5 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 5 / 5
Yayın Network synchronization: Spectral versus statistical properties(Elsevier B.V., 2006-12) Atay, Fatihcan Mehmet; Bıyıkoğlu, Türker; Jost, JürgenWe consider synchronization of weighted networks, possibly with asymmetrical connections. Focusing on causal relations rather than the observed correlations, we show that the synchronizability of networks cannot be directly inferred from their statistical properties. Small local changes in the network structure can sensitively affect the eigenvalues relevant for synchronization, while the gross statistical network properties remain essentially unchanged. Consequently, commonly used statistical properties, including the degree distribution, degree homogeneity, average degree, average distance, degree correlation and clustering coefficient, can fail to characterize the synchronizability of networks in terms of causal relations, despite the observed correlations.Yayın Improving age of information in random access channels(Institute of Electrical and Electronics Engineers Inc., 2020-07) Atabay, Doğa Can; Uysal, Elif; Kaya, OnurWe study Age of Information (AoI) in a random access channel where a number of devices try to send status updates over a common medium. Assuming a time-slotted scenario where multiple transmissions result in collision, we propose a threshold-based lazy version of Slotted ALOHA and derive the time average AoI achieved by this policy. We demonstrate that the average AoI performance of the lazy policy is significantly better than Slotted ALOHA, and close to the ideal round robin benchmark.Yayın Querying sensor networks by using dynamic task sets(Elsevier B.V., 2006-05-15) Çayırcı, Erdal; Coşkun, Vedat; Çimen, ÇağhanA data querying scheme is introduced for sensor networks where queries formed for each sensing task are sent to task sets. The sensor field is partitioned into subregions by using quadtree based addressing, and then a given number of sensors from each subregion are assigned to each task set by using a distributed algorithm. The number of nodes in a task set depends on the task specifications. Hence, the sensed data is retrieved from a sensor network in the level of detail specified by users, and a tradeoff mechanism between data resolution and query cost is provided. Experiments show that the dynamic task sets scheme systematically reduces the number of sensors involved in a query in the orders of magnitude in the expense of slight reduction in the event detection rate.Yayın Quarantine region scheme to mitigate spam attacks in wireless sensor networks(IEEE, 2006-08) Coşkun, Vedat; Çayırcı, Erdal; Levi, Albert; Sancak, SerdarThe Quarantine Region Scheme (QRS) is introduced to defend against spam attacks in wireless sensor networks where malicious antinodes frequently generate dummy spam messages to be relayed toward the sink. The aim of the attacker is the exhaustion of the sensor node batteries and the extra delay caused by processing the spam messages. Network-wide message authentication may solve this problem with a cost of cryptographic operations to be performed over all messages. QRS is designed to reduce this cost by applying authentication only whenever and wherever necessary. In QRS, the nodes that detect a nearby spam attack assume themselves to be in a quarantine region. This detection is performed by intermittent authentication checks. Once quarantined, a node continuously applies authentication measures until the spam attack ceases. In the QRS scheme, there is a trade-off between the resilience against spam attacks and the number of authentications. Our experiments show that, in the worst-case scenario that we considered, a not quarantined node catches 80 percent of the spam messages by authenticating only 50 percent of all messages that it processes.Yayın Cross-layer ransomware detection framework for SDN using HMM, LSTM, and Bayesian inference(Institute of Electrical and Electronics Engineers Inc., 2025-08-28) Serter, Cemal Emre; Çeliktaş, BarışRansomware continues to pose a serious threat to endpoint computers as well as network systems, especially in Software Defined Networks (SDN) environments where programmability and centralized control offer novel attack surfaces. In this paper, a cross-layer detection model for ransomware is introduced that integrates host-based behavioral modeling using Hidden Markov Models (HMM), anomaly detection at flow level using Long Short-Term Memory (LSTM) networks, and probabilistic fusion through Bayesian inference. By correlating host and SDN layer anomalies, the system enhances early-stage detection and reduces false positives. A variational Bayesian approximation technique is utilized for decision score stabilization under ambiguous conditions. The model is evaluated with new ransomware datasets and obtains a range between 97.5%-99.92% F1-score across three benchmark datasets with less than 50 ms latency for detection. The hybrid framework gives a promising direction for real-time threat detection in resilient programmable networks.
