Security teams are drowning in false alarms. The fix is not more alerts, it is fewer wrong ones

Ask anyone who has worked a shift in a security operations centre what the job actually feels like, and the answer is rarely dramatic. It is a queue. Alerts arrive faster than anyone can work them, most turn out to be nothing, and the analyst's real skill becomes deciding what to ignore. The uncomfortable part is that the one alert that mattered was probably in there somewhere.

Dr. Naga Venkata Aswini Pavan Kumar Inguva calls this the quality problem, and he thinks the industry has been solving for the wrong variable. "Effective cybersecurity depends on context, not simply the number of alerts generated," he said. "Organizations should prioritize technologies that improve the quality of threat detection rather than simply increasing the volume of security notifications."

That distinction matters because the instinct when something is missed is almost always to add more detection. More sensors, more rules, more coverage. Each addition is defensible on its own and the cumulative effect is an analyst who trusts the queue less than they did before. Alert fatigue is not a personal failing. It is what happens when a system is tuned to never miss anything and nobody accounts for the cost of being wrong

Inguva holds a Ph.D. in Information Technology from the University of the Cumberlands and a master's in computer science, and has spent more than seven years in enterprise technology in the United States after teaching as a professor in India. His research focuses on applying machine learning to intrusion detection, and the false-positive question is the one he keeps returning to.

The reason it has resisted a clean fix is that the two goals pull against each other. Tighten a detector until it stops crying wolf and it starts missing real intrusions. Loosen it until it catches everything and the analyst drowns. "Balancing high detection capability with a low false-positive rate remains one of the most persistent challenges in cybersecurity research," Inguva said. There is a second complication underneath that one. Machine learning models that perform impressively in a particular environment often degrade badly in a different one, because they learned the shape of that first network rather than the shape of an attack.

His proposed response is a framework he calls the Hybrid Weighted Ensemble Intrusion Detection Framework. The reasoning behind it is that no single model should be trusted to make the call alone. Instead, several models assess the same activity, their outputs are weighted rather than simply counted, and the result carries a confidence score rather than a binary verdict. Alerts are then prioritised, so what reaches the analyst first is what the system is most confident about and most concerned by.

It is worth being clear about what this is. The framework is a conceptual research architecture, not a deployed product. Inguva designed the architecture, assessed existing ensemble learning approaches against the problem, and proposed the confidence scoring and alert prioritisation concepts, producing a roadmap for future validation and implementation. The contribution is the design and the argument, and he is candid that the testing comes next.

The design does answer a real operational complaint, though. A queue sorted by confidence is a different working environment from a queue sorted by arrival time. It gives the analyst a defensible reason to start at the top.

Inguva is equally interested in the gap that swallows a lot of promising security research. Methods that perform well in a paper frequently fail to reach production, not because the mathematics is wrong but because organisations need something that scales, explains itself and fits the workflow people already use. He builds those constraints into the framework rather than treating them as somebody else's problem, which is unsurprising given that he works as a senior software developer for a US state government agency, modernising enterprise applications including telecommunications and network systems, and co-founded Thaapasi Smart Infratech Pvt. Ltd.

He is firm that better tooling is not the whole answer, and pushes back on a common assumption. "A common misconception is that cybersecurity can be achieved solely through advanced software or hardware solutions," he said. "In reality, effective cybersecurity depends on a balanced combination of technology, governance, skilled professionals, continuous improvement, and organizational commitment." Cybersecurity, in his framing, is as much a people, process and governance challenge as a technical one.

That view shapes where he thinks artificial intelligence belongs in the operations centre. Not in the analyst's chair. "AI should be viewed as a decision-support capability that augments human expertise, allowing analysts to focus on complex investigations and strategic decision-making," he said.

His broader research record runs alongside this work, including published research on threat detection using generative adversarial networks, deep learning for network threat detection, and machine learning approaches to classifying attacks, some of which received best paper awards.

The ambition behind the framework is modest in a way that is easy to underrate. It does not promise to catch more. It proposes to be wrong less often, and to be honest about how sure it is. For an analyst three hours into a queue, that is the more useful promise.