The pandemic has created a huge challenge for businesses across the world to continue operating irrespective of the massive shutdowns of workplaces and other facilities. The IT infrastructure on which people were dependent, namely the data centers, cloud systems, internal servers, and the devices that remote working employees used to stay connected to each other and to the company’s mainframe – becomes even more vital. Suddenly, the demands placed on the digital infrastructure have skyrocketed.
While fulfilling the technological challenges to facilitate employees, such technological setups also become a very lucrative target for cybercriminals. Cybersecurity efforts need to be upgraded to prevent a massive crisis from emerging; which puts vital data at risk. In other words, “business continuity” has to gear up to prevent this disaster, to prevent losing billions to these attacks.
In order to tackle this impending disaster along with using other methods, application of deep learning in various ways can prevent organisations from losing their data to cyber criminals. Deep learning is another name for machine learning, it mimics the human brain and derives similar conclusions that a human mind would do while it analyses via its logical brain structure. Deep learning also consists of such neural networks, which are made up multi-layered structures of algorithms. Therefore, deep learning application is considered as one of the best solutions to create a robust cyber security structure. There are four different ways this technology can prevent cyber-attacks.
1. Prevention against malware – traditional solutions to combat malware was to install and enable firewalls that used to detect the malware using the signature-based detection system. A pre-installed database of known threats is installed which is updated by the company regularly when novel threats are detected. While this mechanism is effective against few threats, it fails to deal with advanced threats. With the help of deep learning methods, a company can detect advanced threats and don’t rely on remembering known threats and common attack troubleshoot problems. Instead, the system learns can recognize suspicious activities that might indicate the presence of malware.
2. Intrusion detection and prevention systems – These systems detect malicious network activities and prevent intruders from hacking the systems and alerts the user. Typically, they are recognized by predicted signature patterns and generic attack forms. This is useful against threats like data breaches. Previously, this task was performed by ML algorithms. However, these algorithms made the system generate many false-positives, creating additional work for security teams and causing unnecessary fatigue. Deep learning, convolutional neural networks and Recurrent Neural Networks (RNNs) can be applied to create smarter detection/ prevention systems by assessing the traffic with better accuracy, reducing the number of false alarms and helping security teams differentiate bad and good network activities.
3. Spam detection – Deep learning technique can easily detect and deal with spam and likewise similar or other forms of social engineering. Natural language processing and deep learning combined can learn normal forms of language and interaction patterns and can use various statistical models to detect and block spamming.
4. Analysing the User Behaviour – It is extremely effective and useful for an organisation to analyse and detect user behaviour as an important security practice for a company. However, it is much more difficult than recognising malicious activities, as often these activities bypass the security measures and doesn’t raise red flags. When insider threats occur and employees use their legitimate access as a malicious intent, they are not intruding the system from the outside, which makes many cyber defense applications useless against such attacks.
User and Entity Behavior Analytics (UEBA) is a great application against such attacks. After a short learning period, it can pick up the standard employee behavioural patterns and recognize questionable activities, such as accessing the system in unusual hours, that possibly indicate an insider attack and raise alerts.
Thus, there is multiple usage of deep learning techniques in various domains but using this technique towards strengthening the cyber security threats, will create safer business environments and likewise save the system to lose out on information and incur losses.
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