recent development emanating from RFID technology is Near Field Communication (NFC). Basically, NFC is a popular short range (< 10 cm) wireless communication technology with applications in areas sensitive to security and privacy concerns like contactless payment. Since NFC communications require very close proximity between two communicating devices (e.g., a smartcard and a terminal), it is generally believed that Man-in-the-Middle (MITM) attacks are practically infeasible here. Contrasting this belief, in this paper, we successfully establish MITM attack in NFC communications between a passive tag and an active terminal. We carefully present physical fundamentals of the attack, our engineering design, and results of successful attack implementation. We then identify a potential vulnerability in existing contactless payment protocols due to separation between card authentication and transaction authorization phases. We then show in this paper, how an attacker can compromise the integrity of contactless payment using a malicious MITM smartcard, and also present multiple attack/victim scenarios to demonstrate practicality of our contributions. We also conduct rigorous experimental studies to analyze both hardware and practical ramifications of our attack. Finally, we propose a countermeasure to detect the MITM attack based on experimental analysis, that does not demand any additional hardware.

Although neural networks (especially deep neural networks) have achieved better-than-human performance in many fields, their real-world deployment is still questionable due to the lack of awareness about the limitations in their knowledge. To incorporate such awareness in the machine learning model, prediction with reject option (also known as selective classification or classification with abstention) has been proposed in the literature. In this article, we present a systematic review of the prediction with the reject option in the context of various neural networks. To the best of our knowledge, this is the first study focusing on this aspect of neural networks. Moreover, we discuss different novel loss functions related to the reject option and post-training processing (if any) of network output for generating suitable measurements for knowledge awareness of the model. Finally, we address the application of the rejection option in reducing the prediction time for real-time problems and present a comprehensive summary of the techniques related to the reject option in the context of a wide variety of neural networks. Our code is available on GitHub: https://github.com/MehediHasanTutul/Reject_option .

The threats of botnets are becoming a growing concern infecting more and more computers every day. Although botnets can be detected from their behavioral patterns, it is becoming more challenging to differenti-ate the behavior between the malicious traffic and the legitimate trafficas with the advancement of the technologies the malicious traffics are fol-lowing the similar behavioral patterns of benign traffics. The detectionof malicious traffic largely depends on the traffic features that are beingused to feed in the detection process. Selecting the best features for effec-tive botnet detection is the main contribution of this paper. At the verybeginning, we show the impact of different features on botnet detectionprocess. Then we propose several heuristics to select the best featuresfrom a handful of possible features. Some proposed heuristics are trulyfeature-based and some are group-based, thus generating different accu-racy levels. We also analyze time complexity of each heuristic and providea detailed performance analysis. As working with all combinations of alarge number of features is not feasible, some heuristics work by groupingthe features based on their similarity in patterns and checking all combi-nations within the groups of small number of features which improves thetime complexity by a large margin. Through experiments we show the efficacy of the proposed feature selection heuristics. The result shows thatsome heuristics outperform state-of-the-art feature selection algorithms.