IoT Forensic -- A digital investigation framework for IoT systems

Security issues, threats, and attacks in relation with the IoT have been identified as promising and challenging area of research. Eventually, the need for a forensics methodology for investigating IoT-related crime is therefore essential. However, the IoT poses many challenges for forensics investigators. These include the wide range and variety of information, the unclear lines of differentiation between networks, for example private networks increasingly fading into public networks. Further, integration of a large number of objects in IoT forensic interest, along with the relevance of identified and collected devices makes forensic of IoT devices more complicated. The scope of this paper is to present a framework for IoT forensic. We aimed at the study and development of the link to support digital investigations of IoT devices and tackle emerging challenges in digital forensics. We emphasize on various steps for digital forensic with respect to IoT devices.Comment: Paper presented at 10th International Conference on Electronics, Computers and Artificial Intelligence, , ECAI 2018 - 28-30 June 2018 - Iasi, Romani

CCJRF-ICN: A Novel Mechanism for Coadjuvant Caching Joint Request Forwarding in Information Centric Networks

Information centric networking (ICN) shifts the focus of existing internet architecture from host-oriented to content-oriented model by enabling in-network caching and content-based forwarding. These ICN features help to increase network performance by decreasing content discovery delay, content server load, and network congestion. To route a content interest inside network such that content can be fetched with minimal time is a challenging task in ICN. The performance of the ICN routing protocol can be significantly improved if the decisions related to content chunk placement and request forwarding are taken in a cooperative fashion. This paper describes a novel strategy for co-operative caching joint request forwarding in ICN, focusing on decreasing content retrieval latency. To do so, the caching strategy leverages the concept of connected dominating set (CDS) for creating a virtual backbone network to eliminate caching redundancy and reduce content discovery delay. It considers content chunk placement and request forwarding tasks as strongly co-related procedures. It exploits the caching information so that the request is forwarded to a content router (CR) with the maximum likelihood of carrying needed data using the betweenness centrality (BC) of CR. The proposed approach also uses the Markov chain-based model to estimate CS hit likelihood and use it as decisional parameter while forwarding the interest packet. This mechanism helps to fetch the content within the shortest possible time duration. The CCJRF-ICN adopts the Dijkstra’s shortest path routing and works in collaboration with the CDS-driven caching joint forwarding mechanism. The simulation study of CCJRF-ICN is done inside ns-3 based ndnSIM-2.0 simulator with performance measures like content store (CS) hit ratio, content discovery delay, mean hop distance, network load, and network overhead. The simulation outcomes demonstrate that CCJRF-ICN outperforms the state-of-the-art strategies for realistic topologies (GEANT, US-26, Euro-28) and shows improvement up to 5-35% against stated performance measures

Wpływ ICT na rozwój biznesu lokalnego

Małe i średnie przedsiębiorstwa wnoszą znaczny wkład w dynamizację lokalnej gospodarki, przyczyniając się do rozwoju społeczności i innowacji. Tego typu przedsiębiorstwa pomagają również stymulować wzrost gospodarczy poprzez tworzenie miejsc pracy dla osób, które z różnych względów nie mogą podjąć zatrudnienia w dużych firmach. Małe firmy oferują również możliwości utalentowanym ludziom, którzy mogą opracowywać nowe produkty, usługi lub wdrażać innowacyjne rozwiązania. Co więcej, duże przedsiębiorstwa czerpią korzyści ze współpracy z małymi firmami w ramach społeczności lokalnych. Dzieje się tak, ponieważ wiele dużych gałęzi przemysłu zależy od małych firm – różne przedsięwzięcia biznesowe realizowane są poprzez outsourcing. Niniejszy artykuł ma na celu podkreślenie wkładu małych firm w rozwój lokalnej gospodarki. Ponadto zamiarem Autorów jest zwrócenie uwagi czytelników na rolę MiŚP w rozwoju społeczeństwa i zachęcenie decydentów do pomocy w podnoszeniu poziomu wykorzystania nowych rozwiązań przez tę grupę firm. Istotnym elementem tego opracowania jest przedstawienie analizy dotyczącej wykorzystania technologii informacyjnych i komunikacyjnych w działalności biznesu lokalnego w celu poprawy osiąganych wyników biznesowych.Small and Medium Scale Enterprises contribute to local economies by bringing growth and innovation to the community. This type of businesses also helps in stimulating economic growth by means of providing employment opportunities to the group of people who may not be employable by larger industries. Small businesses provide opportunities to talented people who invent new products, services or implement new solutions for existing ideas. Furthermore, larger businesses as well benefited from small businesses within the same local community, as many large industries depend on small businesses for the completion of various business functions through outsourcing. This paper is aimed at highlighting the contribution of small businesses to growth of the local economy. Our objective is to draw the attention of readers about the role of SME is the development of the society and to encourage people to help in uplifting these classes of industries. The significant point of this study is to give an insight of using information and communication technologies in local community business to improve reachabilty of this class of industries

SVM-based Analysis for Predicting Success Rate of Interest Packets in Information Centric Networks

A consumer in Information Centric Network (ICN) generates an Interest packet by specifying the name of the required content. As the network emphasizes on content retrieval without much bothering about who serves it (a cache location or actual producer), every Content Router (CR) either provides the requested content back to the requester (if exists in its cache) or forwards the Interest packet to the nearest CR. While forwarding an Interest packet, the ICN routing by default does not provide any mechanism to predict the probable location of the content searched. However, having a predictive model before forwarding may significantly improve content retrieval performance. In this paper, a machine learning (ML) algorithm, particularly a Support Vector Machine (SVM) is used to forecast the success of the Interest packet. A CR can then send an Interest packet in the outgoing interface which is forecasted successful. The objective is to maximize the success rate which in turn minimizes content search time and maximizes throughput. The dataset used in is generated from a simulation topology designed in ndnSim comprising 10 K data points having 10 features. The linear, RBF and the polynomial kernel (with degree 3) are used to analyze the dataset. The polynomial kernel shows the best behavior with 98% accuracy. A comparative retrieval time with and without ML demonstrates around 10% improvement with SVM enable forwarding compared to normal ICN forwarding