Biblio

Recently, there has been a dramatic increase in cyber attacks around the globe. Hundreds of 0day vulnerabilities on different platforms are discovered by security researchers worldwide. The attack vectors are becoming more and more difficult to be discovered by any anti threat detection engine. Inorder to bypass these smart detection mechanisms, attackers now started carrying out attacks at extremely low level where no threat inspection units are present. This makes the attack more stealthy with increased success rate and almost zero detection rate. A best case example for this scenario would be attacks like Meltdown and Spectre that targeted the modern processors to steal information by exploiting out-of-order execution feature in modern processors. These types of attacks are incredibly hard to detect and patch. Even if a patch is released, a wide range of normal audience are unaware of this both the vulnerability and the patch. This paper describes one such low level attacks that involves the process of reverse engineering firmwares and manually backdooring them with several linux utilities. Also, compromising a real world WiFi router with the manually backdoored firmware and attaining reverse shell from the router is discussed. The WiFi routers are almost everywhere especially in public places. Firmwares are responsible for controlling the routers. If the attacker manipulates the firmware and gains control over the firmware installed in the router, then the attacker can get a hold of the network and perform various MITM attacks inside the network with the help of the router.

In today's era, the probability of the wireless devices getting hacked has grown extensively. Due to the various WLAN vulnerabilities, hackers can break into the system. There is a lack of awareness among the people about security mechanisms. From the past experiences, the study reveals that router security encrypted protocol is often cracked using several ways like dictionary attack and brute force attack. The identified methods are costly, require extensive hardware, are not reliable and do not detect all the vulnerabilities of the system. This system aims to test all router protocols which are WEP, WPA, WPA2, WPS and detect the vulnerabilities of the system. Kali Linux version number 2.0 is being used over here and therefore the tools like airodump-ng, aircrack-ng are used to acquire access point pin which gives prevention methods for detected credulity and aims in testing various security protocols to make sure that there's no flaw which will be exploited.

With increase in distributed energy resources (DERs) and smart loads, each energy resource and load need a separate power conversion system leading to complex coordination and interaction, reduced energy conversion efficiency, coordinating compliance to grid standards (IEEE 1547) from multiple sources, reduced security. Also, multiple vendors with legacy system designs and proprietary communications interfaces result in redundancy and increase in cost of power electronics systems. This paper presents an energy router concept for buildings applications which provides autonomous power flow between sources and loads with a novel agent-based software interface.

The security of wireless network devices has received widespread attention, but most existing schemes cannot achieve fine-grained device identification. In practice, the security vulnerabilities of a device are heavily depending on its model and firmware version. Motivated by this issue, we propose a universal, extensible and device-independent framework called SCAFFISD, which can provide fine-grained identification of wireless routers. It can generate access rules to extract effective information from the router admin page automatically and perform quick scans for known device vulnerabilities. Meanwhile, SCAFFISD can identify rogue access points (APs) in combination with existing detection methods, with the purpose of performing a comprehensive security assessment of wireless networks. We implement the prototype of SCAFFISD and verify its effectiveness through security scans of actual products.

The security of Industrial Control system (ICS) of cybersecurity networks ensures that control equipment fails and that regular procedures are available at its control facilities and internal industrial network. For this reason, it is essential to improve the security of industrial control facility networks continuously. Since network security is threatening, industrial installations are irreparable and perhaps environmentally hazardous. In this study, the industrialized Early Intrusion Detection System (EIDS) was used to modify the Intrusion Detection System (IDS) method. The industrial EIDS was implemented using routers, IDS Snort, Industrial honeypot, and Iptables MikroTik. EIDS successfully simulated and implemented instructions written in IDS, Iptables router, and Honeypots. Accordingly, the attacker's information was displayed on the monitoring page, which had been designed for the ICS. The EIDS provides cybersecurity and industrial network systems against vulnerabilities and alerts industrial network security heads in the shortest possible time.

Software Defined Networking (SDN) is an emerging architecture providing services on a priority basis for real-time communication, by pulling out the intelligence from the hardware and developing a better management system for effective networking. Denial of service (DoS) attacks pose a significant threat to SDN, as it can disable the genuine hosts and routers by exhausting their resources. It is thus vital to provide efficient traffic management, both at the data layer and the control layer, thereby becoming more responsive to dynamic network threats such as DoS. Existing DoS prevention and mitigation models for SDN are computationally expensive and are slow to react. This paper introduces a novel biologically inspired architecture for SDN to detect DoS flooding attacks. The proposed biologically inspired architecture utilizes the concepts of the human immune system to provide a robust solution against DoS attacks in SDNs. The two layer immune inspired framework, viz innate layer and adaptive layer, is initiated at the data layer and the control layer of SDN, respectively. The proposed model is reactive and lightweight for DoS mitigation in SDNs.

With the rapid growth of Linux-based IoT devices such as network cameras and routers, the security becomes a concern and many attacks utilize vulnerabilities to compromise the devices. It is crucial for researchers to find vulnerabilities in IoT systems before attackers. Fuzzing is an effective vulnerability discovery technique for traditional desktop programs, but could not be directly applied to Linux-based IoT programs due to the special execution environment requirement. In our paper, we propose an efficient greybox fuzzing scheme for Linux-based IoT programs which consist of two phases: binary static analysis and IoT program greybox fuzzing. The binary static analysis is to help generate useful inputs for efficient fuzzing. The IoT program greybox fuzzing is to reinforce the IoT firmware kernel greybox fuzzer to support IoT programs. We implement a prototype system and the evaluation results indicate that our system could automatically find vulnerabilities in real-world Linux-based IoT programs efficiently.

Mitigating the effect of Distributed Denial of Service (DDoS) attacks in wired/wireless networks is a problem of extreme importance. The present paper investigates this problem and proposes a secure AQM to encounter the effects of DDoS attacks on queue's router. The employed method relies on modelling the TCP/AQM system subjected to different DoS attack rate where the resulting closed-loop system is expressed as new Markovian Jump Linear System (MJLS). Sufficient delay-dependent conditions which guarantee the syntheses of a stabilizing control for the closed-loop system with a guaranteed cost J* are derived. Finally, a numerical example is displayed.

With the rapid proliferation of Internet of Thing (IoT) devices, many security challenges could be introduced at low-end routers. Misbehaving routers affect the availability of the networks by dropping packets selectively and rejecting data forwarding services. Although existing Reputation Management (RM) systems are useful in identifying misbehaving routers, the centralized nature of the RM center has the risk of one-point failure. The emerging blockchain techniques, with the inherent decentralized consensus mechanism, provide a promising method to reduce this one-point failure risk. By adopting the distributed consensus mechanism, we propose a blockchain-based reputation management system in IoT networks to overcome the limitation of centralized router RM systems. The proposed solution utilizes the blockchain technique as a decentralized database to store router reports for calculating reputation of each router. With the proposed reputation calculation mechanism, the reliability of each router would be evaluated, and the malicious misbehaving routers with low reputations will be blacklisted and get isolated. More importantly, we develop an optimized group mining process for blockchain technique in order to improve the efficiency of block generation and reduce the resource consumption. The simulation results validate the distributed blockchain-based RM system in terms of attacks detection and system convergence performance, and the comparison result of the proposed group mining process with existing blockchain models illustrates the applicability and feasibility of the proposed works.

Many modern business models of the manufacturing industry use the possibilities of digitization. In particular, the idea of connecting machines to networks and communication infrastructure is gaining momentum. However, in addition to the considerable economic advantages, this development also brings decisive disadvantages. By connecting previously encapsulated industrial networks with untrustworthy external networks such as the Internet, machines and systems are suddenly exposed to the same threats as conventional IT systems. A key problem today is the typical network paradigm with static routers and switches that cannot meet the dynamic requirements of a modern industrial network. Current security solutions often only threat symptoms instead of tackling the cause. In this paper we will therefore analyze the weaknesses of current networks and security solutions using the example of industrial remote maintenance. We will then present a novel concept of how Software-Defined Networking (SDN) in combination with a policy framework that supports attribute-based access control can be used to meet current and future security requirements in dynamic industrial networks. Furthermore, we will introduce an examplary implementation of this novel security framework for the use case of industrial remote maintenance and evaluate the solution. Our results show that SDN in combination with an Attribute-based Access Control (ABAC) policy framework is perfectly suited to increase flexibility and security of modern industrial networks at the same time.

An increasing number of IoT scenarios involve mobile, resource-constrained IoT devices that rely on untrusted networks for Internet connectivity. In such environments, attackers can derive sensitive private information of IoT device owners, e.g., daily routines or secret supply chain procedures, when sniffing on IoT communication and linking IoT devices and owner. Furthermore, untrusted networks do not provide IoT devices with any protection against attacks from the Internet. Anonymous communication using onion routing provides a well-proven mechanism to keep the relationship between communication partners secret and (optionally) protect against network attacks. However, the application of onion routing is challenged by protocol incompatibilities and demanding cryptographic processing on constrained IoT devices, rendering its use infeasible. To close this gap, we tailor onion routing to the IoT by bridging protocol incompatibilities and offloading expensive cryptographic processing to a router or web server of the IoT device owner. Thus, we realize resource-conserving access control and end-to-end security for IoT devices. To prove applicability, we deploy onion routing for the IoT within the well-established Tor network enabling IoT devices to leverage its resources to achieve the same grade of anonymity as readily available to traditional devices.

This paper presents an experimental analysis of current Distributed Denial of Service attacks. Our analysis is based on real data collected by a honeynet system that was installed on an ISP edge router, for a four-month period. In the examined scenario, we identify and analyze malicious activities based on packets captured and analyzed by a network protocol sniffer and signature-based attack analysis tools. Our analysis shows that IoT-based DDoS attacks are one of the latest and most proliferating attack trends in network security. Based on the analysis of the attacks, we describe some mitigation techniques that can be applied at the providers' network to mitigate the trending attack vectors.

As technology is developing exponentially and the world is moving towards automation, the resources have to be transferred through the internet which requires routers to connect networks and forward bundles (information). Due to the vulnerability of routers the data and resources have been hacked. The vulnerability of routers is due to minimum authentication to the network shared, some technical attacks on routers, leaking of passwords to others, single passwords. Based on the study, the solution is to maximize authentication of the router by embedding an application that monitors the user entry based on MAC address of the device, the password is frequently changed and that encrypted password is sent to a user and notifies the admin about the changes. Thus, these routers provide high-level security to the forward data through the internet.

Network-on-Chip (NoC) is the communication platform of the data among the processing cores in Multiprocessors System-on-Chip (MPSoC). NoC has become a target to security attacks and by outsourcing design, it can be infected with a malicious Hardware Trojan (HT) to degrades the system performance or leaves a back door for sensitive information leaking. In this paper, we proposed a HT model that applies a denial of service attack by deliberately discarding the data packets that are passing through the infected node creating a black hole in the NoC. It is known as Black Hole Router (BHR) attack. We studied the effect of the BHR attack on the NoC. The power and area overhead of the BHR are analyzed. We studied the effect of the locations of BHRs and their distribution in the network as well. The malicious nodes has very small area and power overhead, 1.98% and 0.74% respectively, with a very strong violent attack.

Modern Energy Management Systems (EMS) are becoming increasingly complex in order to address the urgent issue of global energy consumption. These systems retrieve vital information from various Internet-connected resources in a smart grid to function effectively. However, relying on such resources results in them being susceptible to cyber attacks. Malicious actors can exploit the interconnections between the resources to perform nefarious tasks such as modifying critical firmware, sending bogus sensor data, or stealing sensitive information. To address this issue, we propose a novel framework that integrates PowerWatch, a solution that detects compromised devices in the smart grid with Cyber-secure Power Router (CSPR), a smart energy management system. The goal is to ascertain whether or not such a device has operated maliciously. To achieve this, PowerWatch utilizes a machine learning model that analyzes information from system and library call lists extracted from CSPR in order to detect malicious activity in the EMS. To test the efficacy of our framework, a number of unique attack scenarios were performed on a realistic testbed that comprises functional versions of CSPR and PowerWatch to monitor the electrical environment for suspicious activity. Our performance evaluation investigates the effectiveness of this first-of-its-kind merger and provides insight into the feasibility of developing future cybersecure EMS. The results of our experimental procedures yielded 100% accuracy for each of the attack scenarios. Finally, our implementation demonstrates that the integration of PowerWatch and CSPR is effective and yields minimal overhead to the EMS.

In-network caching is a feature shared by all proposed Information Centric Networking (ICN) architectures as it is critical to achieving a more efficient retrieval of content. However, the default "cache everything everywhere" universal caching scheme has caused the emergence of several privacy threats. Timing attacks are one such privacy breach where attackers can probe caches and use timing analysis of data retrievals to identify if content was retrieved from the data source or from the cache, the latter case inferring that this content was requested recently. We have previously proposed a betweenness centrality based caching strategy to mitigate such attacks by increasing user anonymity. We demonstrated its efficacy in a transit-stub topology. In this paper, we further investigate the effect of betweenness centrality based caching on cache privacy and user anonymity in more general synthetic and real world Internet topologies. It was also shown that an attacker with access to multiple compromised routers can locate and track a mobile user by carrying out multiple timing analysis attacks from various parts of the network. We extend our privacy evaluation to a scenario with mobile users and show that a betweenness centrality based caching policy provides a mobile user with path privacy by increasing an attacker's difficulty in locating a moving user or identifying his/her route.

This paper proposes a new adaptively distributed packet filtering mechanism to mitigate the DDoS attacks targeted at the victim's bandwidth. The mechanism employs IP traceback as a means of distinguishing attacks from legitimate traffic, and continuous action reinforcement learning automata, with an improved learning function, to compute effective filtering probabilities at filtering routers. The solution is evaluated through a number of experiments based on actual Internet data. The results show that the proposed solution achieves a high throughput of surviving legitimate traffic as a result of its high convergence speed, and can save the victim's bandwidth even in case of varying and intense attacks.

The proliferation of the Internet of Things (IoT) in the context of smart homes entails new security risks threatening the privacy and safety of end users. In this paper, we explore the design space of in-network security for smart home networks, which automatically complements existing security mechanisms with a rule-based approach, i. e., every IoT device provides a specification of the required communication to fulfill the desired services. In our approach, the home router as the central network component then enforces these communication rules with traffic filtering and anomaly detection to dynamically react to threats. We show that in-network security can be easily integrated into smart home networks based on existing approaches and thus provides additional protection for heterogeneous IoT devices and protocols. Furthermore, in-network security relieves users of difficult home network configurations, since it automatically adapts to the connected devices and services.

Network-on-Chip (NoC) architecture is the communication heart of the processing cores in Multiprocessors System-on-Chip (MPSoC), where messages are routed from a source to a destination through intermediate nodes. Therefore, NoC has become a target to security attacks. By experiencing outsourcing design, NoC can be infected with a malicious Hardware Trojans (HTs) which potentially degrade the system performance or leave a backdoor for secret key leaking. In this paper, we propose a HT model that applies a denial of service attack by misrouting the packets, which causes deadlock and consequently degrading the NoC performance. We present a secure routing algorithm that provides a runtime HT detection and avoiding scheme. Results show that our proposed model has negligible overhead in area and power, 0.4% and 0.6%, respectively.

In Ad hoc networks the main purpose is communication without infrastructure and there are many implementations already done on that. There is little effort done for security to prevent threats in ad hoc networks (like MANETs). It is already proven that; there is no any centralized mechanism for defence against threats, such as a firewall, an intrusion detection system, or a proxy in ad hoc networks. Ad hoc networks are very convenient due to its features like self-maintenance, self-organizing and providing wireless communication. In Ad hoc networks there is no fixed infrastructure in which every node works like simply a router which stores and forwards packet to final destination. Due to these dynamic topology features, Ad hoc networks are anywhere, anytime. Therefore, it is necessary to make a secure mechanism for the ad hoc components so that with flexibility they have that security also. This paper shows the secure and flexible implementation about to protect any ad hoc networks. This proposed system design is perfect solution to provide security with flexibility by providing a hybrid system which combines ECC and MAES to detect and prevent Ad hoc network attacks using Intrusion detection system. The complete proposed system designed on NS 2.35 software using Ubuntu (Linux) OS.

Location determination in the indoor areas as well as in open areas is important for many applications. But location determination in the indoor areas is a very difficult process compared to open areas. The Global Positioning System (GPS) signals used for position detection is not effective in the indoor areas. Wi-Fi signals are a widely used method for localization detection in the indoor area. In the indoor areas, localization can be used for many different purposes, such as intelligent home systems, locations of people, locations of products in the depot. In this study, it was tried to determine localization for with the classification method for 4 different areas by using Wi-Fi signal values obtained from different routers for indoor location determination. Linear discriminant analysis (LDA) classification was used for classification. In the test using 10k fold cross-validation, 97.2% accuracy value was calculated.

Routers are important devices in the networks that carry the burden of transmitting information among the communication devices on the Internet. If a malicious adversary wants to intercept the information or paralyze the network, it can directly attack the routers and then achieve the suspicious goals. Thus, preventing router security is of great importance. However, router systems are notoriously difficult to understand or diagnose for their inaccessibility and heterogeneity. The common way of gaining access to the router system and detecting the anomaly behaviors is to inspect the router syslogs or monitor the packets of information flowing to the routers. These approaches just diagnose the routers from one aspect but do not consider them from multiple views. In this paper, we propose an approach to detect the anomalies and faults of the routers with multiple information learning. We try to use the routers' information not from the developer's view but from the user' s view, which does not need any expert knowledge. First, we do the offline learning to transform the benign or corrupted user actions into the syslogs. Then, we try to decide whether the input routers' conditions are poor or not with clustering. During the detection phase, we use the distance between the event and the cluster to decide if it is the anomaly event and we can provide the corresponding solutions. We have applied our approach in a university network which contains Cisco, Huawei and Dlink routers for three months. We aligned our experiment with former work as a baseline for comparison. Our approach can gain 89.6% accuracy in detecting the attacks which is 5.1% higher than the former work. The results show that our approach performs in limited time as well as memory usages and has high detection and low false positives.

As the Internet technology develops rapidly, attacks against Tor networks becomes more and more frequent. So, it's more and more difficult for Tor network to meet people's demand to protect their private information. A method to improve the anonymity of Tor seems urgent. In this paper, we mainly talk about the principle of Tor, which is the largest anonymous communication system in the world, analyze the reason for its limited efficiency, and discuss the vulnerability of link fingerprint and node selection. After that, a node recognition model based on SVM is established, which verifies that the traffic characteristics expose the node attributes, thus revealing the link and destroying the anonymity. Based on what is done above, some measures are put forward to improve Tor protocol to make it more anonymous.

Information-centric networking (ICN) is a Future Internet paradigm which uses named information (data objects) instead of host-based end-to-end communications. In-network caching is a key pillar of ICN. Basically, data objects are cached in ICN routers and retrieved from these network elements upon availability when they are requested. It is a particularly promising networking approach due to the expected benefits of data dissemination efficiency, reduced delay and improved robustness for challenging communication scenarios in IoT domain. From the security perspective, ICN concentrates on securing data objects instead of ensuring the security of end-to-end communication link. However, it inherently involves the security challenge of access control for content. Thus, an efficient access control mechanism is crucial to provide secure information dissemination. In this work, we investigate Attribute Based Encryption (ABE) as an access control apparatus for information centric IoT. Moreover, we elaborate on how such a system performs for different parameter settings such as different numbers of attributes and file sizes.

With the rapid development of network and communication technologies, everything is able to be connected to the Internet. IoT devices, which include home routers, IP cameras, wireless printers and so on, are crucial parts facilitating to build pervasive and ubiquitous networks. As the number of IoT devices around the world increases, the security issues become more and more serious. To handle with the security issues and protect the IoT devices from being compromised, the firmware of devices needs to be strengthened by discovering and repairing vulnerabilities. Current vulnerability detection tools can only help strengthening traditional software, nevertheless these tools are not practical enough for IoT device firmware, because of the peculiarity in firmware's structure and embedded device's architecture. Therefore, new vulnerability detection framework is required for analyzing IoT device firmware. This paper reviews related works on vulnerability detection in IoT firmware, proposes and implements a framework to automatically detect authentication-bypass flaws in a large scale of Linux-based firmware. The proposed framework is evaluated with a data set of 2351 firmware images from several target vendors, which is proved to be capable of performing large-scale and automated analysis on firmware, and 1 known and 10 unknown authentication-bypass flaws are found by the analysis.