In the last decade, we have seen AI transitioning from an industry buzzword to finally being adopted across various enterprise applications. Indian businesses are analysing how they can make processes more efficient which has led to increasing adoption of artificial intelligence in the enterprise across different verticals. Products and services are being rebuilt with the integration of artificial intelligence with the objective of creating a better experience for end consumers. As enterprises are wary of getting left behind, this has driven the demand for professionals skilled in AI-based technologies. The trend is clear— professionals who are skilled in AI are being rewarded, along with a rising emphasis on upskilling keeping artificial intelligence in the centre. Read More
Monthly Archives: January 2020
Wanna Build an AI-powered Organization? Start by Getting EVERYONE to “Think Like A Data Scientist”
In a recent blog I stated that “Crossing the AI Chasm” is primarily an organizational and cultural challenge, not a technology challenge. That “Crossing the AI Chasm” not only requires organizational buy-in, but more importantly, necessitates creating a culture of adoption and continuous learning fueled at the front-lines of customer and/or operational engagement (see Figure 1).
A recent Harvard Business Review (HBR) article “Building the AI-Powered Organization” agrees that despite the promise of AI, many organizations’ efforts with it are falling short because of a failure by senior management to rewire the organization from the bottom up. Read More
How federated learning could shape the future of AI in a privacy-obsessed world
You may not have noticed, but two of the world’s most popular machine learning frameworks — TensorFlow and PyTorch — have taken steps in recent months toward privacy with solutions that incorporate federated learning.
Instead of gathering data in the cloud from users to train data sets, federated learning trains AI models on mobile devices in large batches, then transfers those learnings back to a global model without the need for data to leave the device. Read More
On Designing Machine Learning Models for Malicious Network Traffic Classification
Machine learning (ML) started to become widely deployed in cyber security settings for shortening the detection cycle of cyber attacks. To date, most ML-based systems are either proprietary or make specific choices of feature representations and machine learning models. The success of these techniques is difficult to assess as public benchmark datasets are currently unavailable. In this paper, we provide concrete guidelines and recommendations for using supervised ML in cyber security. As a case study, we consider the problem of botnet detection from network traffic data. Among our findings we highlight that: (1) feature representations should take into consideration attack characteristics; (2) ensemble models are well-suited to handle class imbalance; (3) the granularity of ground truth plays an important role in the success of these methods . Read More
How do you design ML models for malicious network detection?
Machine Learning (ML) has found its place into cybersecurity a long time ago and usage of ML has given cybersecurity teams much-needed insights into the malware network and effective ways to curb cyber attacks. Most ML-based solutions are proprietary or designed for specific feature representations.
…. Malicious attacks are the most dreaded cyber attacks. But, as the enterprises are collecting a large pool of data through their resources. These data sets are quite useful for machine learning models for the detection of malicious attacks and entities in the system.
ML techniques and models applied on the network data include systems for detecting malicious domains, methods for detecting malware delivery or command-and-control communication, techniques for detecting malicious web pages, and various industrial products for enterprise threat detection. Read More
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