Results 131 to 140 of about 3,020 (212)

Botnet detection in internet of things using stacked ensemble learning model. [PDF]

Sci Rep
Ali M, Mushtaq MF, Akram U, Aray DG, Vergara MM, Karamti H, Ashraf I.   +6 more
europepmc   +1 more source

Botnet detection for Android system

, 2013
With the openness design and ease of customizing, Android OS develops rapidly and comes to the leading position among the smartphone OSs. Such popularity has not only led to an increase in Android usage but also to the rise of Android malware ...
Zhang, Jiaqi
core  

Challenges in Experimenting with Botnet Detection Systems

, 2011
In this paper, we examine the challenges faced when evaluating botnet detection systems. Many of these challenges stem from difficulties in obtaining and sharing diverse sets of real network traces, as well as determining a botnet ground truth in such ...
Aviv, Adam J., Haeberlen, Andreas
core  

Employing SAE-GRU deep learning for scalable botnet detection in smart city infrastructure. [PDF]

PeerJ Comput Sci
Tariq U, Ahanger TA.
europepmc   +1 more source

Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets. [PDF]

Sci Rep
Ullah S, Wu J, Lin Z, Kamal MM, Mostafa H, Sheraz M, Chuah TC.   +6 more
europepmc   +1 more source

Enhanced botnet detection in IoT networks using zebra optimization and dual-channel GAN classification. [PDF]

Sci Rep
Shareef SKK, Chaitanya RK, Chennupalli S, Chokkakula D, Kiran KVD, Pamula U, Vatambeti R.   +6 more
europepmc   +1 more source

An Algorithm for Anomaly-based Botnet Detection

, 2006
We present an anomaly-based algorithm for detecting IRC-based botnet meshes. The algorithm combines an IRC mesh detection component with a TCP scan detection heuristic called the TCP work weight. The IRC component produces two tuples, one for determining
James R. Binkley
core  

An integrated networkbased mobile botnet detection system [PDF]

The increase in the use of mobile devices has made them target for attackers, through the use of sophisticated malware. One of the most significant types of such malware is mobile botnets.
Meng, X.
core  

Ensemble averaging deep neural network for botnet detection in heterogeneous Internet of Things devices. [PDF]

Sci Rep
Wardana AA, Kołaczek G, Warzyński A, Sukarno P.   +3 more
europepmc   +1 more source

Stabilized quantum-enhanced SIEM architecture and speed-up through Hoeffding tree algorithms enable quantum cybersecurity analytics in botnet detection. [PDF]

Sci Rep
Tehrani MG, Sultanow E, Buchanan WJ, Amir M, Jeschke A, Houmani M, Chow R, Lemoudden M.   +7 more
europepmc   +1 more source