20 Nov 2022| ONPASSIVE
Useful Deep Learning Applications For Cyber Security
Almost every business is being transformed by artificial intelligence (AI). Deep learning (DL), an AI approach, moves the high-tech industry forward with a seemingly limitless number of applications. They range from object identification for self-driving cars to potentially saving lives by enabling doctors to detect and diagnose cancer more precisely.
This post will look at deep learning and some of the most interesting deep learning applications for cyber security and how you might utilize deep understanding to improve your organization’s security procedures.
Machine learning and deep learning allow computers to learn by doing in the same way that humans do. Deep learning is an integral part of self-driving cars because it will enable them to identify stop signs and distinguish between pedestrians and lampposts. Voice control is available on consumer gadgets such as phones, tablets, televisions, and hands-free speakers. Deep learning has received a lot of attention recently, and for a good reason. It’s achieving previously unattainable goals.
A computer model learns to categorize tasks directly from images, text, or sound using deep learning. Many labeled data and multilayer neural network topologies are used to train models. Deep learning models can achieve cutting-edge accuracy, beating humans in some cases.
Securing systems, networks, and programs from digital intrusions is cyber security. These hacks are usually aimed at gaining access to, changing, or deleting sensitive data, extorting money from users, or disrupting normal business activities.
Because there are more devices than humans nowadays, and attackers are growing more inventive, while effective cyber security measures are strict.
Cyber attacks that attempt to access, modify, or delete data, extort money from customers or the company, or disrupt routine business activities are examples of cybercrime. A comprehensive cyber security plan has layers of protection to guard against cyber crime. Countermeasures should address the following issues:
Critical Infrastructure Security
Cyberattacks on vital infrastructure have become more complex and disruptive, causing systems to go offline, disrupting operations, or allowing attackers to control the affected systems remotely.
Control systems have traditionally been isolated from the open internet by being implemented on air-gapped networks and under strict physical protection. The growth of the Internet of Things has exposed those networks to cybercriminals, reducing staffing and operating costs by allowing remote control and management of intelligent valves and smart meters from anywhere.
By removing air-gap protection in efficiency and cost-cutting, critical infrastructures have become vulnerable to threats and cyberattacks.
Network security refers to the security measures used to keep intruders out of a computer network, including wired and wireless (Wi-Fi) connections.
Processes for securing both on-premises and cloud-based apps. Security should be considered during the design stage of apps, with concerns about how data is handled, user authentication, etc.
Specifically, genuine confidential computing supports consumer privacy, business requirements, and regulatory compliance standards by encrypting cloud data at rest (in storage), in motion (as it travels to, from, and within the cloud), and in use (during processing).
Data protection procedures, such as the GDPR, protect your most sensitive data from illegal access, disclosure, or theft.
To improve endpoint security, raise security awareness throughout the enterprise. Users can be taught to remove questionable email attachments, avoid utilizing unfamiliar USB devices, etc.
Disaster Recovery/Business Continuity Planning
Tools and processes for dealing with unanticipated occurrences, including natural catastrophes, power outages, and cyber security incidents, with little disruption to vital operations.
Storage security is the fusion of storage, networking, and security disciplines, technologies, and approaches for the protection and security of digital assets.
The physical, technological, and administrative controls and the preventative, investigative, and remedial controls connected with storage systems and infrastructure are the core emphasis of storage security.
A concentrated effort within this layer of ICT is required to ensure proper confidentiality, integrity, and availability of data stored and accessible on present and emerging storage systems (Information and communications technology).
Despite the efforts of cyber security specialists to plug security breaches, attackers are continually looking for new ways to avoid detection by IT, bypass protection measures, and exploit new vulnerabilities. The latest cyber security risks are using work-from-home environments, remote access technologies, and new cloud services to put a new twist on “well-known” dangers. Some of the evolving threats are as follows:
Worms, viruses, Trojan horses, and spyware are examples of malicious software that provide illegal access to a computer or cause damage to it. Malware attacks are becoming increasingly “lifeless” and are designed to avoid detection technologies that scan for harmful file attachments, such as antivirus software.
Ransomware is a virus that encrypts files, data, or computers and threatens to delete or destroy the data unless a ransom is paid to the hackers who began the attack. Recent ransomware attacks have targeted state and municipal governments, which are easier to hack than businesses and are under pressure to pay ransom to restore critical apps and websites that citizens rely on.
Phishing is a social engineering technique where people are duped into divulging personal or sensitive information. Phishing scams solicit personal information such as credit card numbers or login passwords via emails or text messages that appear to be from a respectable company. The FBI has reported a rise in pandemic-related phishing, which they attribute to increased remote work.
A computer model learns to categorize tasks directly from images, text, or sound using deep learning. Traditional security solutions such as firewalls and intrusion detection systems, which focus on external threats, may miss insider risks.
Distributed Denial-Of-Service (DDoS) Attacks
A DDoS assault overloads a server, website, or network with traffic, usually from numerous synchronized systems, to bring it down. DDoS assaults use the simple network management protocol (SNMP) used by modems, printers, switches, routers, and servers to overwhelm enterprise networks.
Advanced Persistent Threats (APTs)
An APT occurs when an attacker or a group of intruders get access to a system and remain undetected for an extended period of time. The intruder leaves networks and systems untouched to spy on company activities and collect essential data while evading the activation of defensive countermeasures. An example of an APT is the recent Solar Wind penetration of US federal computers.
A man-in-the-middle attack is an eavesdropping attack in which cyber criminal intercepts and distributes messages between two parties in order to steal data. On an insecure Wi-Fi network, an attacker, for example, can intercept data passing between a guest’s device and the network.
Now that we’ve gone through some of the most frequent cyber risks and attacks, it’s time to look at the role of Deep learning applications in cyber security
Deep learning, convolutional neural networks, and Recurrent Neural Networks (RNNs) can all be used to make more innovative ID/IP systems by analyzing traffic more accurately, minimizing false alerts, and assisting security teams in distinguishing between harmful and good network activities. Next-Generation Firewalls (NGFW), Web Application Firewalls (WAF), and User Entity and Behavior Analytics are examples of unique solutions (UEBA).
Traditional malware solutions, such as standard firewalls, use a signature-based detection technique to detect malware. The company maintains a database of known risks, which is regularly updated to include new threats recently introduced. While this method is effective against primary dangers, it has difficulty dealing with more advanced threats. Deep learning systems can detect increasingly advanced threats without relying on established signatures or attack patterns. Instead, they become familiar with the design and can see odd behavior that could suggest the existence of criminal actors or malware.
Natural language processing (NLP), a deep learning approach, can aid in detecting and managing spam and other forms of social engineering. NLP employs statistical models to detect and block spam by learning standard forms of communication and language patterns.
Learning at a deeper level in monitoring HTTPS network data to look for malicious activities, ANNs show promising results. This effectively deals with various cyber dangers, such as SQL injections and denial-of-service assaults.
Any organization’s deep learning-based security methodology should include tracking and analyzing user activities and behaviors. Because it bypasses security safeguards and frequently does not raise any signals or alerts, it is significantly more challenging to detect than traditional harmful activity against networks. UEBA (User and Entity Behavior Analytics) is an excellent defense against such attacks. After training, it can see standard employee activity patterns and questionable activities, such as accessing the system at odd hours, that could signal an insider attack and trigger alerts.
To avoid any form of hack, it’s critical to keep a check on employees’ official email accounts. Phishing attacks, for example, are frequently carried out by sending emails to employees requesting essential information. To avert this kind of assault, cyber security software and deep learning can be utilized. Emails can also be scanned for any suspicious behavior using natural language processing.
Deep learning is already commonplace on mobile devices, and mobile assistants drive voice-based experiences. When an organization wants to prevent the growing amount of malware on mobile devices, deep learning can be used to discover and analyze threats against mobile endpoints.
In cyber security, deep learning can aid people in detecting malicious attacks, endpoint protection, network analysis, and vulnerability assessments. Humans can make better decisions by identifying ways and means to solve challenges.
Deep learning’s key value is that it can automate monotonous jobs, allowing employees to focus on more essential tasks. A few cyber security professions can be automated using machine learning. Organizations can do activities faster and better by adding deep understanding to the processes.
WebShell is a malicious code put into a website to grant access to make changes to the server’s Webroot. As a result, the database is accessible to intruders. Deep learning can aid in the detection of regular shopping cart activity, and the model can be trained to distinguish between the two.
Deep learning can evaluate prior cyber-attack datasets and determine which network parts were targeted. This can aid in preventing an attack in a specific network area.
Automation is crucial for protecting enterprises against a vast volume of threats. Still, traditional machine learning is too limiting, requiring too much tuning and human interaction to achieve the desired outcomes. Deep understanding takes things a step further by continuing to evolve and learn over time, allowing it to recognize and block threats it hasn’t seen before.
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