Just like the integration of electricity and the Internet of Things (IoT), a combination of machine learning (ML) with blockchain technology has the capability of revolutionizing the globe. As today’s chips are almost hitting Moore’s Law upper limit, the world is in dire need of intelligent machines as well as sustainable solutions. With the steady invention of cheap-yet-powerful chips, super connectivity, and ultra-high performing computers, the machine learning industry is witnessing enormous development and interest. In comparison with ML, blockchain is a new industry but significantly fundamental like the Internet of Things.
In the modern world, people interact with ML while using the internet, emailing, social networking, mapping, e-commerce, and even playing games. Fascinating future applications of machine learning will include smart home security systems, health solution technologies, remote-controlled (driverless) vehicles, and image and voice recognition, etc. Until now, the blockchain solutions are extensively applied in cryptocurrency.
In the future, the blockchain sector promises to unveil some solutions like decentralization and distribution of industries holding financial and power capability, with first-class protection. It grants operators the control and authority of various assets and information, technology currency, and enables its easy share. In the security perspective, both ML and blockchain-based cloud are collaborating to offer top-notch security of big data.
Now, the edition of cyber-security offers a list of new technologies in town meant to provide cyber-security. These solutions are enabled by artificial intelligence (AI), machine learning (ML), blockchain-based cloud, IoT-powered innovations, and gamification among others. These emerging technologies offer protection to organizations against cyber-threats, information breaches, phishing, and against complex attacks harbored in the cloud, endpoints, and network layers.
Machine learning is simply defined as the computing capability (power) of machines (computers) to learn without having to be programmed. ML algorithms apply math techniques across a wide set of data to generate (build) behavioral models and apply these models as the fundamentals of constructing future predictions depending on the data in hand.
So, in line with business, what are common practices of machine learning in data security? ML enables cyber-security pundits to understand the security threats their companies are facing, and help them focus on valuable and strategic actions. It assists in automating the monotonous and menial activities previously executed by overtasked and sometimes poorly equipped security personnel.
Consequently, ML in cyber-security is a vastly growing industry. According to ABI Research, by 2021, ML in cyber-security is set to increase the expenditure in big data, AI and other analytics, whereas some giant technology companies across the globe are already adopting it to offer protection to their customer base. For instance, Google is already using ML solutions to analyze cyber-threats against mobile endpoints that run on android – and detecting and removing viruses from infected devices. Also, Amazon is said to have launched the harvert.AI and AWS Macie, solutions that adopt ML approaches to identify and group data kept on S3 cloud storage capability.
Security experts are toiling to integrate ML with both old and new security solutions, mainly to improve threat detection. According to Jack Gold, the president of J. Gold Associates, most giant security firms have shifted their operations from solely “signature-powered” systems that were used a couple of years back for malware detection to ML-enabled systems that learn and interpret the activities of events and understand whether the events are safe or not. “Although it’s yet a growing industry, it will be the way to go a few years to come. AI and ML will dramatically improve data security.”
Table of Contents
Some of the many uses of machine learning in cyber-security include:
Algorithms of machine learning will enable organizations to identify malware and prevent attacks before they commence. According to David Palmer, the director at Darktrace, a UK-based technology firm, the company has been successful in its adoption of ML-based immune solutions since its establishment back in 2013.
Recently, using the ML algorithm, Darktrace was able to detect some data exfiltration attacks launched via a connected fish tank that occurred in a casino shop in North America. The company boasts to have stopped a similar attack that occurred during the popular WannaCry ransomware crisis – over 200,000 victims in over 100 countries were affected. According to Palmer, their ML-based algorithms detected the attack in the NHS agency network and mitigated it within seconds.
ML is going mainstream to incorporate mobile gadgets, but the effort has been emphasized in voice-powered applications like in Google Now, Siri of Apple Inc., and Alexa of Amazon. Security companies are working to assist businesses to adopt mobile antivirus technologies that use machine language. For example, MobileIron and Zimperium have partnered – Zimperium’s ML-powered threat detection is to be integrated with MobileIron’s security and compliance engine – to sell their combined solution that can identify device, network as well as application threats and automatically mitigate the action to offer security to the data.
It’s believed that machine learning assists human analysts with various job aspects like detection of malware, network analysis, endpoint protection, and examination of vulnerability. For instance, in 2016, CSAIL – Computer Science and Artificial Intelligence Lab department at MIT – built an AI system, an ML platform that assists security analysts to determine the “needles in a haystack.” This system could review millions of logins daily, filter the data, pass the data to a human analyst, and minimize notification, among other actions.
By automating repetitive tasks, the organization staff will focus on other important issues. ML ultimately aims at eliminating human efforts in handling repetitive, low-value decision-making activities. For instance, Booz Allen Hamilton, an information tech consulting firm has adopted this way, reportedly applying ML solutions to efficiently replace human security resources to allow employees to focus on other tasks.
ML can assist close vulnerabilities, especially those zero-day threats targeting unprotected IoT systems. Security pundits are proactively working on this. For instance, at Arizona State University, a team of experts has developed an ML-powered technology to inspect traffic in DARK WEB and detect information associated with zero-day exploits.
The dire need for modern generation financial solutions has resulted in the emergence of cryptocurrency. Recently, studies on blockchain show that it is safe electronic cash transacted by conversing between two peers without the inclusion of a third party. It is a ledger for transacting and offers protection against hacking when transacting virtual money. The records of transactions are encrypted in regard to the regulations and run in computer systems operating in blockchain programs. The transaction data is owned by various people making it hard to be hacked and saving the security expenses. The transactions are approved through mass participation while promptness is ensured. Additionally, it is easy to implement, connect, and expand the system with the use of an open-source and records of the transaction can be openly disclosed and accessed to publicize every detail about the transaction while reducing the regulatory expenses.
The use of blockchain offers better security than storing the information in one centralized database. From the perspective of data management and storage, data damage due to attacks is prevented. Also, due to the transparency of blockchain, the data can be disclosed to any authorized party when required. These strengths enable its suitability in various sectors such as the financial industry and IoT environments.
Through an authentication process, transaction records are finalized by blockchain, by a person forming a block when they combine these transactions over a network. Due to its wide availability and efficiency, cloud computing is used in hand with blockchain. Since the blockchain-based decentralized cloud security solutions are still at their infancy, the prospects show that in the future, there will be more decentralized providers of cloud services and will certainly compete with the already existing ones.
In the last decade, blockchain technology has drawn much attention to a modern financial solution thanks to its security that fits very well in this informatization period. Specifically, blockchain solutions offer security through their authentication of peers enabling sharing of virtual money, encryption as well as hash value generation. The hash values kept by each peer in this block can be impacted by the previous block values hence it is hard to even falsify or manipulate the registered data. The change of data. The global financial sector estimates the blockchain-based technology to have grown by over 20 billion dollars this year.
Additionally, blockchain technology is applicable beyond the IoT niche, and its use is expected to grow. Cloud computing is epitomizing the fundamentals of digital transactions. Over the last decade, the industry has grown at a tremendously fast pace. This increasing need for cloud computing services is a result of fast technological innovations, causing the growth of hundreds to thousands of modern cloud capabilities.
For instance, the main players in the industry like Amazon Web Services as well as Microsoft Azure unveil approximately forty to fifty modern cloud capabilities monthly. This pace of innovation is fundamental for the market, but on the other hand, it is increasing the complexity level and confusion to firms with already sophisticated IT systems. But, blockchain solutions are emerging as strong enablers of decentralized cloud technologies.
Although ML and blockchain-based cloud security technologies can offer top-notch security capabilities for your data, they are not silver bullets. For instance, if the algorithms are not well designed, they may not be useful. This implies that for the technologies to work efficiently, they must be designed properly.
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