A Battle Lost? Report on Two Centuries of Invasion and Management of Lantana camara L. in Australia, India and South Africa

Recent discussion on invasive species has invigorated the debate on strategies to manage these species. Lantana camara L., a shrub native to the American tropics, has become one of the worst weeds in recorded history. In Australia, India and South Africa, Lantana has become very widespread occupying millions of hectares of land. Here, we examine historical records to reconstruct invasion and management of Lantana over two centuries and ask: Can we fight the spread of invasive species or do we need to develop strategies for their adaptive management? We carried out extensive research of historical records constituting over 75% of records on invasion and management of this species in the three countries. The records indicate that governments in Australia, India and South Africa have taken aggressive measures to eradicate Lantana over the last two centuries, but these efforts have been largely unsuccessful. We found that despite control measures, the invasion trajectory of Lantana has continued upwards and that post-war land-use change might have been a possible trigger for this spread. A large majority of studies on invasive species address timescales of less than one year; and even fewer address timescales of >10 years. An understanding of species invasions over long time-scales is of paramount importance. While archival records may give only a partial picture of the spread and management of invasive species, in the absence of any other long-term dataset on the ecology of Lantana, our study provides an important insight into its invasion, spread and management over two centuries and across three continents. While the established paradigm is to expend available resources on attempting to eradicate invasive species, our findings suggest that in the future, conservationists will need to develop strategies for their adaptive management rather than fighting a losing battle

Real-time IoT Device Activity Detection in Edge Networks

The growing popularity of Internet-of-Things (IoT) has created the need for network-based traffic anomaly detection systems that could identify misbehaving devices. In this work, we propose a lightweight technique, IoTguard, for identifying malicious traffic flows. IoTguard uses semi-supervised learning to distinguish between malicious and benign device behaviours using the network traffic generated by devices. In order to achieve this, we extracted 39 features from network logs and discard any features containing redundant information. After feature selection, fuzzy C-Mean (FCM) algorithm was trained to obtain clusters discriminating benign traffic from malicious traffic. We studied the feature scores in these clusters and use this information to predict the type of new traffic flows. IoTguard was evaluated using a real-world testbed with more than 30 devices. The results show that IoTguard achieves high accuracy (>98%), in differentiating various types of malicious and benign traffic, with low false positive rates. Furthermore, it has low resource footprint and can operate on OpenWRT enabled access points and COTS computing boards.Information and Communication Technolog