In recent years, the term "deepfake" has become increasingly popular in the tech world, with cybersecurity experts warning of its growing threat. Deepfakes are realistic images, audio or video content created with the help of machine learning and artificial intelligence techniques that manipulate or replace existing content to create convincing but false media. Deepfake technology can be used for a wide range of purposes, from harmless pranks to malicious activities such as fraud, blackmail, propaganda, or cyberbullying. In this article, we will explore the concept of deepfake, why and how it poses a threat to people, and the pros and cons of deepfake in terms of cybersecurity. Deepfake is a portmanteau of the words "deep learning" and "fake." It is a type of synthetic media that is created using deep learning algorithms, specifically generative adversarial networks (GANs), which learn to generate new data that is similar to existing data.

Gerasim Hovhannisyan, CEO and Co-Founder atEasyDMARC, a rapidly growing B2B SaaS to solve email security and deliverability problems. In part, because hybrid and remote workplaces are the new normal for most companies, the sophistication of cyberattacks and the risks they pose have grown rapidly over the last few years. In fact, these new work styles have opened up a whole new set of phishing methods for threat actors. According to Cybersecurity Ventures, global cybercrime is expected to grow by 15% per year over the next five years, costing about $10.5 billion by 2025. Even though hundreds of IT experts analyze threats daily, it is a daunting task.

Microsoft is reportedly investing $10-billion in OpenAI, the owner of the somewhat controversial large language model chatbot ChatGPT. It uses a deep learning technique to generate text and conversations often indistinguishable from those created by actual humans. ChatGPT has dazzled amateurs and industry experts ever since its launch at the end of November last year. Given a prompt, ChatGPT can answer complex questions, provide suggestions and even debug programming code all while sounding extremely human. Microsoft's reported $10-billion investment in OpenAI shows that major companies are willing to invest in artificial intelligence software.

Abstract: Network intrusion detection systems (NIDSs) play an important role in computer network security. There are several detection mechanisms where anomaly-based automated detection outperforms others significantly. Amid the sophistication and growing number of attacks, dealing with large amounts of data is a recognized issue in the development of anomaly-based NIDS. However, do current models meet the needs of today's networks in terms of required accuracy and dependability? In this research, we propose a new hybrid model that combines machine learning and deep learning to increase detection rates while securing dependability.

AI victories are finally not science fiction anymore. With the help of the surrounding technologies such as graphic cards, cloud, and simple languages, The integration of AI (Artificial Intelligence) and RPA (Robotic Process Automation) within the supply chain field is finally happening. In addition, governments and organizations have started to regulate the same. We might won't be able to dive in the technicality side of each. However, will explain the relationship between this triad and how they share the sustainable same impact in today's world.

The threat of cyber attacks has recently increased dramatically and traditional measures now appear to be insufficiently competent. Because of this, deep learning in cyber security is rapidly gaining ground and may hold the key to solving all your cybersecurity issues. With the advent of technology, there is also an increase in threats to data security and the need to protect an organization's operations using cybersecurity tools. However, companies are struggling due to most cybersecurity tools being dependent. They rely on signatures or evidence of compromise for the threat detection capabilities of the technologies they use to safeguard their business.

Behavior Analysis An essential deep learning-based security strategy for any firm is tracking and examining user activities and habits. Since it goes beyond security mechanisms and sometimes doesn't trigger any signals or alerts, it is substantially harder to spot than conventional malevolent behavior against networks. For instance, insider attacks happen when employees utilize their legitimate access for nefarious purposes rather than breaking into the system from the outside, making many cyber protection systems ineffective in the face of such attacks. One effective defense against these attacks is User and Entity Behavior Analytics (UEBA). After a period of adjustment, it can learn the typical patterns of employee behavior and identify suspicious activity that may be an insider attack, such as accessing the system at odd hours, and then raise alarms.

Welcome to insideBIGDATA's "Heard on the Street" round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace. We invite submissions with a focus on our favored technology topics areas: big data, data science, machine learning, AI and deep learning. Data is the new oil.

The field of Data Science and Machine Learning is growing every single day. As new models and algorithms are being proposed with time, these new algorithms and models need enormous data for training and testing. Deep Learning models are gaining so much popularity nowadays, and those models are also data-hungry. Obtaining such a massive amount of data in the context of the different problem statements is quite a hideous, time-consuming, and expensive process. The data is gathered from real-life scenarios, which raises security liabilities and privacy concerns. Most of the data is private and protected by privacy laws and regulations, which hinders the sharing and movement of data between organizations or sometimes between different departments of a single organization--resulting in delaying experiments and testing of products.

The Industrial Internet of Things, or IIoT, has recently gained popularity due to its ability to create communication networks between different components of an industry and bring about the new revolution--Industry 4.0. Powered by wireless 5G connectivity and artificial intelligence (AI), IIoT holds the ability to analyze critical problems and provide solutions that can improve the operational performance of industries ranging from manufacturing to health care. IoT is highly user-centric--it connects TVs, voice assistants, refrigerators, etc.--whereas IIoT deals with enhancing the health, safety, or efficiency of larger systems, bridging hardware with software, and carrying out data analysis to provide real-time insights. However, while IIoT does have many advantages, it also comes with its share of vulnerabilities such as security threats in the form of attacks trying to disturb the network or siphoning resources. As IIoT is getting more popular in industries, it is becoming crucial to develop an efficient system to handle such security concerns.