-In the last years, governmental bodies have been futilely trying to fight against dark web marketplaces. Shortly after the closing of "The Silk Road" by the FBI and Europol in 2013, new successors have been established. Through the combination of cryptocurrencies and nonstandard communication protocols and tools, agents can anonymously trade in a marketplace for illegal items without leaving any record. This paper presents a research carried out to gain insights on the products and services sold within one of the larger marketplaces for drugs, fake ids and weapons on the Internet, Agora. Our work sheds a light on the nature of the market; there is a clear preponderance of drugs, which accounts for nearly 80% of the total items on sale. The ready availability of counterfeit documents, while they make up for a much smaller percentage of the market, raises worries. Finally, the role of organized crime within Agora is discussed and presented.

Cyber Supply Chain(CSC) system is complex which involves different sub-systems performing various tasks. Security in supply chain is challenging due to the inherent vulnerabilities and threats from any part of the system can be exploited at any point within the supply chain. This can cause a severe disruption on the overall business continuity. Therefore, it is paramount important to understand and predicate the threats so that organization can undertake necessary control measures for the supply chain security. Cyber Threat Intelligence (CTI) provides an intelligence analysis to discover unknown to known threats using various properties including threat actor skill and motivation, Tactics, Techniques, Procedure (TTP), and Indicator of Compromise (IoC). This paper aims to analyse and predicate threats to improve cyber supply chain security. We have applied Cyber Threat Intelligence (CTI) with Machine Learning (ML) techniques to analyse and predict the threats based on the CTI properties. That allows to identify the inherent CSC vulnerabilities so that appropriate control actions can be undertaken for the overall cybersecurity improvement. To demonstrate the applicability of our approach, CTI data is gathered and a number of ML algorithms, i.e., Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT), are used to develop predictive analytics using the Microsoft Malware Prediction dataset. The experiment considers attack and TTP as input parameters and vulnerabilities and Indicators of compromise (IoC) as output parameters. The results relating to the prediction reveal that Spyware/Ransomware and spear phishing are the most predictable threats in CSC. We have also recommended relevant controls to tackle these threats. We advocate using CTI data for the ML predicate model for the overall CSC cyber security improvement.

Activity of Daily Life (ADL) recognition plays an important role in tracking functional decline among elderly people who suffer from Alzheimer's disease. Accurate recognition enables smart environments to support and assist the elderly to lead an independent life for as long as possible. Current work has generally focused on applying a range of traditional classification and semantic reasoning based techniques in order to recognise ADLs. However, the ability to represent the complex structure of an ADL in a flexible manner remains a challenge. In this paper, we present an ADL recognition approach, which uses a hierarchal structure for the representation and modelling of the activities, its associated tasks and their relationships. We describe an approach in constructing ADLs based on a task-specific and intention-oriented plan representation language called Asbru. The proposed method is particularly flexible and adaptable for caregivers to be able to model daily schedules for Alzheimer's patients. A proof of concept prototype evaluation has been conducted for the validation of the proposed ADL recognition engine, which has comparable recognition results with existing ADL recognition approaches.

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Abstract. This paper presents a new application of the snapdrift algorithm [1]: feature discovery and clustering of speech waveforms from nonstammering and stammering speakers. The learning algorithm is an unsupervised version of snapdrift which employs the complementary concepts of fast, minimalist learning (snap) & slow drift (towards the input pattern) learning. The Snap Drift Neural Network (SDNN) is toggled between snap and drift modes on successive epochs. The speech waveforms are drawn from a phonetically annotated corpus, which facilitates phonetic interpretation of the classes of patterns discovered by the SDNN.