EWSN'15 Industry Session - Francesco Flammini (Ansaldo STS)

This document proposes a secure communication framework for embedded networking. The framework aims to be platform neutral and hardware independent. It incorporates a secured database containing all possible system events. Embedded systems can authenticate and access this database. The framework also protects the communication channel by encrypting transmissions, secure handshaking, and using self-adaptive frame structures. It references event indexes from the database rather than transmitting actual information. The database is dynamically recreated on each power-on to change event indexes and improve security. The framework is designed to be easily implemented using proven security technologies while providing modest protection for embedded network devices.

Passive network monitoring techniques can provide valuable situational awareness for network security professionals. The document describes techniques for passively discovering information about nodes on a network, including operating systems, roles, services, and configurations. This contextual information helps analysts by reducing false positives and focusing resources. The passive approach does not disrupt networks and can operate continuously, in contrast to active scanning tools. A network monitoring prototype is being developed to test these passive discovery techniques.

IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com

This document discusses security issues related to wireless sensor networks. It begins with an introduction to wireless sensor networks and an overview of security challenges due to limited sensor node capabilities. It then summarizes common attacks on different layers of wireless sensor networks and discusses security objectives. The document outlines key areas of research on sensor network security including key management, secure time synchronization, and secure routing. It provides details on different key management schemes, time synchronization protocols, and discusses vulnerabilities of existing synchronization schemes to various attacks.

This document summarizes a research paper that classifies different types of networks and discusses their associated security issues. It categorizes networks based on size (LAN, MAN, WAN), design (peer-to-peer, client-server, standalone), layering (layered, non-layered), and provides examples such as Ethernet, Wi-Fi, VPNs. It also discusses common security threats for different network types like viruses, denial of service attacks, and evaluates security measures including encryption, firewalls, access control. The paper aims to provide a comprehensive classification of networks and analyze how security needs vary depending on the network and software development stages.

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This document provides an introduction to cryptography and network security. It defines cryptography as the study of secret writing and discusses its use in securing communications and verifying messages. The document outlines basic concepts in cryptography like plaintext, ciphertext, ciphers, keys, encryption, and decryption. It also discusses different types of security, like unconditional security based on information theory versus computational security based on limiting an attacker's resources. The overall document serves as a high-level overview of cryptography and security.

This document discusses intrusion detection in mobile ad hoc networks (MANETs). It begins with background on intrusion detection systems (IDS) in general and why they are important for MANETs given their vulnerabilities. It then discusses three key aspects of IDS for MANETs: common attacks on MANETs, architectures for IDS in MANETs, and achievements in research on IDS for MANETs. Specifically, it reviews research on IDS architectures, detection techniques, resistance to attack types, and applicability to different routing protocols. The document provides an overview of issues and approaches regarding securing MANETs through intrusion detection.

This document proposes a hybrid architecture for a distributed intrusion detection system using multiple agents. The key aspects of the architecture include: - Using multiple independent tracker agents that monitor hosts and generate reports sent to monitors and storage. - Monitors analyze activity and compare to signatures to detect known attacks, or send data to anomaly detectors. - Anomaly and misuse detectors use classification and pattern matching to detect known and unknown attacks. - An inference module coordinates entities across hosts to classify new attacks using a knowledge base and signature generator. - A countermeasure module alerts administrators and can take actions like dropping packets in response to detected attacks.

Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network (Inception-CNN), Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning models in MANET.

The document proposes two techniques - periodic collection and source simulation - to prevent leakage of location information in sensor networks from a global eavesdropper. Periodic collection provides high location privacy while source simulation provides tradeoffs between privacy, communication cost, and latency. The techniques are efficient and effective at providing source and sink location privacy compared to existing methods that only defend against local adversaries.

Wireless Sensor Network(WSN) is an emerging technology and explored field of researchers worldwide in the past few years, so does the need for effective security mechanisms. The sensing technology combined with processing power and wireless communication makes it lucrative for being exploited in abundance in future. The inclusion of wireless communication technology also incurs various types of security threats due to unattended installation of sensor nodes as sensor networks may interact with sensitive data and /or operate in hostile unattended environments. These security concerns be addressed from the beginning of the system design. The intent of this paper is to investigate the security related issues in wireless sensor networks. In this paper we have explored general security threats in wireless sensor network with extensive study.