How to apply natural language processing to cybersecurity

It’s built into state-of-the-art NLP models like Google’s BERT and OpenAI’s GPT-3. Despite the attention mechanism’s rapid adoption into NLP models, it’s not without cost. By analyzing logs, messages and alerts, NLP can identify valuable information and compile it into a coherent incident report. It captures essential details like the nature of the threat, affected systems and recommended actions, saving valuable time for cybersecurity teams.

Researchers’ new hardware and software system streamlines state-of-the-art sentence analysis

Algorithms based on distributional semantics have been largely responsible for the recent breakthroughs in NLP. They use machine learning to process text, finding patterns by essentially counting how often and how closely words are used in relation to one another. The resultant models can then use those patterns to construct complete sentences or paragraphs, and power things like autocomplete or other predictive text systems. Algorithms based on frame semantics use a set of rules or lots of labeled training data to learn to deconstruct sentences. This makes them particularly good at parsing simple commands—and thus useful for chatbots or voice assistants.

Why NLP matters in cybersecurity

Han says SpAtten’s focus on efficiency and redundancy removal is the way forward in NLP research. “Human brains are sparsely activated by key words. NLP models that are sparsely activated will be promising in the future,” he says. “Not all words are equal — pay attention only to the important ones.” NLP offers many benefits that can revolutionize cybersecurity efforts.

The algorithms provide an edge in data analysis and threat detection by turning vague indicators into actionable insights. NLP can sift through noise to pinpoint real threats, improving response times and reducing the likelihood of false positives. Natural language processing (NLP) is the branch of artificial intelligence (AI) that deals with training computers to understand, process, and generate language.

This innovative technology enhances traditional cybersecurity methods, offering intelligent data analysis and threat identification. As digital interactions evolve, NLP is an indispensable tool in fortifying cybersecurity measures. Cybersecurity is imperative in the modern digital landscape.

The attention mechanism’s accuracy often comes at the expense of speed and computing power, however. It runs slowly on general-purpose processors like you might find in consumer-grade computers. So, MIT researchers have designed a combined software-hardware system, dubbed SpAtten, specialized to run the attention mechanism. SpAtten enables more streamlined NLP with less computing power. Transformer models take applications such as language translation and chatbots to a new level. Innovations such as the self-attention mechanism and multi-head attention enable these models to better weigh the importance of various parts of the input, and to process those parts in parallel rather than sequentially.

• Innovations such as the self-attention mechanism and multi-head attention enable these models to better weigh the importance of various parts of the input, and to process those parts in parallel rather than sequentially.
• An NLP algorithm uses this data to find patterns and extrapolate what comes next.
• Simply put, NLP cuts down the time between threat detection and response, giving organizations a distinct advantage in a field where every second counts.
• So, MIT researchers have designed a combined software-hardware system, dubbed SpAtten, specialized to run the attention mechanism.

Search engines, machine translation services, and voice assistants are all powered by the technology. The researchers think SpAtten could be useful to companies that employ NLP models for the majority of their artificial intelligence workloads. “Our vision for the future is that new algorithms and hardware that remove the redundancy in languages will reduce cost and save on the power budget for data center NLP workloads” says Wang. NLP algorithms can scan vast amounts of social media data, flagging relevant conversations or posts. These might include coded language, threats or the discussion of hacking methods. By quickly sorting through the noise, NLP delivers targeted intelligence cybersecurity professionals can act upon.

The models they produce don’t actually understand the sentences they construct. At the end of the day, they’re writing prose using word associations. While the impressive results are a remarkable leap beyond what existing language models have achieved, the technique involved isn’t exactly new.

The attention mechanism also includes multiple computation branches (called heads). Similar to tokens, the unimportant heads are identified and pruned away. Once dispatched, the extraneous tokens and heads don’t factor into the algorithm’s downstream calculations, reducing both computational load and memory access. Natural-language processing (NLP) algorithms are now able to generate protein sequences and predict virus mutations, including key changes that help the coronavirus evade the immune system. Alongside these software advances, the researchers also developed a hardware architecture specialized to run SpAtten and the attention mechanism while minimizing memory access. The design enables SpAtten to rank the importance of tokens and heads (for potential pruning) in a small number of computer clock cycles.