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Intrusion Detection Systems (IDSs) are an important defense tool against the sophisticated and ever-growing network attacks. These systems need to be evaluated against high quality datasets for correctly assessing their usefulness and comparing their performance. We present an Intrusion Detection Dataset Toolkit (ID2T) for the creation of labeled datasets containing user defined synthetic attacks. The architecture of the toolkit is provided for examination and the example of an injected attack, in real network traffic, is visualized and analyzed. We further discuss the ability of the toolkit of creating realistic synthetic attacks of high quality and low bias.

Over the last years the number of cyber-attacks has been constantly increasing. Since isolated Intrusion Detection Systems (IDSs) cannot cope with the number and sophistication of attacks, collaboration among the defenders is required. Collaborative IDSs (CIDSs) work by exchanging alert traffic to construct a holistic view of the monitored network. However, an adversary can utilize probe-response attacks to successfully detect CIDS's monitoring sensors. We discuss the practicability of such attacks, suggest improvements, and also propose novel techniques to reduce the effects of such attacks. Moreover, we present preliminary results in the applicability of the attacks and hints on performing such attacks in a well known CIDS.