continues to improve this platform with features like Intel Deep Learning Boost with Vector Neural Network Instruction (VNNI) that bring enhanced AI inference performance across the data center and edge deployments. 

While that will continue to serve as a strong AI foundation for years, the most advanced deep learning training needs for Intel customers call for performance to double every 3.5 months, and those types of breakthroughs will only happen with a portfolio of AI solutions like Intel’s. Intel is equipped to look at the full picture of computing, memory, storage, interconnect, packaging and software to maximize efficiency, programmability and ensure the critical ability to scale up distributing deep learning across thousands of nodes to, in turn, scale the knowledge revolution.

Microsoft Research releases PipeDream system to train deep neural networks using pipeline parallelism

Microsoft researchers in the Systems Research Group, along with students and colleagues from Carnegie Mellon University and Stanford University, have proposed a new way to parallelize DNN training. The system, called PipeDream, achieves up to 5.3 times faster training time than traditional approaches across a range of models.

DNN training happens in iterations of forward and backward pass computations. In each iteration, the training loop processes a minibatch of input data and performs an update to the model parameters. The most common approach to parallelize DNN training is a method called data parallelism, which partitions input data across workers (accelerators).

Unfortunately, despite advances in performance optimizations to speed up data parallelism, it can suffer from high communication costs at scale when training on cloud infrastructure. Also, rapid increases in GPU compute speed over time will further shift the bottleneck of training towards communication for all models.

Deep Neural Networks (DNNs) have facilitated tremendous progress across a range of applications, including image classification, translation, language modeling, and video captioning. DNN training is extremely time-consuming, needing efficient multi-accelerator parallelization.

PipeDream, a system developed as part of Microsoft Research’s Project Fiddle, introduces pipeline parallelism, a new way to parallelize DNN training by combining traditional intra-batch parallelism (model and data parallelism) with inter-batch parallelism (pipelining).

PipeDream revisits using model parallelism for performance, as opposed to the traditional motivation of working set size limitations for training large models. It uses pipelining of multiple inputs to overcome the hardware efficiency limitations of model-parallel training. A general pipeline parallel setup involves layers split across stages, with each stage potentially replicated and running data parallel. 

Multiple batches are injected into the pipeline to keep it full in steady state. Pipeline-parallel training, in most cases, communicates far lesser data than data-parallel training as it needs to communicate only the activations and gradients at the boundary of two stages. In steady state all workers are busy doing work with no pipeline stalls as in model-parallel training.

As DNNs do not always divide evenly among available workers, PipeDream may decide to use data parallelism for some stages—multiple workers can be assigned to a given stage, processing different minibatches in parallel. PipeDream uses a scheduling algorithm called 1F1B to keep hardware fully utilized, while achieving semantics similar to data parallelism.

PipeDream has been built to use PyTorch (an earlier version of PipeDream uses Caffe). Its evaluation, encompassing many combinations of DNN models, datasets, and hardware configurations, confirms the training time benefits of PipeDream’s pipeline parallelism. 

Compared to data-parallel training, PipeDream reaches a high target accuracy on multi-GPU machines up to 5.3 times faster for image classification tasks, up to 3.1 times faster for machine translation tasks, 4.3 times faster for language modeling tasks, and 3 times faster for video captioning models. PipeDream is also 2.6 to 15 times faster than model parallelism and up to 1.9 times faster than hybrid parallelism.