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%).

Internet of Things (IoTs) are set to revolutionize our lives and are widely being adopted nowadays. The IoT devices have a range of applications including smart homes, smart industrial networks and healthcare. Since these devices are responsible for generating and handling large amounts of sensitive data, the security of the IoT devices always poses a challenge. It is observed that a security breach could effect individuals and eventually the world at large. Artificial intelligence (AI), on the other hand, has found many applications and is widely being explored in providing security specifically for IoT devices. Malicious insider attack is the biggest security challenge associated with the IoT devices. Although, most of the research in IoT security has pondered on the means of preventing illegal and unauthorized access to systems and information; unfortunately, the most destructive malicious insider attacks that are usually a consequence of internal exploitation within an IoT network remains unaddressed. Therefore, the focus of this research is to detect malicious insider attacks in the IoT environment using AI. This research presents a lightweight approach for detecting insider attacks and has the capability of detecting anomalies originating from incoming data sensors in resource constrained IoT environments. The results and comparison show that the proposed approach achieves better accuracy as compared to the state of the art in terms of: a) improved attack detection accuracy; b) minimizing false positives; and c) reducing the computational overhead.

The increasing number of rice product safety issues and the potential for contamination have established an enormous need for an effective strategy for the traceability of the rice supply chain. Tracing the origins of a rice product from raw materials to end customers is very complex and costly. Existing food supply chain methods (for example, rice) do not provide a scalable and cost-effective means of agricultural food supply. Besides, consumers lack the capability and resources required to check or report on the quality of agricultural goods in terms of defects or contamination. Consequently, customers are forced to decide whether to utilize or discard the goods. However, blockchain is an innovative framework capable of offering a transformative solution for the traceability of agricultural products and food supply chains. The aim of this paper is to propose a framework capable of tracking and monitoring all interactions and transactions between all stakeholders in the rice chain ecosystem through smart contracts. The model incorporates a system for customer satisfaction feedback, which enables all stakeholders to get up-to-date information on product quality, enabling them to make more informed supply chain decisions. Each transaction is documented and stored in the public ledger of the blockchain. The proposed framework provides a safe, efficient, reliable, and effective way to monitor and track rice products safety and quality especially during product purchasing. The security and performance analysis results shows that the proposed framework outperform the benchmark techniques in terms of cost-effectiveness, security and scalability with low computational overhead.

Intelligent visual surveillance systems are attracting much attention from research and industry. The invention of smart surveillance cameras with greater processing power has now been the leading stakeholder, making it conceivable to design intelligent visual surveillance systems. It is possible to assure the safety of people in both homes and public places. This work aims to distinguish the suspicious activities for surveillance environments. For this, a 63 layers deep CNN model is suggested and named “L4-Branched-ActionNet”. The suggested CNN structure is centered on the alteration of AlexNet with added four blanched sub-structures. The developed framework is first transformed into a pre-trained framework by conducting its training on an object detection dataset called CIFAR-100 with the SoftMax function. The dataset for suspicious activity recognition is then forwarded to this pretrained model for feature acquisition. The acquired deep features are subjected to feature subset optimization. These extracted features are first coded by applying entropy and then an ant colony system (ACS) is utilized on the entropy-based coded features for optimization. The configured features are then fed into numerous SVM and KNN based classification models. The cubic SVM has the highest efficiency scores, with a performance of 0.9924 in order of accuracy. The proposed model is also validated on the Weizmann action dataset and attained an accuracy of 0.9796. The successful findings indicate the suggested work’s soundness.