  With the recent prevalence of remote education, academic assessments are
often conducted online, leading to further concerns surrounding assessment
misconducts. This paper investigates the potentials of online assessment
misconduct (e-cheating) and proposes practical countermeasures against them.
The mechanism for detecting the practices of online cheating is presented in
the form of an e-cheating intelligent agent, comprising of an internet protocol
(IP) detector and a behavioural monitor. The IP detector is an auxiliary
detector which assigns randomised and unique assessment sets as an early
procedure to reduce potential misconducts. The behavioural monitor scans for
irregularities in assessment responses from the candidates, further reducing
any misconduct attempts. This is highlighted through the proposal of the
DenseLSTM using a deep learning approach. Additionally, a new PT Behavioural
Database is presented and made publicly available. Experiments conducted on
this dataset confirm the effectiveness of the DenseLSTM, resulting in
classification accuracies of up to 90.7%.
