From product recommendation to cyber-attack prediction: generating attack graphs and predicting future attacks

Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks in terms of risk management. The proposed method has been experimentally evaluated and validated, with the results showing that it is both practical and effective

PI3Kinase signaling in glioblastoma

Glioblastoma (GBM) is the most common primary tumor of the CNS in the adult. It is characterized by exponential growth and diffuse invasiveness. Among many different genetic alterations in GBM, e.g., mutations of PTEN, EGFR, p16/p19 and p53 and their impact on aberrant signaling have been thoroughly characterized. A major barrier to develop a common therapeutic strategy is founded on the fact that each tumor has its individual genetic fingerprint. Nonetheless, the PI3K pathway may represent a common therapeutic target to most GBM due to its central position in the signaling cascade affecting proliferation, apoptosis and migration. The read-out of blocking PI3K alone or in combination with other cancer pathways should mainly focus, besides the cytostatic effect, on cell death induction since sublethal damage may induce selection of more malignant clones. Targeting more than one pathway instead of a single agent approach may be more promising to kill GBM cells

Recommender systems meeting security: From product recommendation to cyber-attack prediction

© Springer International Publishing AG 2017. Modern information society depends on reliable functionality of information systems infrastructure, while at the same time the number of cyber-attacks has been increasing over the years and damages have been caused. Furthermore, graphs can be used to show paths than can be exploited by attackers to intrude into systems and gain unauthorized access through vulnerability exploitation. This paper presents a method that builds attack graphs using data supplied from the maritime supply chain infrastructure. The method delivers all possible paths that can be exploited to gain access. Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks. The goal of this paper is to identify attack paths and show how a recommendation method can be used to classify future cyber-attacks. The proposed method has been experimentally evaluated and it is shown that it is both practical and effective