Deep learning has gained significant traction in the field of cybersecurity due to its ability to detect and prevent various types of cyber threats effectively. Cybersecurity protects internet-connected devices and services from malicious attacks by hackers, spammers, and cybercriminals. Companies use it to protect themselves against phishing schemes, ransomware attacks, identity theft, and data breaches. In today’s world, technology plays a more significant role in everyday life than ever before. Smart home automation technology and concepts such as the Internet of Things are examples of this trend. Technology brings in so many benefits, it’s not that hard to believe, there could be threats lurking behind every device. And these cyber security threats posed by modern tech are real, despite society’s rosy perception of modern advances.
Cyberattacks are increasing in volume and complexity, and artificial intelligence (AI) helps under-resourced security operations analysts stay ahead of them. AI technologies like machine learning, natural language processing, deep learning, and other ML algorithms curate threat intelligence from millions of research papers, blogs, and news stories, reducing response times dramatically.
What Is Deep Learning
Deep learning, a subset of machine learning, is a neural network with three or more layers. The DL neural networks attempt to mimic the behavior of the human brain, leveraging knowledge learned by processing large amounts of information called training data. Although a neural network with a single layer can make approximate predictions, additional hidden layers used in DL networks can significantly improve accuracy. With the advancements in computing infrastructures, it is now possible to efficiently and quickly run large DL models on huge repositories of training data to glean useful and accurate knowledge.
Artificial intelligence (AI) uses deep learning to automate, analytical and physical tasks with minimal human involvement. Deep learning powers several everyday products and services, including digital assistants, voice-enabled TV remotes, fraud detection programs, and emerging technologies (like self-driving cars).
Deep Learning in Cyber Security
In 2023, cyber threats won’t be the only security challenge. Adopting new technologies brings vulnerabilities, while perennial issues make up the “top challenges. As a result, new challenges arise, such as ransomware evolution, blockchain revolution, lOT threats, AI expansion, and vulnerabilities with serverless apps.
Modern cyber security solutions must address the growing dynamics of modern cyberattacks (the changing trends), especially in detecting new threats, analyzing complex vectors and events, and scaling to the sheer volume of attacks. By identifying behavioral anomalies, detecting malware and botnets, and detecting DDoS attacks, deep learning can solve many cyber security problems.
Top 5 Applications of Deep Learning in Cybersecurity
We are on the cusp of a revolution in cybersecurity as deep learning is rapidly gaining traction and holds the key to solving all our cybersecurity problems. Here’s how the following DL applications can help us overcome cybersecurity challenges.
1. Intrusion Detection and Prevention Systems (IDS/IPS)
IDS and IPS systems detect malicious network activity, alert users in time, and prevent intruders from system access. Typically, malicious attacks are recognized by their known signatures and generic attack forms. Data breaches are an excellent example of this type of threat.
Traditionally, ML algorithms performed this task. However, these algorithms generated many false positives, causing unnecessary fatigue for security teams. By analyzing traffic with better accuracy, reducing false alerts, and helping security teams differentiate between good and bad network activities, deep learning, convolutional neural networks, and Recurrent Neural Networks (RNNs) play a significant role in creating innovative and effective ID/IP systems.
2. Signature-Based Detection
Malware is detected using signature-based detection systems in traditional malware solutions, such as firewalls. The company runs a database of known threats, updating it frequently to incorporate recent new threats. This technique is efficient against these threats but less effective against more advanced threats.
As deep learning algorithms do not solely rely on memorizing known signatures and attack patterns, they can detect more advanced threats. Learning the system allows them to identify suspicious activities that might indicate malware or bad actors.
3. Spam and Social Engineering Detection
Social engineering is the process of persuading someone to share their knowledge. People share valuable information with social engineers because they don’t understand the consequences, as well as because they don’t know how to protect their IT infrastructure and systems from cyber-attacks. An organization’s employees or third-party agencies may carry out these attacks. For financial gain or revenge, they violate the rules of the organization. In social engineering attacks, the attacker uses different tactics to gather sensitive information from the victims.
A deep learning technique called Natural Language Processing (NLP) can help us detect and deal with spam and other forms of social engineering. In order to detect and block spam, natural language processing uses a variety of statistical models to learn standard communication patterns and language patterns.
4. User Behavior Analytics
Tracking and analyzing user behavior is a standard and key security practice. Suspicious or malicious user behavior bypasses security measures and frequently does not raise flags and alerts, and hence is much harder to detect than traditional malicious activity against networks. In the case of insider threats, employees who abuse their legitimate access with malicious intent are not infiltrating the system from the outside, so many cyber defense tools are ineffective.
A security tool against such attacks is User and Entity Behavior Analytics (UEBA). It can detect suspicious activities, such as accessing the system at unusual hours, that may indicate an insider attack after a learning period.
5. Monitoring Emails
Maintaining an eye on the official email accounts of our employees is crucial in preventing cyberattacks. Phishing attacks, for example, are commonly perpetrated by emailing sensitive data to employees. These types of attacks can be prevented with the help of cybersecurity software and deep learning. Natural language processing can also be used to detect suspicious behavior in emails.
Cybersecurity is increasingly relying on deep learning. By automating repetitive tasks, classifying malware, analyzing network traffic, and identifying potential threats, deep-learning technology, and its applications strengthen the security of organizations and individuals. As DL evolves over time, it can preemptively recognize and block threats that it has never seen before, paving the way for a secure Internet world.