Searched for: contributor%3A%22Nadeem%2C+A.+%28mentor%29%22
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Băbălău, Ion (author)
In an era where cyber threats evolve with alarming speed and sophistication, the role of Security Operation Centers (SOCs) has become increasingly pivotal in safeguarding digital infrastructures. SOCs serve as the frontline defence against malicious entities, where they continuously monitor and analyze network traffic, as well as the activity of...
master thesis 2023
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Zelenjak, Jegor (author)
SAGE is an unsupervised sequence learning pipeline that generates alert-driven attack graphs (AGs) without the need for prior expert knowledge about existing vulnerabilities and network topology. Using a suffix-based probabilistic deterministic finite automaton (S-PDFA), it accentuates infrequent high-severity alerts without discarding frequent...
bachelor thesis 2023
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Constantinescu, Vlad (author)
The interpretability of an attack graph is a key principle as it reflects the difficulty of a specialist to take insights into attacker strategies. However, the quantification of interpretability is considered to be a subjective manner and complex attack graphs can be challenging to read and interpret. In this research paper, we propose a new...
bachelor thesis 2023
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Van den Broeck, Senne (author)
Intrusion Detection Systems (IDSes) detect malicious traffic in computer networks and generate a large volume of alerts, which cannot be processed manually. SAGE is a deterministic algorithm that works without a priori network/expert knowledge and can compress these alerts into attack graphs (AGs), modelling intruders’ paths in the network....
bachelor thesis 2023
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Dumitriu, Alexandru (author)
This research paper focuses on the complex domain of alert-driven attack graphs. SAGE is a tool which generates such attack graphs (AGs) by using a suffix-based probabilistic deterministic finite automaton (S-PDFA). One of the substantial properties of this algorithm is to detect infrequent severe alerts while maintaining the context of attacks...
bachelor thesis 2023
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Oprea, Ioan (author)
SAGE is a deterministic and unsupervised learning pipeline that can generate attack graphs from intrusion alerts without input knowledge from a security analyst. Using a suffix-based probabilistic deterministic finite automaton (S-PDFA), the system compresses over 1 million alerts into less than 500 attack graphs (AGs), which are concise and...
bachelor thesis 2023
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de Heer, Hugo (author)
MalPaCA makes use of unsupervised machine learning to provide malware capability assessment by clustering the temporal behaviour of malware network packet traces. A comparative analysis was performed on various clustering algorithms to determine the best clustering algorithm in terms of network behaviour discovery. The clustering algorithms...
bachelor thesis 2021
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Fucarev, Silviu (author)
Clustering data is a classic topic in the academic community and in the industry. It is by and large one of the most popular unsupervised classification techniques. It is fast and flexible as it can accommodate all kinds of data when a suitable similarity metric is found. SeqClu is an online k-medoids prototype based clustering algorithm...
bachelor thesis 2021
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Epifanov, Mikhail (author)
Malware Packet-sequence Clustering and Analysis (MalPaCA) is a unsupervised clustering application for malicious network behavior, it currently uses solely sequential features to characterize network behavior. In this paper an extensive comparison between those features and statistical features is performed. During the comparison a better...
bachelor thesis 2021
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Park, Sung kyung (author)
Identifying novel malware and their behaviour enables security engineers to prevent and protect users with devices on the network from attackers. MalPaCA is an algorithm that helps to understand the behaviours of the network traffic by clustering uni-directional network connections which can be analyzed further to interpret which label suites...
bachelor thesis 2021
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Al-Obaidi, Rami (author)
Clustering is a group of (unsupervised) machine learning algorithms used to categorize data into clusters. The most popular clustering algorithm is k-means clustering. K-means clustering clusters the data into k clusters where a cluster is represented by the mean of the data points called a centroid. Instead of using the mean as a centroid, a...
bachelor thesis 2021
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te Wierik, Ruben (author)
Real-time sequence clustering is the problem of clustering an infinite stream of sequences in real time with limited memory. A variant of the k-medoids algorithm called <i>SeqClu </i>is the suggested approach, representing a cluster with <i>p </i>most representative sequences of the cluster, called prototypes, to solve the problem of maintaining...
bachelor thesis 2021
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Hagspiel, Johannes (author)
MalPaCa is a novel, unsupervised clustering algorithm, which creates based on the network flow of a software a behavioral profile representing its actual capabilities. One of the key variables affecting is performance and usability is the sequence length or how many packets it analyzes in order to group a connection to a cluster. This article...
bachelor thesis 2021
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