Biblio
For the security of mobile ad-hoc networks (MANETs), a group of wireless mobile nodes needs to cooperate by forwarding packets, to implement an intrusion detection system (IDS). Some of the current IDS implementations in a clustered MANET have designed mobile nodes to wait until the cluster head is elected before scanning the network and thus nodes may be, unfortunately, exposed to several control packet attacks by which nodes identify falsified routes to reach other nodes. In order to detect control packet attacks such as route falsification, we design a route cache sharing mechanism for a non-clustered network where all one-hop routing data are collected by each node for a cooperative host-based detection. The cooperative host-based detection system uses a Support Vector Machine classifier and achieves a detection rate of around 95%. By successfully detecting the route falsification attacks, nodes are given the capability to avoid other attacks such as black-hole and gray-hole, which are in many cases a result of a successful route falsification attack.
Software Defined Networking (SDN) has introduced both innovative opportunities and additional risks in the computer networking. Among disadvantages of SDNs one can mention their susceptibility to vulnerabilities associated with both virtualization and the traditional networking. Selecting a proper controller for an organization may not be a trivial task as there is a variety of SDN controllers on the market and each of them may come with its own pros and cons from the security point of view. This research proposes a comprehensive methodology for organizations to evaluate security-related features available in SDN controllers. The methodology can serve as a guideline in the decisions related to SDN choice. The proposed security assessment follows a structured approach to evaluate each layer of the SDN architecture and each metrics defined in presented research has been matched with the security controls defined in NIST 800-53. Through the tests on actual controllers the paper provides an example on how the proposed methodology can be used to evaluate existing SDN solutions.
This paper shows that stochastic heuristic approach for implicitly solving addition chain problem (ACP) in public-key cryptosystem (PKC) enhances the efficiency of the PKC and improves the security by blinding the multiplications/squaring operations involved against side-channel attack (SCA). We show that while the current practical heuristic approaches being deterministic expose the fixed pattern of the operations, using stochastic method blinds the pattern by being unpredictable and generating diffident pattern of operation for the same exponent at a different time. Thus, if the addition chain (AC) is generated implicitly every time the exponentiation operation is being made, needless for such approaches as padding by insertion of dummy operations and the operation is still totally secured against the SCA. Furthermore, we also show that the stochastic approaches, when carefully designed, further reduces the length of the operation than state-of-the-art practical methods for improving the efficiency. We demonstrated our investigation by implementing RSA cryptosystem using the stochastic approach and the results benchmarked with the existing current methods.
The process of release of a single domain wall from the closure domain structure at the microwire ends and the process of nucleation of the reversed domain in regions far from the microwire ends were studied using the technique that consists in determining the critical parameters of the rectangular magnetic field pulse (magnitude-Hpc and length-τc) needed for free domain wall production. Since these processes can be influenced by the magnitude of the magnetic field before or after the application of the field pulse (Hi, τ), we propose a modified experiment in which the so-called three-level pulse is used. The three-level pulse starts from the first level, then continues with the second measuring rectangular pulse (Hi, τ), which ends at the third field level. Based on the results obtained in experiments using three-level field pulses, it has been shown that reversed domains are not present in the remanent state in regions far from the microwire ends. Some modification of the theoretical model of a single domain wall trapped in a potential well will be needed for an adequate description of the depinning processes.
The greatest threat towards securing the organization and its assets are no longer the attackers attacking beyond the network walls of the organization but the insiders present within the organization with malicious intent. Existing approaches helps to monitor, detect and prevent any malicious activities within an organization's network while ignoring the human behavior impact on security. In this paper we have focused on user behavior profiling approach to monitor and analyze user behavior action sequence to detect insider threats. We present an ensemble hybrid machine learning approach using Multi State Long Short Term Memory (MSLSTM) and Convolution Neural Networks (CNN) based time series anomaly detection to detect the additive outliers in the behavior patterns based on their spatial-temporal behavior features. We find that using Multistate LSTM is better than basic single state LSTM. The proposed method with Multistate LSTM can successfully detect the insider threats providing the AUC of 0.9042 on train data and AUC of 0.9047 on test data when trained with publically available dataset for insider threats.
Recently, the novel networking technology Software-Defined Networking(SDN) and Service Function Chaining(SFC) are rapidly growing, and security issues are also emerging for SDN and SFC. However, the research about security and safety on a novel networking environment is still unsatisfactory, and the vulnerabilities have been revealed continuously. Among these security issues, this paper addresses the ARP Poisoning attack to exploit SFC vulnerability, and proposes a method to defend the attack. The proposed method recognizes the repetitive ARP reply which is a feature of ARP Poisoning attack, and detects ARP Poisoning attack. The proposed method overcomes the limitations of the existing detection methods. The proposed method also detects the presence of an attack more accurately.
This paper investigates the effectiveness of reinforcement learning (RL) model in clustering as an approach to achieve higher network scalability in distributed cognitive radio networks. Specifically, it analyzes the effects of RL parameters, namely the learning rate and discount factor in a volatile environment, which consists of member nodes (or secondary users) that launch attacks with various probabilities of attack. The clusterhead, which resides in an operating region (environment) that is characterized by the probability of attacks, countermeasures the malicious SUs by leveraging on a RL model. Simulation results have shown that in a volatile operating environment, the RL model with learning rate α= 1 provides the highest network scalability when the probability of attacks ranges between 0.3 and 0.7, while the discount factor γ does not play a significant role in learning in an operating environment that is volatile due to attacks.
In this paper, we outline a novel, forward error correction-based information hiding technique for adaptive rate wireless communication systems. Specifically, we propose leveraging the functionality of wireless local area network modulation and coding schemes (MCS) and link adaptation mechanisms to significantly increase covert channel throughput. After describing our generalized information hiding model, we detail implementation of this technique within the IEEE 802.11ad, directional multi-Gigabit standard. Simulation results demonstrate the potential of the proposed techniques to develop reliable, high-throughput covert channels under multiple MCS rates and embedding techniques. Covert channel performance is evaluated in terms of the observed packet error ratio of the underlying communication system as well as the bit error ratio of the hidden data.
The quantity of Internet of Things (IoT) devices in the marketplace and lack of security is staggering. The interconnectedness of IoT devices has increased the attack surface for hackers. "White Worm" technology has the potential to combat infiltrating malware. Before white worm technology becomes viable, its capabilities must be constrained to specific devices and limited to non-harmful actions. This paper addresses the current problem, international research, and the conflicting interest of individuals, businesses, and governments regarding white worm technology. Proposed is a new perspective on utilizing white worm technology to protect the vulnerability of IoT devices, while overcoming its challenges.
With the growing use of the Robot Operating System (ROS), it can be argued that it has become a de-facto framework for developing robotic solutions. ROS is used to build robotic applications for industrial automation, home automation, medical and even automatic robotic surveillance. However, whenever ROS is utilized, security is one of the main concerns that needs to be addressed in order to ensure a secure network communication of robots. Cyber-attacks may hinder evolution and adaptation of most ROS-enabled robotic systems for real-world use over the Internet. Thus, it is important to address and prevent security threats associated with the use of ROS-enabled applications. In this paper, we propose a novel approach for securing ROS-enabled robotic system by integrating ROS with the Message Queuing Telemetry Transport (MQTT) protocol. We manage to secure robots' network communications by providing authentication and data encryption, therefore preventing man-in-the-middle and hijacking attacks. We also perform real-world experiments to assess how the performance of a ROS-enabled robotic surveillance system is affected by the proposed approach.
Reconnaissance might be the longest phase, sometimes take weeks or months. The black hat makes use of passive information gathering techniques. Once the attacker has sufficient statistics, then the attacker starts the technique of scanning perimeter and internal network devices seeking out open ports and related services. In this paper we are showing traffic accountability and time to complete the specific task during reconnaissance phase active scanning with nmap tool and proposed strategies that how to deal with large volumes of hosts and conserve network traffic as well as time of the specific task.
Smart technologies at hand have facilitated generation and collection of huge volumes of data, on daily basis. It involves highly sensitive and diverse data like personal, organisational, environment, energy, transport and economic data. Data Analytics provide solution for various issues being faced by smart cities like crisis response, disaster resilience, emergence management, smart traffic management system etc.; it requires distribution of sensitive data among various entities within or outside the smart city,. Sharing of sensitive data creates a need for efficient usage of smart city data to provide smart applications and utility to the end users in a trustworthy and safe mode. This shared sensitive data if get leaked as a consequence can cause damage and severe risk to the city's resources. Fortification of critical data from unofficial disclosure is biggest issue for success of any project. Data Leakage Detection provides a set of tools and technology that can efficiently resolves the concerns related to smart city critical data. The paper, showcase an approach to detect the leakage which is caused intentionally or unintentionally. The model represents allotment of data objects between diverse agents using Bigraph. The objective is to make critical data secure by revealing the guilty agent who caused the data leakage.
Securing Internet of things is a major concern as it deals with data that are personal, needed to be reliable, can direct and manipulate device decisions in a harmful way. Also regarding data generation process is heterogeneous, data being immense in volume, complex management. Quantum Computing and Internet of Things (IoT) coined as Quantum IoT defines a concept of greater security design which harness the virtue of quantum mechanics laws in Internet of Things (IoT) security management. Also it ensures secured data storage, processing, communication, data dynamics. In this paper, an IoT security infrastructure is introduced which is a hybrid one, with an extra layer, which ensures quantum state. This state prevents any sort of harmful actions from the eavesdroppers in the communication channel and cyber side, by maintaining its state, protecting the key by quantum cryptography BB84 protocol. An adapted version is introduced specific to this IoT scenario. A classical cryptography system `One-Time pad (OTP)' is used in the hybrid management. The novelty of this paper lies with the integration of classical and quantum communication for Internet of Things (IoT) security.
This research proposes an inspection on Trust Based Routing protocols to protect Internet of Things directing to authorize dependability and privacy amid to direction-finding procedure in inaccessible systems. There are number of Internet of Things (IOT) gadgets are interrelated all inclusive, the main issue is the means by which to protect the routing of information in the important systems from different types of stabbings. Clients won't feel secure on the off chance that they know their private evidence could without much of a stretch be gotten to and traded off by unapproved people or machines over the system. Trust is an imperative part of Internet of Things (IOT). It empowers elements to adapt to vulnerability and roughness caused by the through and through freedom of other devices. In Mobile Ad-hoc Network (MANET) host moves frequently in any bearing, so that the topology of the network also changes frequently. No specific algorithm is used for routing the packets. Packets/data must be routed by intermediate nodes. It is procumbent to different occurrences ease. There are various approaches to compute trust for a node such as fuzzy trust approach, trust administration approach, hybrid approach, etc. Adaptive Information Dissemination (AID) is a mechanism which ensures the packets in a specific transmission and it analysis of is there any attacks by hackers.It encompasses of ensuring the packet count and route detection between source and destination with trusted path.Trust estimation dependent on the specific condition or setting of a hub, by sharing the setting information onto alternate hubs in the framework would give a superior answer for this issue.Here we present a survey on various trust organization approaches in MANETs. We bring out instantaneous of these approaches for establishing trust of the partaking hubs in a dynamic and unverifiable MANET atmosphere.
Security is one of the main and continual challenges that restrict government stakeholders (e.g. citizens) engagement with the cloud services. This paper has as its objective the discovery of the security perceptions of cloud-based e-government services from the citizens' and IT-staff perspectives. It investigates the factors that influence the citizen's perception of security. Little efforts have been done by previous literature to investigate and analyze the integration between citizens' concerns regarding the perceived security and those of IT -staff, the current study highlights this issue. This work provides an empirical study to understand citizens' priorities, needs and expectations regarding the perceived security of cloud-based e-government services which are a novel e-government initiative in Jordan, also enriches the existing security perceptions literature by introducing new insights. An interpretive-qualitative approach was adopted, as it helps to understand the participants' perceptions in the research natural setting.
Modern multicore System-on-Chips (SoCs) are regularly designed with third-party Intellectual Properties (IPs) and software tools to manage the complexity and development cost. This approach naturally introduces major security concerns, especially for those SoCs used in critical applications and cyberinfrastructure. Despite approaches like split manufacturing, security testing and hardware metering, this remains an open and challenging problem. In this work, we propose a dynamic intrusion detection approach to address the security challenge. The proposed runtime system (SoCINT) systematically gathers information about untrusted IPs and strictly enforces the access policies. SoCINT surpasses the-state-of-the-art monitoring systems by supporting hardware tracing, for more robust analysis, together with providing smart counterintelligence strategies. SoCINT is implemented in an open source processor running on a commercial FPGA platform. The evaluation results validate our claims by demonstrating resilience against attacks exploiting erroneous or malicious IPs.
Deception technology is used to lure, detect and defend against attacks. Deception technology should be used within organizations. There are five ways that deception technology is changing the cyber security landscape.
Raef Meeuwisse, CISM, CISA, ISACA expert speaker, and author of Cybersecurity for Beginners, has explored the different ways in which the human mind can be hacked as well as the effectiveness of these techniques. One of the techniques involves the manipulation of cognitive biases. Meeuwisse also examined how cybersecurity techniques could be used to analyze and defend against tactics used to hack the human mind.