Malicious software is a pernicious global problem. A novel multi-task learning framework is proposed in this paper for malware image classification for accurate and fast malware detection. We generate bitmap (BMP) and (PNG) images from malware features, which we feed to a deep learning classifier. Our state-of-the-art multi-task learning approach has been tested on a new dataset, for which we have collected approximately 100,000 benign and malicious PE, APK, Mach-o, and ELF examples. Experiments with seven tasks tested with 4 activation functions, ReLU, LeakyReLU, PReLU, and ELU separately demonstrate that PReLU gives the highest accuracy of more than 99.87% on all tasks. Our model can effectively detect a variety of obfuscation methods like packing, encryption, and instruction overlapping, strengthing the beneficial claims of our model, in addition to achieving the state-of-art methods in terms of accuracy.

Nowadays, cybersecurity companies implement a variety of methods to discover new, previously unknown malware files. Machine learning (ML) is a powerful and widely used approach for this task. At Kaspersky we have a number of complex ML models based on different file features, including models for static and dynamic detection, for processing sandbox logs and system events, etc. We implement different machine learning techniques, including deep neural networks, one of the most promising technologies that make it possible to work with large amounts of data, incorporate different types of features, and boast a high accuracy rate. But can we rely entirely on machine learning approaches in the battle with the bad guys? Or could powerful AI itself be vulnerable? In this article we attempt to attack our product anti-malware neural network models and check existing defense methods. An adversarial attack is a method of making small modifications to the objects in such a way that the machine learning model begins to misclassify them.

Cybercriminals today are extremely organized and often take advantage of social trends to deliver weaponized bundles used to launch an attack against victims. These bundles are typically delivered via phishing emails or malware web sites that include misinformation targeting fears and uncertainty. In recent months, for example, threat intelligence researchers have been seeing an evolution in ransomware attacks targeting those most impacted by COVID-19, such as hospitals and health care providers. In fact, 41 hospitals announced ransomware attacks during the first half of 2020. Ransomware gangs, typically associated with well-established and known criminal organizations are also evolving their tactics for extortion, including publicly shaming victim organizations and threatening to publish files to the internet or auction off PII (personally identifiable information) to the highest bidder.

We all live by rules, some rules are defined strictly and some loosely. There is new research in social psychology about how our world is wired by rule makers & rule breakers¹, including how all of us as people and communities are wired to follow some rules'tightly', and some'loosely'. Cybersecurity is eventually about people, and how some break rules (attackers) and others make rules (Cyber Warriors & products). The cybersecurity effort at the very heart of it is a pattern recognition problem, trying to understand patterns of attacks in various ways and classifying them into benign (rule follower), malicious (rule breaker), or potentially requiring more investigation on precise intent. So, what is the role of Machine Learning (ML) in such pattern recognition problems?

A branch of artificial intelligence called machine learning is all around us. It's employed by Facebook to help curate content (and target us with ads), Google uses it to filter millions of spam messages each day, and it's part of what enabled the OpenAI bot to beat the reigning Dota 2 champions last year in two out of three matches. There are seemingly endless uses. Adding one more to the pile, Microsoft and Intel have come up with a clever machine learning framework that is surprisingly accurate at detecting malware through a grayscale image conversion process. Microsoft detailed the technology in a blog post (via ZDNet), which it calls static malware-as-image network analysis, or STAMINA.

"Human analysis is very limited. We quickly get overwhelmed," says Leyla Bilge, a member of the Symantec Research Labs whose team studies the future use of artificial intelligence in blocking attacks. "AI on the other hand can handle millions of calculations in a second. It can identify malicious activity that humans miss." The good news is that advances in AI, machine learning, and advanced behavioral analytics may change the equation in security's favor.

What is DMARC and How Does it Improve Email Security? By practically every measure, cybersecurity threats are growing more numerous and sophisticated each passing day, a state of affairs that doesn't bode well for an IT industry struggling with a security skills shortage. In a recent ESG and ISSA survey, 70 percent of cyber security professionals felt the cybersecurity skills gap had an effect on their organization. The Center for Cyber Safety and Education and (ISC)2 predicted a shortfall of 1.8 million cybersecurity professionals by 2022 after quizzing 19,000 security experts. With less security talent to go around, there's a growing concern that businesses will lack the expertise to thwart network attacks and prevent data breaches in the years ahead.