In this paper, we present an empirical evaluation of an approach to predict attacker's activities based on information exchange and data mining. We gathered the cyber security alerts shared within the SABU platform, in which around 220,000 alerts from heterogeneous geographically distributed sensors (intrusion detection systems and honeypots) are shared every day. Subsequently, we used the methods of sequential rule mining to identify common attack patterns and to derive rules for predicting attacks. As we illustrate in this paper, a collaborative environment allows attack prediction in multiple dimensions. First, we can predict what will the attacker do next and when. Second, we can predict where will the attack hit, e.g., when an attacker is targeting several networks at once. In a week-long experiment, we processed in total over 1 million alerts, from which we mined predictive rules every day. Our findings show that most of the rules display stable values of support and confidence and, thus, can be used to predict cyber attacks in consecutive days after mining without a need to actualize the rules every day.