The most detrimental cyber attacks are usually not originated by malicious outsiders or malware but from trusted insiders. The main advantage insider attackers have over external elements is their ability to bypass security checks and remain undiscovered, this may cause serious damage to the organizational assets.This paper focuses on insider threat detection through behavioral analysis of users. User behavior is categorized as normal or malicious based on user activity. A series of events and activities are analyzed for feature selection to efficiently detect adversarial behavior. Selected feature vectors are used for model training during the implementation phase. A deep learning based approach is proposed that detects insiders with greater accuracy and low false positive rate. A rich event / user role based feature set containing Logon/Logoff events, User_role, Functional_unit etc are used for detection. The dataset used is the CMU CERT synthetic insider threat dataset r4.2. Performance of our proposed algorithm has been compared to other well-known techniques i.e. long short term Memory-convolutional neural network, random forest, long short term memory-recurrent neural network, one class support vector machine , Markov chain model,multi state long short term memory & convolutional neural network, gated recurrent unit & skipgram. The comparison proved that our novel approach produces relatively good accuracy( 90.60%), precision(97%) and F1 Score (94%).

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Forgery investigation and detection has been a relevant topic of interest for human beings since ages. Important messages written and transported by kings in old ages were sealed with signatures and stamps to achieve this purpose. But with the advent of digital technology, forgery detection has become even more important since tools for forgery have become vast as well. In this paper a technique based on pixel clustering has been introduced for detection of modification, alteration or forgery done with a different ink color pen. Hyperspectral images are used for ink mismatch detection in a handwritten note. We propose ink classification based on pixel intensities values present in all the bands of hyperspectral images of the handwritten note. Our proposed technique is quite simple yet effective in detecting ink mismatch with relatively high accuracy.