Authored By: Sudipta Biswas, Core Committee member of Infosec Foundation
Artificial Intelligence (AI) and Machine Learning (ML) are changing the natural order of things—right from how we work and how the economy runs, to the nature of today’s warfare, communications, privacy protection norms, etc. Classic example of AI is Driver-less Car. While their long-term impact remains uncertain, these technologies are a huge help to cyber security experts as they can be used to quickly identify and analyse possible attacks.
Artificial intelligence (AI) is the ability of a computer program or a machine to think, learn and act like a human being. It is also a field of study that tries to make computers smart. The main goal of AI is to enable the development of computer systems that are able to do the things that humans do. AI involves the study of different methods for making computers behave as intelligently as people. It is the concept of making machines capable of performing tasks without human intervention, such as building smart machines.
Machine learning (ML) is a subset of AI and is based on the idea of writing computer algorithms that automatically upgrade themselves by discovering patterns in existing data, without being explicitly programmed. It is also used to automatically analyse the way interconnected systems work in order to detect cyber-attacks and limit their damage. The entire processing of ML tools depends on data. The more data an algorithm obtains, the more accurate it becomes and thus, the more effective the results it delivers.
The Role of Machines In Cyber Security
Machine learning and artificial intelligence (AI) are being applied more broadly across industries and applications than ever before, as computing power, data collection and storage capabilities increase. From the cyber security perspective, this means new exploits and weaknesses can quickly be identified and analysed to help mitigate further attacks.
The Perfect Fit
Machines are much better and more cost-efficient than humans when it comes to handling huge amounts of data and performing routine tasks. This is exactly what the cyber security industry needs at the moment, especially with the large number of new threats appearing every day. Most of these new threats can easily be classified under existing families or familiar types of threats. In most cases, spending time looking at each new threat in detail would, in all probability, be a waste of time for a researcher or reverse engineer. Human classification, especially in bulk, will be error-prone due to boredom and distractions. Machines, however, do not mind going through the same routine, over and over, and they perform routine, repetitive tasks much faster and more efficiently than people do.
But that doesn’t mean they always get it right. Even with AI, it is necessary to keep an eye on the work to check whether the algorithms are still working within the desired parameters. AI and ML without human interference might drift from the set path. But working in partnership with AI, researchers are relieved of the burden of menial work.
The Impact of AI and ML
The past five years have seen a tremendous rise in the use of AI and ML technologies for enterprises. Most applications can be attributed to advancements in computing power and the evolution of paradigms like distributed computing, Big Data and cloud computing. Early commercial applications of ML were pioneered by technology giants like Google, Amazon and Facebook. These businesses managed to build a store of valuable behavioural data from millions of users. In order to effectively collect, cleanse, organise and analyse their consumer data, these companies built scalable Big Data frameworks and applications which were then open sourced to the world. This helped these frameworks to improve fast and allowed businesses to derive more value from their data. Organisations are already beginning to use AI to bolster cyber security and offer more protection against sophisticated hackers. AI helps by automating complex processes for detecting attacks and reacting to breaches.
Machine Learning and Artificial Intelligence (AI) are being applied more broadly across industries and applications than ever before, as computing power, data collection and storage capabilities increase. From the cyber security perspective, this means new exploits and weaknesses can quickly be identified and analysed to help mitigate further attacks.
How is Artificial Intelligence Being Used?
In order to detect unusual behaviour on a network, there are newer security technologies that are using Artificial Intelligence programs. AI uses machine learning to detect similarities and differences within a data set and report any anomalies. Machine learning is a part of AI that can help to recognize patterns in data and predict effects based on past experience and data. AI systems, in most of the cases, use machine learning technology to generate results that replicate human functioning. As per an article published in Forbes titled Separating Fact From Fiction: The Role Of Artificial Intelligence In Cybersecurity, ML, coupled with application isolation, prevents the downside of malware execution — isolation eliminates the breach, ensures no data is compromised and that malware does not move laterally onto the network.
Another way that cyber-attacks are changing are in terms of speed. Humans are not able to detect the abnormalities at the speed that the attacks happen. AI, however, can assess a huge amount of data generated on a network to identify what doesn’t belong there.
AI solutions can work effectively if there are powerful input data, so organizations can start to capture their log data and consolidate into a common data repository so that the broad set of AI-enabled tools and analytics can become effective. There should also be a complete visibility to all aspects of the network, which includes internal network communication, server logs, etc.
Security experts are hoping to use predictive analytics to frame new ways to deal with cyber threats. These are insight driven solution enabled with the help of AI. Machine learning can help in anti-malware, performing dynamic risk analysis and detecting anomaly. AI techniques can be made to learn to remove the noise or unwanted data, and facilitate security experts to understand cyber environment for detection of any anomalous activity. AI can also benefit cyber security with automated techniques to generate cyber courses of action (COAs) whenever cyber threats are detected.
It is believed that now is the time to seriously contemplate artificial intelligence for cyber-security for any business. If you wish to protect your business data against cyber-attacks
How Can AI and Machine Learning Help Prevent Cyber Attacks?
AI systems and deep learning algorithms are already helping cybersecurity professionals develop effective solutions to fight against cyber-crime. If it weren’t for artificial intelligence and machine learning, the cybersecurity landscape would be very different than it is right now.
As cyber threats evolve, and the attacks become more complex and widespread, conventional defense tools are often not enough to detect and stop them on time. Therefore, security solutions that are powered by machine learning are the next big thing in cybersecurity.
Thanks to their ability to learn and adapt over time, such tools can promptly eliminate well-known threats, as well as respond to new emerging risks before they do any harm, by recalling and processing data from prior attacks.
Another benefit of artificial intelligence is the ability to perform specific tasks on its own, this way saving time and reducing the risk of human error. Unlike people, AI systems don’t make mistakes as they handle threats according to a standardized playbook, this way responding to each threat in the most effective way.
With the AI systems on their side, security experts can spend less time performing routine tasks and focus on building a stronger defense that would allow stopping sophisticated cyber-attacks before they even occur. Therefore, implementing machine learning and AI systems is crucial to stay one step ahead of cybercriminals.And yet, no technology is a silver bullet, and AI is just a tool, which can only do what criminals or security experts command it to do.
AI and ML may become new paradigms for automation in cybersecurity. They enable predictive analytics to draw statistical inferences to mitigate threats with fewer resources. Applications for automated network security include self-encrypting and self-healing drives to protect data and applications.
In the current world of data deluge, it is nearly impossible for humans alone to analyse the billions of logs generated from the existing infrastructure components. Integrating AI into the existing systems including Security Monitoring Solutions, SIEM, Intrusion Detection Systems, Cryptographic technologies and Video vigilance systems can help in addressing many of these challenges to a larger extent. Application of AI based technologies into the existing systems will bring in much enhanced systems that help in better decision making. Some of the key areas where in the functionalities of AI makes a difference are:
• Data Mining
• Pattern Recognition
• Fraud Detection
• Analytics
• Fuzzy Logic
• Development of expert Systems
Within the Cyber security sector, these attributes of AI can bring in tremendous benefits, out of which some of them are already in place and there are huge opportunities yet to explore. Machine learning based antivirus systems and tools can help in quickly and accurately identifying malware like Polymorphic virus based on its continuous learning capabilities. Such systems can detect suspicious files based on the behavioural or structural analysis and it helps in detecting threats at an early stage. It can easily determine the likelihood of a malicious virus attack by analysing and breaking down the DNA of each file.
Along with AI and ML, another aspect of security which CISOs are concerned about is compliance. Every organization needs to be compliant with numerous regulations and non-compliant to any of these can lead to heavy fines. For example, General Data Protection
Regulation (GDPR) which will be a reality in few months can cost €20m or 4% of annual global turnover if the organization is found non-compliant. AI and ML with support of cognitive computing is enabling the enterprises to keep a track of their compliance status to avoid any legal issues.
As the digital world is moving fast, we can expect completely automated Cyber-attacks orchestrated by intelligent machines. These expert systems will have the potential to analyse the DNA of past attack models, strategies and utilize its acquired knowledge for organizing new attack models attacks that have higher success rates and larger impact. As human resources alone won’t be enough to combat this, the need of the hour for global organizations, Government and defence agencies is to suit up their existing Cyber security and defence environment with AI and its underlying technologies.
“Cybersecurity solutions that rely on ML use data from prior cyber-attacks to respond to newer but somewhat similar risks.”
In this way, an AI system powered by ML can leverage what it knows and understands about past attacks and threats to identify other attacks in the same vein or style.
Because hackers are consistently building upon older threats – including new abilities or tweaking previously used samples to build out a malware family – utilizing AI and ML systems to look out for and provide notification of emerging attacks could be incredibly beneficial to stemming the tide of zero-day threats.
AI and ML have made it a bit easier to detect the proliferation of malware and identify early on in the lifecycle if a file/resource is showing signs of belligerent behaviour. This level of automation has been possible with pattern detection, behaviour-based anomaly detection and advanced use of heuristics – all based on Machine-learned solutions – to keep the intruders out.
Types of Artificial Intelligence Applications Being Used in Cyber Security Solutions:
It is up to human imagination. For the sake of clarity, following application categories can be examined:
- Spam Filter Applications (spamassassin)
- Network Intrusion Detection and Prevention
- Fraud detection
- Credit scoring and next-best offers
- Botnet Detection
- Secure User Authentication
- Cyber security Ratings
- Hacking Incident Forecasting
