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Daoud, Luka, Rafla, Nader.  2019.  Analysis of Black Hole Router Attack in Network-on-Chip. 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS). :69–72.

Network-on-Chip (NoC) is the communication platform of the data among the processing cores in Multiprocessors System-on-Chip (MPSoC). NoC has become a target to security attacks and by outsourcing design, it can be infected with a malicious Hardware Trojan (HT) to degrades the system performance or leaves a back door for sensitive information leaking. In this paper, we proposed a HT model that applies a denial of service attack by deliberately discarding the data packets that are passing through the infected node creating a black hole in the NoC. It is known as Black Hole Router (BHR) attack. We studied the effect of the BHR attack on the NoC. The power and area overhead of the BHR are analyzed. We studied the effect of the locations of BHRs and their distribution in the network as well. The malicious nodes has very small area and power overhead, 1.98% and 0.74% respectively, with a very strong violent attack.

Macdonald, M., Frank, R., Mei, J., Monk, B..  2015.  Identifying digital threats in a hacker web forum. 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). :926–933.

Information threatening the security of critical infrastructures are exchanged over the Internet through communication platforms, such as online discussion forums. This information can be used by malicious hackers to attack critical computer networks and data systems. Much of the literature on the hacking of critical infrastructure has focused on developing typologies of cyber-attacks, but has not examined the communication activities of the actors involved. To address this gap in the literature, the language of hackers was analyzed to identify potential threats against critical infrastructures using automated analysis tools. First, discussion posts were collected from a selected hacker forum using a customized web-crawler. Posts were analyzed using a parts of speech tagger, which helped determine a list of keywords used to query the data. Next, a sentiment analysis tool scored these keywords, which were then analyzed to determine the effectiveness of this method.