Passive operating system fingerprinting based on multi-layered sub-signature matching scheme (MLSMS).

Rangkaian komputer merupakan dimensi yang penting dalam organisasi moden. Oleh itu, usaha memastikan rangkaian ini dapat berjalan pada prestasi puncak dianggap amat penting dalam organisasi ini. Computer networks become an important dimension of the modern organizations. Thus, keeping the computer networks running at the peak performance is considered as a crucial part for these organizations

Node Verification to Join the Cloud Environment Using Third Party Verification Server

Currently, cloud computing is facing different types of threats whether from inside or outside its environment.  This may cause cloud to be crashed or at least unable to provide services to the requests made by clients. In this paper, a new technique is proposed to make sure that the new node which asks to join the cloud is not composing a threat on the cloud environment. Our new technique checks the node before it will be guaranteed to join the cloud whether it runs malwares or software that could be used to launch an attack. In this way the cloud will allow only the clean node to join it, eliminating the risk of some types of threats that could be caused by infected nodes

Performance Evaluation of Machine Learning Approaches in Detecting IoT-Botnet Attacks

Botnets are today recognized as one of the most advanced vulnerability threats. Botnets control a huge percentage of network traffic and PCs. They have the ability to remotely control PCs (zombie machines) by their creator (BotMaster) via Command and Control (C&C) framework. They are the keys to a variety of Internet attacks such as spams, DDOS, and spreading malwares. This study proposes a number of machine learning techniques for detecting botnet assaults via IoT networks to help researchers in choosing the suitable ML algorithm for their applications. Using the BoT-IoT dataset, six different machine learning methods were evaluated: REPTree, RandomTree, RandomForest, J48, metaBagging, and Naive Bayes. Several measures, including accuracy, TPR, FPR, and many more, have been used to evaluate the algorithms’ performance. The six algorithms were evaluated using three different testing situations. Scenario-1 tested the algorithms utilizing all of the parameters presented in the BoT-IoT dataset, scenario-2 used the IG feature reduction approach, and scenario-3 used extracted features from the attacker’s received packets. The results revealed that the assessed algorithms performed well in all three cases with slight differences