How Machine Learning is Changing the Dynamics of Cyber Security for Australian Businesses
I was not shocked to read that private details of almost 100,000 Australian bank customers were exposed in a cyber-attack on a real-time platform called PayID (source: The Sydney Morning Herald). It is not that the hackers are eyeing at the popular enterprises only, medium and small sized businesses are equally at risk for cyber-attacks. The increased number of connected IoT devices have enhanced the complexity of the cyber threats. And these attacks are going to get more severe and unpredictable. No doubt, with continuous developments on the technology front there will be numerous ways in which you will be capable of protecting security breaches. However, the hacks have become so advanced that even the intelligent security systems find it difficult to detect and defuse these attacks. This is when the concept of machine learning and AI chips in.
Machine learning often referred to as ML is a small crop in the huge field of artificial intelligence. It employs a set of algorithms that allow computers to utilise cyber security models and verify the authenticity of the logged data, real-time communication as well as transactions. Predictive analytics, behavioural analytics and integration with real-time assessment tools are some of the innovative techniques which organisations are adopting to protect their data from sophisticated cyber-attacks.
Challenges faced by the machine learning technology in preventing sophisticated data theft
One of the staggering challenges that machine learning community is facing with the development of the technology in the field of cyber security is malware detection and identification. And this challenge is going to get dramatically typical since the hackers are using advanced techniques such as polymorphism, impersonation, compression and obfuscation to bypass deployed detection applications. Another challenge is definitely the scarcity of subject matter experts that ultimately lead to other problems such as incorrectly labelled samples, unbalanced datasets and much more.
Access to datasets is one of the key hurdles that stalls the entire process of cyber crime investigation. Due to advancements in technology, the machine learning community has also come up with some methods that can enable an organisation to be ready for the challenges or find methods to either eliminate cyber-attacks or minimise their effect. The machine learning community is also gearing up to present solutions which help businesses generate labels with advanced pivoting techniques and solve the problem of lack of appropriate datasets.
Another challenge I would like to highlight here is the lack of enough experienced and skilled cyber security analysts and scientists who can actually help control the rise of global cyber-attacks. There is a huge opportunity in the improvement of ML algorithms when it comes to tackling the cyber security issues because of the abundance of big data available. Let’s keep our fingers crossed that in the near future, there will be development of methods and solutions which will boost the application of machine learning in cyber security. If we keep the future challenges aside, there are numerous areas in which machine learning enables you to prevent the confidential data from cyber-attacks.
Few ways in which machine learning boosts cyber security in any organisation
There are several ways in which machine learning enables you to tackle the advanced cyber security attacks. Some of these are:
Machine learning constantly monitors the network for any breaches and generates abundant real-time data that enables you to figure out potential network threats. The threats that machine learning is capable of identifying are policy violations, unknown malware and insider threats.
Machine learning identifies and predicts the malicious applications and websites that are available online. It analyses the internet activity to identify the attack infrastructure that is staged for breaking into your secured network and stealing confidential data.
Machine learning algorithms can detect even the most complex and rare malware which are trying to ruin the health of your endpoints. The whole identification process is based on the activities and behaviour study of the malware which machine learning might have encountered previously.
Machine learning acts as a boon to the organisations that are shifting to cloud infrastructure after understanding its benefits. It analyses the suspicious login activities to prevent any security breach. It conducts IP reputation analysis on a regular basis to point out the threats and risks in the applications and software which are hosted in the cloud.
Machine learning analyses the encrypted traffic data elements on a regular basis and enables the capability of finding the malware from within the encrypted traffic. Machine learning algorithms study those malicious patterns and successfully finds the hidden threats.
Machine learning does help you to ensure the health of your infrastructure and network but relying on just a layer of security is not an advisable move. A single layer of security means the hacker or the bad actor has just one door to pass to gain access of your confidential data. To seal all the security loopholes of your network, you can rely on a product such as Sophos Intercept X. It comprises of numerous complementary and reinforcing obstacles which need to be overcome at once to successfully complete an attack. To dig deeper into the product specifications and its applications or know more about any other Sophos products, reach out to us at +61 29098 6006 or drop us a mail at email@example.com.
Exigo Tech is one of the leaders in offering managed services or ensuring robust security for your cloud and infrastructure. We recently got recognised as the Partner of the Year at Sophos Discovery. Contact us today and get all your network security concerns addressed!