Malicious Intrusions Research Articles

Blockchain for precision irrigation: Opportunities and challenges

AbstractIn modern cities, smart irrigation systems are designed to operate via Internet of things (IoT) based sensor units having precise measurements of irrigation requirements such as amount of water, crop temperature, and humidity to build a robust supply chain ecosystem. The usage of sensors and networking units enable the optimal usage of irrigation resources, and is termed as precision irrigation (PI). Thus, PI leverage an efficient solution to handle the scarcity of essential resources such as food, water, land units, and crop yields. Thus, farmers gets better returns in the market due to high production. However, in PI, the exchange of crop readings from sensor units to actuators are processed through open channels, that is, Internet. Thus, it open the doors for malicious intruders to deploy network and sensor‐based attacks on PI‐sensors, to drain the available resources, and battery power of sensor nodes in the network. This reduces the optimum and precise utilization of irrigation resources, low‐yield crops and damaged crops in supply chain systems. This leads to dissatisfaction among agriculture stakeholders such as quality control units, logistics, suppliers, and customers. Motivated from the above discussions, the survey presents the advantages of integrating blockchain (BC) with PI to handle issues pertaining to security, trust, and transactional payments among agriculture stakeholders. The survey is directed to achieve threefold objective‐ attack models and countermeasures in PI systems, integration of BC in PI to mitigate attack models, and research challenges in deploying BC in PI. To address the first objective, the survey proposes an in‐depth comparative analysis of traditional irrigation systems with PI, with discussions on attack models. To address the second objective, the survey proposes an integration model of BC with PI to secure IoT sensors, and maintain trust and transparency among stakeholders. Finally, the survey addresses the open research challenges of deploying BC in PI‐based irrigation systems, and presents a case‐study of AgriChain as an industry ready‐solution that envisions BC with PI ecosystem. Thus, the proposed survey acts as a roadmap for agriculture industry stakeholders, researchers, to deploy BC in IoT‐based PI that leverages an efficient, robust, trust‐worthy, and secure ecosystem.

Reinforcement Learning Empowered IDPS for Vehicular Networks in Edge Computing

As VANETs have been widely applied in various fields including entertainment and safety- related applications like autonomous driving, malicious intrusions into VANETs may lead to disastrous results. Hence, intrusion detection accuracy as well as efficiency is sensitive to the normal operation of VANETs. Regarding this, in this article we propose an architecture of IDPS for VANETs. One of the highlights of the architecture is that it applies RL throughout the architecture in order to deal with the dynamics of VANETs and to make proper decisions according to current VANETs states, aiming at high detection accuracy. On the other hand, the architecture is deployed in EC in an attempt to obtain low detection latency with high processing efficiency, since VANETs IDPS is sensitive to latency, especially for safety applications. A case study is conducted to assess the validity of the proposed VANETs IDPS in EC, with the results revealing that it holds the capacity to detect and prevent intrusion in VANETs in complex environments.

TEACHING AND LEARNING OF INDUSTRIAL CYBER SECURITY TECHNOLOGIES BASED ON PHOENIX CONTACT COMPANY WIRELESS EQUIPMENT

Nowadays occurs a jump in approaches to maritime technical systems developing with implementation of Industry 4.0, IIoT, Shipping 4.0 concepts. Progress in wireless technologies allows to perform absolutely new engineering tasks. Maritime branch realizes digital transformation steps, which envisage creation of unmanned, autonomous and remote controlled ships. But such systems are vulnerable for external malicious intrusion. Thus it's necessary to deepen information technologies learning in maritime education in the following directions: IIoT, industrial wired and wireless data transfer technologies and hardware; satellite systems; big data, artificial intelligence, virtual and augmented reality; remote control; cyber security technologies. National University "Odessa Maritime Academy" participates in Trainings in Automation Technologies for Ukraine project. Obtained mobile equipment allows to study PcWorx and CoDeSys software for automation systems development (based on PLCs) and Profibus, ProfiNet, EtherCAT and wireless technologies. This base equipment may be supplemented by security firewalls. 
 Ways of modern technologies implementing in maritime branch are analyzed. Directions of deep studying are shown. Actuality and possibilities of cyber security technologies studying are highlighted. Approaches and technologies, successfully realized in education process and planned for future, are described.

Towards Secure Data Forwarding with ANFIS and Trust Evaluation in Wireless Sensor Networks

The rapid growth of Wireless Networking and MEMS augmented the human lifestyle. The combination of Wireless Sensor Networks (WSN) with Artificial Intelligence (AI) payed way to development of enormous application which can think like human and predict better results. Nowadays, AI assisted WSN applications are used in many places like border security, traffic monitoring and management, automated vehicles and much more which helps and saves human life. However, the possibility of adverse attacks on sensor nodes are also increasing. There are several reports on malicious intrusions and data leakage in WSN due to poor security mechanism. In this proposal, a novel Trust based Routing solution is proposed with the employment of ANFIS and NN with Trust algorithm. The experimental results of ATE protocol has proven that the protocol secure the WSN with higher lifetime and confidential data forwarding.

MANET Security Appraisal: Challenges, Essentials, Attacks, Countermeasures & Future Directions

Currently, Mobile Ad hoc Networks (MANETs) have evolved as one of the essential next-generation wireless network technologies. MANET comprises of mobile nodes that are self-configurable, and every mobile node behaves as a router for every other node allowing data to move by making use of multi-hop network routes. MANETs signify a networking class that is crucial and differs from conventional systems. Although MANETs are being popularly employed in commercial as well as academic fields, these were primarily designed for deployment in areas like military battlefields, emergency rescue and search operations, and other challenging or hostile environments. The distributed and wireless nature of MANETs paves the way for intruders to reduce MANET functionalities. MANET are susceptible to various attack at different layers since the majority of MANET routing protocols are designed with the assumption that no malicious intruder is present in the network. Therefore, recognizing those threats and finding solutions for their mitigation become essential. This study analyses various security attributes, challenges, attacks on multiple layers and countermeasures for thwarting attacks in MANETs.

Interference and Intrusion in Wireless Sensor Networks

Wireless sensor network (WSN) systems for safety-critical, space and Internet of Things applications have recently begun to adopt open standards and commercial-off-the-shelf equipment, and persistently face challenges of malicious intrusion and spectrum coexistence. These threats are explored through Monte-Carlo simulation and benchtop testing, including matched protocol interference and sophisticated, interactive intrusion attacks. The need for expanding intrusion detection via a more holistic approach, whilst simultaneously improving WSN security, is illustrated. Discussions on WSN security, vulnerabilities, and attacks are also provided.

Prudently Secure Information Theoretic LSB Steganography for Digital Grayscale Images

The endangerment of online data breaches calls for exploring new and enhancing existing sneaky ways of clandestine communication to tailor those to match the present and futuristic technological and environmental needs, to which malicious intruders wouldn't have an answer. Cryptography and Steganography are the two distinct techniques that, for long, have remained priority choices for hiding vital information from the unauthorized. But the visibility of the encrypted contents makes these vulnerable to attack. Also, the recent legislative protection agreed to law enforcement authorities in Australia to sneak into pre-shared cryptographic secret keys (PSKs) shall have a devastating impact on the privacy of the people. Hence, the need of the hour is to veil in the encrypted data underneath the cover of Steganography, whose sole intent is to hide the very existence of information. This research endeavor enhances one of the most famous images Steganography technique called the Least Significant Bit (LSB) Steganography, from the security and information-theoretic standpoint by taking a known-cover and known-message attack scenario. The explicit proclamation of this research endeavor is that the security of LSB Steganography lies in inducing uncertainty at the time of bit embedding process. The test results rendered by the proposed methodology confers on the non-detectability and imperceptibility of the confidential information along with its strong resistance against LSB Steganalysis techniques.

SECURED AODV TO PROTECT WSN AGAINST MALICIOUS INTRUSION

One of the security issues in Wireless Sensor Networks (WSN) is intrusion detection. In this paper, we propose a new defense mechanism based on the Ad hoc On-Demand Vector (AODV) routing protocol. AODV is a reactive protocol designed for ad hoc networks and has excellent flexibility to be adapted to a new secure version. The main objective of the proposed secured AODV routing protocol is to protect WSN against malicious intrusion and defend against adversary attacks. This secured AODV protocol works well with the WSN dynamics and topology changes due to limited available resources. It establishes secure multi-hop routing between sensor nodes with high confidence, integrity, and availability. The secured AODV utilizes an existing intrusion dataset that facilitates new collection from all the exchanged packets in the network. The protocol monitors end to end delay and avoid any additional overhead over message transfer between sensor nodes. The experimental results showed that this secured AODV could be used to fight against malicious attacks such as black hole attacks and avoid caused large transmission delays.

HADIoT: A Hierarchical Anomaly Detection Framework for IoT

The Internet of Things establishes the intimacy between the Internet and the physical world. Due to portable size, most IoT devices have limited computing and storage capabilities and are vulnerable to various malicious intrusions. Therefore, it is vital to have efficient approaches to distinguish the true IoT data from fake one, we term such methods as anomaly detection (AD). To detect anomalies accurately and efficiently, in this article a 3-hierarchy joint local and global anomaly detection framework, HADIoT, is proposed, in which IoT devices generate and transmit sensory data to their local edge servers for local AD after data refinement which includes re-framing, normalization, complexity reduction via Principal Component Analysis, and symbol mapping. High detection accuracy is achieved by jointly local and global ADs. The local AD focuses on the data pattern consistency of individual devices via the Gated Recurrent Unit, and the processed data is then forwarded from edge servers to the cloud server for global AD. The global AD focuses on the analysis of the data pattern correlations between different IoT devices, using the Conditional Random Fields. For the maintenance of cyber-security, the proposed anomaly detection framework HADIoT enables to provide an accurate and faster anomaly detection for IoT applications, compared with existing anomaly detection methods. The performance of the proposed method is also empirically evaluated through simulations, using a real dataset - the Information Security Center of Excellence (ISCX) 2012 dataset. Simulation results demonstrate the effectiveness of the proposed framework in terms of True Positive Rate, False Positive Rate, Precision, Accuracy and F_score, compared with three benchmark schemes.

Sustainable Software Agent Programming

Since all network vulnerabilities cannot be predicted and detected in advance and malicious intruders cannot prevent penetration into the system in any case, Intrusion Detection System (IDS) is essential to the security of a network system. Intrusion detection system technology based on mobile agents has been commonly utilized over the last several years to detect intrusion via the distributed network.Software agents are software components that run on the display device to aid, or take responsibility for, the purchase of physical information. These agents operate on the device's standard operating system and utilize low-level memory access requests from the Application Programming Interface (API) or use a specialized operating scheme for data acquisition. The system should be available and allow customized software to be executed for this strategy. A dedicated analyst interface agent presents the output of the multiagent detection layer to the operator which retrieves more detailed information to facilitate incident analysis. Our efficiency findings demonstrate the possibility to combine high speed hardware with the sophisticated agent software based on agents

Bayesian network and game theory risk assessment model for third-party damage to oil and gas pipelines

Tremendous amounts of oil and gas products are transported in pipelines worldwide resulting in increasing interest to identify the hazards and evaluate the associated risks associated with this critical infrastructure. Third-party intrusion is one of the least quantifiable factors being considered during the pipeline hazard assessment stage despite the substantial contributing to the total number of oil and gas pipeline incidents. This is because a probabilistic risk assessment cannot reliably model human actions and be applied to intentional acts. Due to the distinctive motivations of third-party damage, an unintentional third-party damage Bayesian Network model and a game-theoretic model on malicious intrusion will therefore be built, to examine the mechanism of pipeline failure caused by this mode. This study is conducted aiming at investigating pipeline risk resulting from third-party damage, and will formulate risk assessment models to identify threats, prioritize risks and determine which integrity plan should apply to different pipeline segments given the condition of third-party interference (both the accidental damage and malicious acts).

Topology Verification Enabled Intrusion Detection for In-Vehicle CAN-FD Networks

Controller area network with flexible data rate (CAN-FD) is a widely used communication protocol for in-vehicle sensing and control. However, due to the lack of inherent security mechanisms, unauthorized devices could access the CAN-FD by embedding external intruding devices (XIDs) to in-vehicle networks. Malicious intrusion into CAN-FD can expose a compromised vehicle to significantly increased safety, security, and privacy related risks. To enhance the security of CAN-FD networks, a novel intrusion detection method based on verification of network topology is proposed, where XIDs can be reliably detected through a simple random walk based network topology construction and subsequent verification. When an intrusion is detected, a secure mode would be triggered to further protect the network from being attacked. Simulation results indicate that multiple XIDs can be accurately detected, while the increment in the number of XIDs from 1 to 8 can lead to the convergence time increasing from 48 to 102 steps in the powertrain subnet and from 189 to 416 steps in the body subnet.

Adaptive System to Improve Decision Making for Protecting Data Conveyed over WLAN

In the past two decades, the rapid growth of demand for wireless data communications has become evident due to the unreliability of wired data communications, which has become inadequate due to high costs and management monitoring of its security problems. Furthermore, such data may be intercepted when transmitted via wire connections with a negative attack and its contents changed with illegal modification. Therefore, due to these circumstances, there is an urgent need to protect data and achieve a wireless local area network (WLAN) for transmission. Data protection is therefore required through the design and implementation of a rigid algorithm that reduces the risk of hacking into wireless networks and prevents hackers from finding inevitable vulnerabilities in the data protection system. This research paper introduces a new technology to protect data from potential risks and provides a new “type” of encryption algorithms that minimizes these risks. This technology relies on the adoption of renewable rules proposed as production bases based on ambiguous logic to accomplish the construction of an intelligent data analysis system and move forward with appropriate protection decisions. The targeted packets for the local area network were captured and analyzed using open source software “WIRESHARK”. Unsupervised learning classifier was used to monitor the network and detect the malicious intruders. The flow data have been collected and features are extracted and analyzed to examine the transmitted packets which were classified and ranked to have a specific cluster. Minimizing and comprising risk taking in consideration the imprecise data which was achieved via fuzzy rules. The results were discussed and concluded that potential risks could be minimized by the production rules that control the proposed data for the transferred encryption system. MATLAB 2014 toolkit based on a laptop with an Intel I3 processor and 4GB RAM was used.

A Hidden Markov Model Combined With Markov Games for Intrusion Detection in Cloud

Cloud computing has evolved as a new paradigm for management of an infrastructure and gained ample consideration in both industrial and academic area of research. A hidden Markov model (HMM) combined with Markov games can give a solution that may act as a countermeasure for many cyber security threats and malicious intrusions in a network or in a cloud. A HMM can be trained by using training sequences that may be obtained by analyzing the file traces of packet analyzer like Wireshark network analyzer. In this article, the authors have proposed a model in which HMM can be build using a set of training examples that are obtained by using a network analyzer (i.e., Wireshark). As it is not an intrusion detection system, the obtained file traces may be used as training examples to test a HMM model. It also predicts a probability value for each tested sequence and states if sequence is anomalous or not. A numerical example is also shown in this article that calculates the most optimal sequence of observations for both HMM and state sequence probabilities in case a HMM model is already given.

Intrusion Detection Techniques for Secure Communication in Different Wireless Networks

Technological advancement in the design of wireless communication have propelled an active interest in the field of Wireless Networks, Wireless Sensor Networks (WSNs), and Mobile Adhoc Networks (MANETs). Now days the speed and privacy are more reason of concern than the performance. The attacks can occur and there is always a chance that it will be a success. One of the major problems with Wireless Network security is that, all types of attacks are not known, and new ones emerge constantly [6]. Moreover, there is also a range of attacks that can be launched in the different mode, and thus making it more difficult for the Intrusion Detection System (IDS) to detect them. Therefore, main approach in network security is to detect and remove malicious intrusions. In this paper three different techniques have been proposed for securing Wireless LAN, WSNs and MANETs.

Authentication and Authorization Mechanism for Cloud Security

Cloud Computing is a most widespread and popular form of computing, promising high reliability for customers and providers both at the same point of time for many fields, where cloud storage security is based on Authentication and Authorization in cloud computing. Data is uploaded into a cloud and stored in a datacenter, Authentication and authorization are the major concerns to access the data stored in cloud by users from the data center. Security is a major issue; these are mainly deal with identity and access management, prevention of data loss and malware attack control management. In this paper we majorly concentrated on Authentication and Authorization to cloud access, we focused on identity management mechanism as cloud security solution that provides directory services for application access management. We discussed the protocols that support authorization and allows the communication across applications with the help of tokens instead of credentials. We even concentrated on the different mechanisms which plays a major role in designing a secured cloud computing architecture from malicious intrusions and attacks, it is a step to verify the presence and functioning of the cloud customers and cloud providers through security mechanisms to protect from different risks, threats and attacks. In this paper we discussed different security Algorithms and Authentication architecture along with the proposed algorithm, where analysis is done along with the computational evaluation with output

Pseudonym-Based Privacy Preserving Framework for Facilitating Cloud Data Security

The advent of cloud computing has revolutionized the option of sharing cloud resources among the cloud users for minimizing the cost overhead. But, the cloud data security is considered as the predominant issue that need to be addressed through the implementation of privacy preserving approaches that sustains and prevents the cloud resources and users from being compromised by the malicious intruders. In this paper, a Pseudonym-based Privacy Preservation Framework (PBPRF) is proposed for understanding its potential towards the accuracy and privacy preservation of cloud data based on the concept of P-Gene. This proposed PBPRF incorporates the benefits of the P-Gene which is responsible in the cloud space for providing security for the stored and utilized private data in the cloud that are periodically exchanged with the clients of the cloud environment. This proposed PBPRF scheme ensures secure sharing of data by relying on a trustworthy data aggregation scheme which is fully dependent on erasable data hiding technique. The simulation experiments and results of the proposed PBPRF mechanism is compared with the baseline PRIAS and TPAAAS techniques in terms of pseudonym generation cost, pseudonym verification, execution time and pseudonym verification time under batched and separated environment.

Enforcing opacity by insertion functions under multiple energy constraints

This paper investigates the enforcement of opacity by insertion functions subject to multiple quantitative constraints capturing resource or energy limitations. There is a malicious intruder attempting to infer secrets of the system from its observations. To prevent the disclosure of secrets, the insertion function inserts fictitious events to the output of the system to obfuscate the intruder. The system is initialized with several types of resources, referred to as energy. The energy is consumed or replenished with event occurrences while always consumed with event insertions. The insertion function must enforce opacity while ensuring that each type of resource is never depleted. This problem is then reduced to a two-player game between the insertion function and the system (environment), with properly defined objectives. A game structure called the Energy Insertion Structure, denoted by EIS is proposed, which provably contains solutions to the energy constrained opacity enforcement problem. Then we further study the bounded cost rate insertion problem on the insertion function’s winning region of EIS, which requires that the long run average rate of insertion cost be bounded. This problem is formulated as a multidimensional mean payoff game and a special method called hyperplane separation technique is applied to efficiently solve it.

10 Steps to Strategically Build and Implement your Enterprise Imaging System: HIMSS-SIIM Collaborative White Paper

An enterprise imaging (EI) strategy is an organized plan to optimize the electronic health record (EHR) so that healthcare providers have intuitive and immediate access to all patient clinical images and their associated documentation, regardless of source. We describe ten steps recommended to achieve the goal of implementing EI for an institution. The first step is to define and access all images used for medical decision-making. Next, demonstrate how EI is a powerful strategy for enhancing patient and caregiver experience, improving population health, and reducing cost. Then, it is recommended that one must understand the specialties and their clinical workflow challenges as related to imaging. Step four is to create a strategy to improve quality of care and patient safety with EI. Step five demonstrates how EI can reduce costs. Then, show how EI can help enhance the patient experience. Step seven suggests how EI can enhance the work life of caregivers and step eight describes how to develop EI governance. Step nine describes the plan to implement an EI project, and finally, step 10, to understand cybersecurity from a patient safety perspective and to protect images from accidental and malicious intrusion.

Sensor network security defense strategy based on attack graph and improved binary PSO

Attack graph is a common tool for qualitative analysis of Sensor network security and it can provide an important basis for security administrators to prevent malicious intrusion. In order to conduct Sensor network security assessment and active defense, this paper proposed a Sensor network security defense strategy based on attack graphs and improved binary PSO. Based on each intrusion action in the attack graph, it constructed a set of weighted defense strategies to emphasize the defense cost. In order to prevent Sensor network malicious intrusion with minimum cost, the strategy introduced and improved binary particle swarm optimization algorithm and obtains the minimum key strategy set of attack graph. Based on the principle of M-IDS combined with game theory and attack pattern mining algorithm of Markov Decision Process (MDP), the optimal protection strategy is determined by game theory, and MDP is used to predict future attacks and design corresponding protection strategies. Simulation experiments show that compared with the ant colony algorithm and greedy algorithm, the proposed strategy can effectively obtain the optimal solution of the minimum key strategy set and it is more efficient.