Biblio

Authenticating a person's identity has always been a challenge. While attempts are being made by government agencies to address this challenge, the citizens are being exposed to a new age problem of Identity management. The sharing of photocopies of identity cards in order to prove our identity is a common sight. From score-card to Aadhar-card, the details of our identity has reached many unauthorized hands during the years. In India the identity thefts accounts for 77% [1] of the fraud cases, and the threats are trending. Programs like e-Residency by Estonia[2], Bitnation using Ethereum[3] are being devised for an efficient Identity Management. Even the US Home Land Security is funding a research with an objective of “Design information security and privacy concepts on the Blockchain to support identity management capabilities that increase security and productivity while decreasing costs and security risks for the Homeland Security Enterprise (HSE).” [4] This paper will discuss the challenges specific to India around Identity Management, and the possible solution that the Distributed ledger, hashing algorithms and smart contracts can offer. The logic of hashing the personal data, and controlling the distribution of identity using public-private keys with Blockchain technology will be discussed in this paper.

After the Fukushima nuclear disaster and the Wenchuan earthquake, the relevant government agencies recognized the urgency of disaster-straining robots. There are many natural or man-made disasters in Taiwan, and it is usually impossible to dispatch relevant personnel to search or explore immediately. The project proposes to use the architecture of Intelligent Internet of Things (AIoT) (Artificial Intelligence + Internet of Things) to coordinate with ground, surface and aerial and underwater robots, and apply them to disaster response, ground, surface and aerial and underwater swarm robots to collect environmental big data from the disaster site, and then through the Internet of Things. From the field workstation to the cloud for “training” deep learning model and “model verification”, the trained deep learning model is transmitted to the field workstation via the Internet of Things, and then transmitted to the ground, surface and aerial and underwater swarm robots for on-site continuing objects classification. Continuously verify the “identification” with the environment and make the best decisions for the response. The related tasks include monitoring, search and rescue of the target.

Distributed Denial of Service (DDoS) attacks are intense and are targeted to major infrastructure, governments and military organizations in each country. There are a lot of mitigations about DDoS, and the concept of Content Delivery Network (CDN) has been able to avoid attacks on websites. However, since the existing CDN system is fundamentally centralized, it may be difficult to prevent DDoS. This paper describes the distributed CDN Schema using Private Blockchain which solves the problem of participation of existing transparent and unreliable nodes. This will explain DDoS mitigation that can be used by military and government agencies.

Among most of the cyber attacks that occured, the most drastic are advanced persistent threats. APTs are differ from other attacks as they have multiple phases, often silent for long period of time and launched by adamant, well-funded opponents. These targeted attacks mainly concentrated on government agencies and organizations in industries, as are those involved in international trade and having sensitive data. APTs escape from detection by antivirus solutions, intrusion detection and intrusion prevention systems and firewalls. In this paper we proposes a classification model having 99.8% accuracy, for the detection of APT.