Stopping digital attacks before they happen may seem an impossible task, but the looming threat of zero-day security breaches are rising at a nearly exponential pace. Gone are the days when a worst-case scenario of a malware infection meant a reformatted hard drive in a worst case scenario now that ransomware has reared its ugly head. 

In an unfortunate double-edged sword of computational complexity, zero-day methods for attacking systems are becoming more refined and difficult to detect while computer systems become more complex with each operating system update and change in networking standards. Manually analyzing a system for irregularities is a nigh impossible task. The demand for software that can accurately predict changes in attack methods while eliminating as many false positives as possible could be more important for the coming years than nearly any other digital security goal on the agenda. 

Zero-day attacks: An unseen danger

For evidence of the havoc, a zero-day attack can wreak one needs look no further than the attack and the nearly $4 billion in damages it caused globally. 

The crux of the problem lies in how the exploit was detected yet still managed to propagate despite attempts to circumvent its deployment. instead opting to keep the exploit for potential future use of their own. By March of 2017, Microsoft discovered the weakness in their EternalBlue system that could lead to potential breaches and issued emergency security patches for Windows accordingly. 

Unfortunately, many users did not properly update their operating systems leading to a full attack in May of 2017 that affected an estimated 200,000 machines. Most notably, the attack caused significant damage to the National Health Service of the United Kingdom and decreased their ability to effectively respond to emergencies. 

Though WannaCry was a known threat, it exploited a known zero-day vulnerability that deeply compromised systems, encrypting documents and demanding a ransom for their safe return that often never came. Detecting zero-day exploits of this nature is an outright national security risk as the very same technology could have targeted computers more vital than those running the NHS.

Creation of zero-day exploits

One of the greatest issues with potential exploits is that they might be determined through entirely automated processes. While an attack surface analysis requires software engineers to have a working knowledge of the program and a deep understanding of its internal systems, fuzz testing is a much more dangerous and unpredictable method of securing an exploit. 

By randomly modifying a program and observing how it reacts, programs are able to generate likely paths of attack that can be then bundled into a deployment technique that is protected by a metamorphic or polymorphic defense technique. Metamorphic defenses attempt to change the software in a way that makes it difficult for a human to decipher but may be more vulnerable to a machine-automated fix. Polymorphic protection, on the other hand, combines the actual payload of the exploit in with its encryption method that makes it much more difficult to detect prematurely. 

Machine learning solutions

Signature-based and statistics-based detection methods, though effective for well-known security dangers, are of little use to detecting zero-day attacks. Both methods depend on methods that require data collection and databases of information that make them limited in scope when it comes to anticipating changes in attack methodology. 

Behavior-based detection breaks away from mass data collection and instead focuses on the way programs interact with one another to determine if a program's actions are intended or the result of a change in function. By running on a target computer for long periods of time, behavior-based detection can build a profile based on the machine's performance and accurately predict how its programs are intended to run. 

By combining these three methods in various ways it is possible to develop hybrid-based detection methods that mix established database examples with machine-learning driven monitoring to effectively combat threats. From that point on it is a matter of knowing how to block weak endpoints in user software and systems after monitoring usual computer behavior and developing databases of how known software should be operating. 

The bottom line for zero-day security is that its detection and countermeasures are difficult to develop quickly yet hold dire importance in the field of cybersecurity. With projections for security data suggesting a new zero-day threat may emerge on a daily basis by 2022, the road to future security begins with present defenses and proper research.