The Future of Intrusion Detection

In 2013, networks had to contend with 80 million malware threats. In 2014, the number of new threats rose to 140 million. Furthermore, as recent attacks by hacker organizations Equation Group and Carbanak demonstrate, the new influx of malware is as undetectable as ever. Clearly, the proliferation of cyber threats shows no sign of abating, and strongholds of sensitive data must contend with them. Given the exponential growth of intelligent malware, this is a difficult task, but not impossible. The key is adaptable, flexible detection and prevention technology that uses predictive intelligence and advanced computer algorithms.

The incorporation of artificial intelligence (AI) technology will most likely be the prevailing trend in intrusion detection and prevention. AI programs can scan networks extensively and respond quickly to threats, as well as adapt themselves to shifting attack tactics, even if said tactics are designed to be “undetectable.” Future malware programs will likely incorporate AI as well, so the investment of time and money in programs capable of computer behavioral analysis and other AI traits is essential.

AI will also allow organizations to prevent attacks before they happen. According to Rob Enderle at CIO.com, most organizations are investing more time and money into offensive anti-malware tools than they are investing into defensive ones. This, says Jon Oltsik at Network World, is a mistake, and he cautions against putting all of an organization’s resources into detection and response, if such a strategy waylays the development of a solid preventative system. In order to establish dynamic, advanced prevention against malicious attacks, organizations need to invest in defenses that are proactive and self-learning—two traits that the incorporation of AI makes possible.

Some defense systems that are currently on the market are already incorporating aspects of AI into their programming. One is Bitdefender, an internet security package that uses machine-learning algorithms in order to adapt its defenses as it is exposed to data in the form of security threats. Instead of following a predetermined operating procedure, Bitdefender operates on predictive modeling, which uses data input to make predictions and decisions and detect patterns to learn how threats behave and what they will look like in the future. Another defense system that uses advanced algorithms to detect and prevent intrusions is Palo Alto Networks’ PAN-OS 7.0, which recently made enhancements to its security platform. The PAN-OS-7.0 has expanded prevention capabilities that are designed to address increasingly complex security threats. The platform addresses the rapidly multiplying number of malware threats with automated threat identification and prevention. Automated file analysis allows it to identify what the malware is targeting and prevent a related breach, while the platform’s pattern identification capabilities recognize malware’s behavior as a threat. The platform also features threat level classification to prioritize malware management and simplify managing the deluge of threats.

Bitdefender and the PAN-OS 7.0 are only two examples of defense systems that have caught on to the necessity of advanced algorithms and preventative strategies. As AI technology advances, so too will our capability for anticipating the malware of the future and preventing malicious breaches. Capsicum Group offers a suite of proactive cybersecurity services, including penetration testing, ethical hacking, malware simulations, network security management, and technical consulting. Schedule a meeting to review your cybersecurity concerns with Capsicum experts today.