The security industry is rife with data protection challenges. It faces catastrophic cyberattacks. And the troubles continue to mount with the rise of web-connected devices. Adding salt to the injury, is the shortage of skilled cyber talent, which fail to avert the burgeoning stress. With snowballing disruption, a prominent branch of technology development that shows promising signs to alleviate the security risks is machine learning. It has opened a new realm of data protection by leveraging the power of data and automation.
Machine learning is a branch of artificial intelligence (AI) and works on the principles of human emulation — learning from experience and patterns much like humans, but without the interference of humans. The technology has greatly grown in the past five years. The evolution can be attributed to a host of reasons. Including smart hardware, distributed computing, and the Cloud. Google, Facebook, and Amazon are already stealing ahead on the path of machine learning innovation. They enable smart search engines, dynamic news feeds, and unerring product recommendations respectively.
Machine learning: disruption sweeping the security landscape
Back in the day, companies relied static, rule-based engines to identify behavioral patterns that indicate looming threats. However, these indications often fell off the mark. The success rate was low. With advancements in machine learning, companies are empowered with data deception technology and real-time anomaly handling that leave no scope for guesswork and manual intervention. Subsequently, the reaction to breaches has also smartened up.
The process is seeped into the essentials of big data. Organizations integrate a pool of behavioral data from various sources. This consumer data is cleaned, organized, and analyzed for insights that show anomalies (dangers at hand) and alert authorities to act. When smart personnel meets smarter technology that transforms over time, a unique springboard of opportunity is created, facilitating defenders to combat cyberattacks effectively.
Machine learning as a security net
- Swift detection of malicious activities
- Machine Learning algorithms are instrumental in detecting malicious activities swiftly and mitigating them in a time-bound manner. The vulnerabilities are diagnosed within seconds and eliminated on the fly before they can unleash damage to the organization.
- Analysis of mobile touch-points
- Machine learning is coming to mobile phones with a great tempo, and the amalgam is galvanizing a new era of information security. While the most important application remains the image recognition systems and voice-based experiences powered by Google Assistant, Amazon’s Alexa, and Apple’s Siri, the future possibilities are endless and unprecedented. Machine learning, coupled with AI and the Internet of Things (IoT), collects data and leverages the value of it to enable devices that function on their own and take informed decisions. Subject detection by smart cameras and translating languages in real-time are other significant applications.
- Automated redundant tasks
- Automating repetitive tasks is the cornerstone benefit of every new-age digital technology gracing the humankind, and Machine learning is no exception. According to the team at techiespad, ML-based algorithms can take up tactical fights against mundane security issues, thereby freeing time for other strategic priorities. This is a strong step towards intelligent resource allocation and improved productive capacity.
- Improved human analysis
- Machine learning enhances and consolidates human analysis to an unmatched level. Humans are empowered to detect, analyze, and mitigate threats with greater quality and precision across an array of aspects that includes network analysis, endpoint protection, and vulnerability assessment.
- Effective zero-day vulnerability closure
- Zero-day attacks appear like a bolt out of the blue, targeting unsecured IoT devices. And, use cases suggest that machine learning is an effective weapon to counter such attacks. The technology works by dodging vulnerabilities and restricting the patch exploits before they end up as data breaches.
The encircling dangers
Machine learning has its dangers too.
Attackers are deploying machine learning too with a view to achieving automated hacks that sabotage data security. The ‘bad actors’ are launching savvier attacks after studying and analyzing the machines on target and identifying vulnerabilities on the go.
Data tampering is another big roadblock to effective machine learning application. If attackers tamper with the source data, they manipulate the insights and trick systems into believing something that isn’t there. Attackers hatch out strategies to manipulate features of malware-detection code and eliminate its effectiveness, thereby rendering the algorithm powerless. As a result, malware codes pass on as clean codes. The hacking of data and turning it against itself is a security crisis that needs a speed fix. Building scalable systems that could accommodate large databases while adapting to human behavior in real-time is also a tightrope walk.
Commercializing machine learning is worrisome. Companies are capitalizing on the huge hype around the technology and selling products that create a false sense of security. The catchphrase here is ‘supervised learning’, which entails choosing and labeling databases that algorithms are trained on. The algorithms, as a result, become defensive against a cache of attacks, a certain few and known. Not all and anomalous.
And last but not least, it’s the language that acts as barrier to machine learning application. Remember how Facebook was forced to abandon its AI experiments when machines developed their own language, which were strange for the humans to interpret. The kind of deep learning applied there must be unimaginably powerful. Empowering machines to function on their own is the new, fascinating talk, but the implications could be ruinous if machine process text to create language humans can’t understand.
Machine learning is a strong stimulus, raising a new set of machine-accelerated humans and building best-of-the-breed security landscape. As of now, the technology is flourishing to help detect and obliterate security threats. In the future, a strong level of differentiation will be achieved when ML specialization becomes handy via breakthrough products. However, it’s important for companies to develop products while focusing on the need for monitoring and minimizing the risks associated. Mind that we are as strong as vulnerable in the face of fast-streaming digital renaissance. Enthusiastic hopping on the bandwagon can wobble the ride.