Predictive analytics by means of machine learning techniques and mathematical algorithms utilises historical data to predict future outputs. Popularity of predictive analytics has soared among healthcare, financial and marketing industries. Large volumes of data processed by applying complex algorithms produce patterns which abet decision making.
Implementing predictive analytics in the field of cyber security can assist with early detection and mitigation of cyber threats. In addition, predictive analytics provides the insights necessary for strategic cyber security planning. By providing the ability to analyse historic data, predictive models assist in identifying potential threats. As a consequence, the probability of anomaly detection increases.
Predictive analytics in cyber security relies on strong data sources. System and network logs provide the foundational data for detecting anomalies and threats. Network traffic data in the form of payloads and packet headers provide comparative data for discovering normal and abnormal behaviour. While, system logs present recorded evidence on application and operating systems activities.
Data pre-processing guarantees predictive models receive high quality input through data normalisation which involves removing duplicates and irrelevant information. By doing so, model performance and accuracy improves.
A prevailing example of predictive analytics usage involves Intrusion Detection Systems (IDS). Network-based IDS exercise the power of consuming machine learning resources for spotting deviation in traffic patterns. Additionally, analysis of user behaviour assists in detecting abnormal patterns. For instance, if a user account only has activity during daytime hours, yet an unexpected surge of activity transpires during the nightfall. In such a case, the detection of an unusual anomaly would represent foul play.
Challenges with predictive analytics
Ethical and privacy challenges arise with predictive analytics. In connection with handling sensitive data, organisations must ensure stringent policies to prevent unauthorised access and misuse of information. Furthermore, establishing a balance between predictive analytics and user privacy requires due thought.
Compliance with ethical standards and data privacy laws prevents legal repercussions and enhances public trust. Aside from the legal and privacy challenges, technical challenges related to handling large data sets pose problems for implementing predictive analytics in the cyber security field. Considering that, effective predictive analytics relies heavily on large data sets, yet in cyber security providing a dataset with diverse range of threat scenarios has its own setbacks. Inevitably, inadequate or biased data leads to ineffective models, wherefore shrinking their reliability levels.
A second technical challenge of model interpretability exists when implementing predictive analytics in cyber security. Model interpretability problems cause advanced machine learning algorithms such as deep neural networks to function as ‘black-boxes’. As a consequence, model decisions lack opacity. On that account, lack of interpretability obstructs trust and understanding in the models’ predictions for high importance security applications whereby transparency has a vital part to play.
The third technical challenge of implementing predictive analytics in cyber security focuses on the dynamic nature of cyber threats. Given that, cyber threats continue to evolve with attackers adopting emerging technologies and strategies. Thus, an adaptive nature for a predictive model materialises as an essential requirement. However, this process requires updating and retraining the model frequently, thus guaranteeing model accuracy and relevance in real-time remains challenging.
False positive and false negatives present the ultimate technical challenge in terms of implementing predictive analytics with cyber security. An example of a false positive links to when a predictive model marks a genuine activity as a cyber threat incorrectly. In reaction, to consistently responding against false positive threats, cyber security teams may develop ‘alert fatigue’, whereby a culture of overlooking or ignoring warnings grows, leading to a major cyber incident. Consequently, trust in security systems erodes and operations costs increased.
On the other hand, an example of a false negative corresponds to the predictive model failing to detect an actual cyber threat. Undetected breaches result in zero interventions to stop the malicious activity. Subsequently, severe consequences of missed breaches lead to data loss and reputational damage.
Countering the technical challenges with predictive models in cyber security requires extensive research and development work. Over and above that, the focus of anyone implementing predictive models must rest on ensuring data privacy, minimising negative consequences of inaccurate predictions and overcoming technical hurdles to lucratively leverage predictive analytics for strengthening cyber security.
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