Addressing the Cyber Defense Problem
Approaches in early cyber defense Intrusion Prevention Systems (IPS) and Intrusion Detection Systems (IDS) principles still have value today but they just need to get better. IPS encryption will have to change or it may become obsolete with the computing capabilities of the Quantum computer. This will require different algorithms that must have uncrackable encryption capabilities will having lower bit payloads capable of encrypting small processing systems such as IoT. Current IDS cannot keep up with malware treats now reaching 1 million threats per day. These systems will require new ways hardening data networks while being able to deep view both structured and unstructured data that are increasing every day.
ProjectSafety has put together a thee pronged cyber defense approach that can address the cost, limitations and inaccuracy of current cyber defense systems. Our unique combination of compression, cryptography and deep learning artificial intelligence have the capabilities of unhackable IPS systems with Deep Learning IDS abilities capable of detecting anomalies at the binary level. These combined IPS/IDS approaches offer network and business intelligence savings that are so great they could pay for the entire cyber defense system. These technologies are network.
True Random Encryption
Criminals can crack security when they can anticipate and predict the actions, behaviors, and outputs of their target’s processes. They can predict cyphertext, key size, or how keys and how passwords are communicated between end-points.
Uniquely True Random Engines used in authentication and encryption can address weaknesses in current static algorithms used in current cyber defense process hardening. Current legacy static encryption has a determinable beginning and end in file size and with the computing capabilities of complete algorithm formulas being determined. As computing power increases these deterministic static algorithms will continue to be broken as will their determinable static processes.
To address current legacy cyber defense security trustless technology solutions must be put in place to both human to machine and machine to machine digital processes. Security factors cannot be accessed or changed, by either error or malicious intent if they are not seen and randomly processed. All current legacy encryption keys have a “fixed” length. Fixed Keys can be identified, making them easier to crack. Super Computers are currently breaking many fix encryption keys with upcoming Quantum Computers capable of breaking all fixed encryption keys and mathematical algorithms. True random addresses crypto cracking even in a post Quantum world.
Deep Learning Networks
Deep Learning Networks
Microsegmintation
We have connected and interconnected so many digital processes that we have lost
system visualization and process event validation. A lack of digital system visibility offers hackers a wide open opportunity to exploit digital processes at will.
To combat digital process breaches, new security techniques have been deployed to segment, visualize and audit even the he most complex digital processes. This security technique is called Micro-segmentation. Micro-segmentation is a security technique that enables fine-grained security policies to be assigned to data center applications, down to the workload level. One major benefit of micro-segmentation is that it integrates security directly into a virtualized workload without requiring a hardware-based firewall.
Micro-segmentation offers a two fold value in operational efficiencies and security. Segmented system visibility offers specific operational views that can offer insights into improved process efficiencies while isolating
risk management targets. This methodology offers the capability of negating cyber defense costs through efficiencies gained in through micro-segmentation operational improvements. The cyber attack risk surface can also be reduced by deploying separate authentication, viewing and security solutions to the specified micro-segment.
Deep Leaning AI
Artificial intelligence (AI) is offering a deeper look in our system processes. As we do this we need to be aware of and even refine our security methodologies. Called Deep Learning, this deep look into our system processes offer tremendous intelligence capabilities but can be accessing and gathering information with no authentication or protection
Cyber Defense Artificial Intelligence (AI) is just a by-product of Deep Learning AI. Intrusion Detection Systems (IDS) define a baseline normal of industrial and business system processes. From this baseline you can then can find methods of detecting anomalies, duplication, undesired events or inefficient processes. How these baseline audits are done are critical to the Cyber Defense solution. Using software and algorithms alone can add to cyber-attacks and these methods of audit must be properly defined.
As we go deeper to identify digital processes, we must be sure we are securing the viewing of these Deep Learning capabilities. Algorithms and software alone have vulnerabilities that hackers have already identified. Simply put, AI can be hacked by AI. To defend against this capability techniques such as data randomization, pattern audit matching and processor bot duplication must be used for secure audit procedures and technologies. These technologies are available today.