Biblio
Network attack is a significant security issue for modern society. From small mobile devices to large cloud platforms, almost all computing products, used in our daily life, are networked and potentially under the threat of network intrusion. With the fast-growing network users, network intrusions become more and more frequent, volatile and advanced. Being able to capture intrusions in time for such a large scale network is critical and very challenging. To this end, the machine learning (or AI) based network intrusion detection (NID), due to its intelligent capability, has drawn increasing attention in recent years. Compared to the traditional signature-based approaches, the AI-based solutions are more capable of detecting variants of advanced network attacks. However, the high detection rate achieved by the existing designs is usually accompanied by a high rate of false alarms, which may significantly discount the overall effectiveness of the intrusion detection system. In this paper, we consider the existence of spatial and temporal features in the network traffic data and propose a hierarchical CNN+RNN neural network, LuNet. In LuNet, the convolutional neural network (CNN) and the recurrent neural network (RNN) learn input traffic data in sync with a gradually increasing granularity such that both spatial and temporal features of the data can be effectively extracted. Our experiments on two network traffic datasets show that compared to the state-of-the-art network intrusion detection techniques, LuNet not only offers a high level of detection capability but also has a much low rate of false positive-alarm.
The focus of this paper is to propose an integration between Internet of Things (IoT) and Video Surveillance, with the aim to satisfy the requirements of the future needs of Video Surveillance, and to accomplish a better use. IoT is a new technology in the sector of telecommunications. It is a network that contains physical objects, items, and devices, which are embedded with sensors and software, thus enabling the objects, and allowing for their data exchange. Video Surveillance systems collect and exchange the data which has been recorded by sensors and cameras and send it through the network. This paper proposes an innovative topology paradigm which could offer a better use of IoT technology in Video Surveillance systems. Furthermore, the contribution of these technologies provided by Internet of Things features in dealing with the basic types of Video Surveillance technology with the aim to improve their use and to have a better transmission of video data through the network. Additionally, there is a comparison between our proposed topology and relevant proposed topologies focusing on the security issue.
Globalization of semiconductor design, manufacturing, packaging and testing has led to several security issues like over production of chips, shipping of faulty or partially functional chips, intellectual property infringement, cloning, counterfeit chips and insertion of hardware trojans in design house or at foundry etc. Adversaries will extract chips from obsolete PCB's and release used parts as new chips into the supply chain. The faulty chips or partially functioning chips can enter supply chain from untrusted Assembly Packaging and Test (APT) centers. These counterfeit parts are not reliable and cause catastrophic consequences in critical applications. To mitigate the counterfeits entering supply chain, to protect the Intellectual Property (IP) rights of owners and to meter the chip, Secure Split Test (SST) is a promising solution. CSST (Connecticut SST) is an improvement to SST, which simplifies the communication required between ATP center and design house. CSST addresses the scan tests, but it does not address the functional testing of chips. The functional testing of chips during production testing is critical in weeding out faulty chips in recent times. In this paper, we present a method called PUF-SST (Physical Unclonable Function – SST) to perform both scan tests and functional tests without compromising on security features described in CSST.
With the spectacular increase in online activities like e-transactions, security and privacy issues are at the peak with respect to their significance. Large numbers of database security breaches are occurring at a very high rate on daily basis. So, there is a crucial need in the field of database forensics to make several redundant copies of sensitive data found in database server artifacts, audit logs, cache, table storage etc. for analysis purposes. Large volume of metadata is available in database infrastructure for investigation purposes but most of the effort lies in the retrieval and analysis of that information from computing systems. Thus, in this paper we mainly focus on the significance of metadata in database forensics. We proposed a system here to perform forensics analysis of database by generating its metadata file independent of the DBMS system used. We also aim to generate the digital evidence against criminals for presenting it in the court of law in the form of who, when, why, what, how and where did the fraudulent transaction occur. Thus, we are presenting a system to detect major database attacks as well as anti-forensics attacks by developing an open source database forensics tool. Eventually, we are pointing out the challenges in the field of forensics and how these challenges can be used as opportunities to stimulate the areas of database forensics.
In this paper we explore the potential for securing a distributed Arabic Optical Character Recognition (OCR) system via cloud computing technology in a pervasive and mobile environment. The goal of the system is to achieve full accuracy, high speed and security when taking into account large vocabularies and amounts of documents. This issue has been resolved by integrating the recognition process and the security issue with multiprocessing and distributed computing technologies.