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Applications of AI in Cybercrime

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In this cyber age, the role of Artificial Intelligence is often undermined in terms of its application in cybercrime. In this brief article, let’s have a look at how Artificial Intelligence can be used to fighting cybercrime.

Globally, cases of cybercrime is growing exponentially and in the never ending fight the law enforcement and government agencies have to adopt the latest technologies to stay ahead of cyber criminals. As new cyber threats are encountered by law enforcement agencies, it is imperative to bring the reforms in the investigations procedures.

In this digital and cyber age, the applications of Artificial Intelligence (AI), Machine Learning (ML), deep learning, is often not utilized in combating cybercrime. Adopting technologies like AI, ML and deep learning process will never compromise the accuracy or effectiveness of the investigation measures besides it helps to augment the role of a human in bringing the cyber criminals into justice.

Some of the latest cases in cybercrime involves in the usage of hacking and malware, botnets and Distributed Denial of Service (DDoS) attacks by organized cybercriminals. The challenges for cyber police and security experts in tracking and identifying the insider threat factor and the uncertainties surrounding the increasingly connected IoT, further complicates the task of protecting the business internally and externally.

Process Model
One of interesting models where the applications of AI and ML in combating cybercrimes, is the Defense Advanced Research Projects Agency (DARPA) in the US. DARPA has implemented and deployed automated systems fielding a generation of machines with potent algorithms that can discover, prove and fix software flaws in real-time without human intervention. The success of this technology model has further highlighted the measures taken by the government and security agencies in adopting technology of the future.

Evolving from a traditional rule-based system, security experts have employed machine-learning techniques, drawing on data insight to identify patterns and apply machine-readable context to events. It is through this precise technology that businesses have used to analyze big data sets. In the security perspective, machine learning systems use anomaly detection. In simple terms, this means that a default model is defined and classified as ‘normal’. If an outlier is detected that varies from the default model, it is considered to be an anomaly. The crucial factor here is that ‘normal’ model is not static and as more and more data is added into it the ‘normal’ behavior evolves into a real environment which is accurate and always updated. Complex behavioral patterns can be derived if unknown threats are assessed against the ‘normal’ model.

In the current scenario, Artificial Intelligence will perhaps remain out of reach for many due to the large investment, skills shortage, immense volume of data, trained models and the large processing capacity are still required.

Conclusion
In response to the growing cyber threat, the cyber detection capabilities of law enforcement agencies relating to cybercrimes should be constantly refined, improved and fully deployed.

As the current security threats are identified, managed and mitigated, the automated threat and risk management method of using Artificial Intelligence and Machine Learning becomes a reality.

In coming years, the implementation of Artificial Intelligence, Machine Learning, deep learning, by the government and security agencies will be utilized in combating cybercrime. This is the only way by which the government agencies can stay ahead of cyber-criminals.

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