Deep learning overhauls the existing options for growing cyber threats.
Fremont, CA: Cybersecurity, otherwise known as information technology security, refers to the act of protecting the data, systems, networks, and programs from digital attacks. In the existing connected world, the necessity to secure systems is rising. It is evident from the prevailing cyberattacks in different industry sectors like health care, government agencies, education institutions, and energy. According to the UK government’s Cyber Security Breaches Survey 2019, 32% of businesses have identified cybersecurity breaches or attacks in the past 12 months. Besides, with the advent of new devices that overhaul people, security risks are evolving, and cyberattackers are becoming more innovative in implementing these attacks. However, to stand off the digital attacks, a better understanding is needed. Let's take a look at the common types of cyberattacks:
Malware is a type of malicious software, including ransomware, spyware, viruses, and Trojan horses. The attack occurs when a user accidentally clicks a dangerous link or associated email attachments that install risky software on to the computer. Once the malware gains access into the system, it can do the following.
· Install some additional harmful software
· Blocks access to files or the computer system
· Renders the system inoperable by disrupting the specific system and network components
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Man-in-the-Middle (MitM) attack is an eavesdropping attack, where an attacker intercepts a two-party transaction between a trusted client and network server. The eavesdropper tends to steal victims' credential information like passwords, login IDs, credit card details, and much more. The attacker alters their communication by gaining access to their data when they still believe that they communicate with each other. In this type of attack, the attackers sneak through the network by
· Session hijacking
· IP spoofing
According to a study, over 1 million new phishing websites are created every month and is one of the most annoying cyber threats. It is the practice of sending fraudulent emails that appear to be from trusted sources. These emails contain the link to steal the users’ sensitive credentials like credit card number, login details, and in some cases; it can install malware into the victim’s system. Some of the types of phishing attempts include
· Vishing: It is conducted over the phone to make the victim reveal their PINs, passwords, and payment details.
· Smishing: It is a popular form of phishing executed by sending SMS.
· Spear Phishing: It is a targeted phishing attack attempted to reach a specific victim to seek financial gain.
Distributed Denial of Service (DDoS) attack
In this type of attack, a system is overwhelmed with more volume of network traffic than it can handle, making them unable to respond to the service requests. Unlike other attacks that try to steal credentials, this attack is designed to disrupt the regular activity of an organization as revenge by the attackers. The distraction gives a chance for cybercriminals to launch different types of attacks as well.
SQL injection attack
SQL is a programming language used to communicate with databases, thereby manage data in these databases. A SQL injection occurs when an attacker inserts malicious code into an SQL powered server enabling the server to expose sensitive information and provide access or ability to modify data.
As the types of attacks are increasing daily, technology can play a vital role in mitigating the threats while protecting the organizations and computers themselves from attacks and data breaches. In that sense, the effective techniques employed include next-generation firewalls, malware and ransomware protection, antivirus software, DNS filtering, and email security solutions. Still, none of the above protection methods achieved a great deal in facing the security challenges. In this scenario, deep learning is proving to be a unique approach that can quickly mitigate security risks.
Deep learning is a subset of machine learning that processes data in a non-linear approach. It is an Artificial Intelligence (AI) function that can work like a human brain to make critical decisions. Being an advanced model of traditional machine learning, deep learning can be used to detect money laundering or fraud. Deep learning identifies fraudulent activity utilizing the non-linear technique, including the time, IP address, geographic location, type of retailer, and others. With traditional machine learning, things that contribute to threat are defined while with deep learning, a variety of risks can be detected using the labeled data as “threat” and “non-threat.” Malware detection, network intrusion, spam, phishing detection, and binary analysis are some areas where deep learning has shown significant improvements utilizing the rule-based and classic machine learning-based solutions. For its extensive capabilities, deep learning is also used across various fields such as computer vision, natural language processing, audio, speech and image recognition, automatic handwriting generation, healthcare, and virtual assistants.
Of all, security awareness can earn a lot. Empowering the IT people by conducting awareness to make appropriate security decisions through their knowledge of deep learning is of paramount importance to keep in pace with the awaiting cyber threats.