Assessing the Impact of a Supervised Classification Filter on Flow-based Hybrid Network Anomaly Detection

Constant evolution and the emergence of new cyberattacks require the development of advanced techniques for defense. This paper aims to measure the impact of a supervised filter (classifier) in network anomaly detection. We perform our experiments by employing a hybrid anomaly detection approach in network flow data. For this purpose, we extended a state-of-the-art autoencoder-based anomaly detection method by prepending a binary classifier acting as a prefilter for the anomaly detector. The method was evaluated on the publicly available real-world dataset UGR'16. Our empirical results indicate that the hybrid approach does offer a higher detection rate of known attacks than a standalone anomaly detector while still retaining the ability to detect zero-day attacks. Employing a supervised binary prefilter has increased the AUC metric by over 11%, detecting 30% more attacks while keeping the number of false positives approximately the same

Mitigation of DoS Attacks Using Machine Learning

Útoky typu odoprenia služby (DDoS) sú v dnešných počítačových sieťach stále frekventovanejším bezpečnostným incidentom. Táto práca sa zameriava na detekciu týchto útokov a poskytnutie relevantných informácii za účelom ich mitigácie v reálnom čase. Spomínaná funkcionalita je dosiahnutá s využitím techník prúdového dolovania z dát a strojového učenia. Výsledkom práce je sada nástrojov zastrešujúca celý proces strojového učenia - od vlastnej extrakcie príznakov cez predspracovanie dát až po export natrénovaného modelu pripraveného na nasadenie v produkcii. Experimentálne výsledky vyhodnotené na viacerých reálnych a syntetických dátových sadách poukazujú na presnosť systému väčšiu ako 99% s možnosťou spoľahlivej detekcie prebiehajúceho útoku do 4 sekúnd od jeho začiatku.Distributed Denial of Service (DDoS) attacks are an ever-increasing type of security incident on modern computer networks. This thesis aims to detect these attacks and provide relevant information in order to mitigate them in real-time. This functionality is achieved by data stream mining and machine learning techniques. The output of the work is a series of tools executing the process of the whole machine learning pipeline - from custom feature extraction through data preprocessing to exporting a trained model ready for deployment. The experimental results evaluated on various real and synthetic datasets indicate an accuracy of over 99% with an ability to reliably detect an ongoing attack within the first 4 seconds of its start.

Heuristic Methods for the Mitigation of DDoS Attacks that Abuse TCP Protocol

TCP SYN Flood sa v súčasnosti radí medzi najpopulárnejšie útoky typu DoS. Táto práca popisuje sieťovú mitigačnú metódu TCP Reset Cookies ako jeden z možných spôsobov ochrany. Spomínaná metóda je založená na zahadzovaní všetkých prijatých pokusov o nadviazanie spojenia, až pokým s daným klientom nie je uzatvorená bezpečnostná asociácia na základe využitia mechanizmu TCP three-way-handshake. Tento prístup dokáže efektívne odraziť aj sofistikovanejšie útoky, avšak za cenu sekundového oneskorenia pri prvom nadväzovanom spojení daného klienta. Metóda však nie je vhodná vo všetkých prípadoch. Z tohto dôvodu táto práca ďalej navrhuje a implementuje spôsob dynamického prepínania rôznych mitigačných metód na základe aktuálne prebiehajúcej komunikácie. Tento projekt bol vykonaný ako súčasť bezpečnostného výskumu spoločnosti CESNET. Spomínaná implementácia metódy TCP Reset Cookies je už v čase písania tejto práce integrovaná do DDoS riešenia nasadeného na hlavnej sieti spoločnosti CESNET, ako aj v českom národnom peeringovom uzle NIX.CZ.TCP SYN Flood is one of the most wide-spread DoS attack types used on computer networks nowadays. As a possible countermeasure, this thesis proposes a network-based mitigation method TCP Reset Cookies. The method utilizes the TCP three-way-handshake mechanism to establish a security association with a client before forwarding its SYN data. The algorithm can effectively mitigate even more sophisticated SYN flood attacks at the cost of 1-second delay for the first established connection. However, the method may not be suitable for all the scenarios, so decision-making algorithm to switch between different SYN Flood mitigation methods according to discovered traffic patterns was also developed. The project was conducted as a part of security research by CESNET. The discussed implementation of TCP Reset Cookies is already integrated into a DDoS protection solution deployed in CESNET's backbone network and Czech Internet exchange point at NIX.CZ.

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.11Nsciescopu

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60–80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies