Cybercriminals are continuously looking for new hacking strategies. One of the best ways to avoid the rising tide of cyber threats relies on the awareness and deep understanding of the trending technologies. The advancement in technology brings the revolutionary changes with the introduction of machine learning, artificial intelligence and other analytical approaches in enhancing malicious activities of hackers. Few ML enabled suspicious activities involves:
Hackers write thousands of line of scripts manually to develop a trojan, scrapers or viruses for their distribution and execution over the network. The only question arises is that what will happen if they pace up malware creation process with the leverage of automation with machine learning. In the year 2015, a total of 230,000 new malware script samples were collected daily, which is a huge figure. What if machine learning filled the gap between development and execution of such scripts, the picture will rise exponentially. In the year 2017, research published from Cornell University revealed the algorithm to bypass machine learning based detection systems. The method uses the generative adversarial network (GAN) implemented programs to generate the adversarial malware samples for the attack.
Hackers are focused towards staying as long as they can hide in the victim’s system. Machine learning provides a path to flew away without getting identified under the radar. Security professional suggests that machine learning holds the capability to modify the malware samples codes by detecting flaws from previous codes. This way the newly modified malware will not fall under the security surveillance of the victims.
It’s believed that soon machines will be capable of talking to each other for performing actions. These expert systems will take decisions smartly without following any instructional command from the botnet header. In the past few years, the development of predictive analytics based software system with advanced tools using swarm technology analyses billions of data to present accurate outcomes. Recent studies conducted by Fortinet cyber threat researcher shows that the smart botnets are used to perform repeated attacks on the vulnerability found in Apache Struts framework causing the Equifax hack. Hackers picked automated and intelligent decisions to break the vulnerabilities in a system.
As concluded from the above explanations, hivenets are growing drastically, expanding their ability to attack various users and hinder response. Swarm technology enables hivenets with self-learning capability from past behaviour.
Strengthening Brute Force Technique:
The brute force technique used by hackers guesses for the victim’s password. Machine learning can also be used to enhance the brute force algorithm. The neural network’s ability to generate texts from trained texts can widely applicable in such scenarios. Researchers from MIT followed the same approach for generating a password and collected satisfying results. Apart from academics, this idea is promising for the cyber attackers too, as a recent report collected by 4IQ suggested for the existence of around 1.4 billion passwords database on the dark web from all cyber attacks.
Use of AI can be widely seen in voice or face recognition, assistance etc., these days few companies are creating fake voice samples and videos which can mimic the voice of any person. As we know most of the companies are working hard on the technology to achieve the maximised security with biometric authentication techniques, but at the same time, intruders are also busy in generating fake audios to penetrate the security systems.
Machine learning avails various platforms to create automated applications such as voice recognition, assistance, chat-bots etc., over the internet. One can enrol in Machine Learning Course to build their career in trending AI technology and walk with the knowledge of its practical implementation in various fields for simplifying the process.
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