Machine Learning Transforms Threat Detection

Organisations and business are being swamped by billions of attempted cyber hacks on a daily basis, which is overwhelming human analysis, however, Machine Learning (ML) is now tipping the advantage toward defenders as it can significantly improve cyber threat detection and prevent threats. ML can manage many information sources and super-correlate information in the millions, billions and trillions daily.

ML comprehends threats in real time, understands the infrastructure of a company and its network design and attack vectors, and protects and defends with human talent and machine power. The algorithm is capable of massive amounts of data mining and these machines don’t stop whereas humans need breaks and sleep.

Improved Detection

An algorithm can learn from its mistakes on the fly. This allows it to always be on its A game. It's always the best version of itself because it's always improving its game. A good ML discipline is one that can "see" patterns of behavior, guessing the form of an attack and how to fight back. The algorithm can be trained with different types of attacks, can learn the methods to gain privileged access and lateral movements, and can even adapt in real time to a situation. An excellent ML approach can learn from false positives.

False positives will always exist, but they're reduced with each interaction with an algorithm because the machine is continuously learning. After implementing an ML system, false positives can be reduced by 50% to 90%.

While ML decreases false positives, it can increase the speed at which threats are detected. That can dramatically shrink the window of compromise for a system. ML detect threats quickly known and unknown threats with unsupervised and reinforced learning. That's why in the chess game between adversary and defender, once an attacker makes a move, all the outcomes from that move can be determined through ML and flagged or blocked.

Cyber Criminals Can Use ML Too

Cyber criminals realise that they can use ML to automate their attacks and eliminate most human intervention. They can write an algorithm, train it with a pattern of attack, and, while the machine is running its sorties, can kick back with a martini by the pool.

That's why defenders need to use ML at every attack vector, at the gateways, at the endpoints, in the cloud, because if there's a gap in a system's defenses, an adversary's ML algorithm will find it. The new cyber-criminal isn't some kid in a dark basement with a computer. It's often a criminal group that's using ML to launch large-scale attacks on thousands of companies at the click of a virtual button.

The Human Factor

Skilled human analysts are sill needed to confirm some actions, make final decisions, and identify exceptions. But with over a million cybersecurity jobs vacancies worldwide, there aren't enough analysts to go around. The large majority of tasks security analysts are being saddled with now is triage work, sorting through threats to find those that need further scrutiny. Fortunately, that kind of work can be done with ML in an effective and efficient way, freeing up analysts' time to address serious threats.

The COVID-19 pandemic has accelerated this shift from off-line processes towards on-line across organisational functions, whether they are corporate, government, or non-profit organisations. Consequently, enterprises have witnessed a significant growth in data and information generated during this pandemic period.

Organisations, particularly in the financial services sector, are investing significantly in Blockchain technology to prepare for the future. Blockchain could become one of the game-changers for the entire world.

The implementation of Artificial Intelligence (AI) and Machine Learning (ML) systems may serve as a solution, bringing with them many benefits in helping to prepare the cyber-security workforce of tomorrow. Currently, the technology’s ability is simple, yet is still of great benefit, in that human staff are freed up to focus on more complex threats, with the AI/ML shield in place to deal with the high volume of more low-level attacks.

Splunk:        TechBeacon:       Enterprise Talk:         Enterprise Talk:      Forrester:      

You Might Also Read: 

AI, Machine Learning & Deep Learning… Whats The Difference?: