Biblio

The marine and maritime domain is well represented in the Sustainable Development Goals (SDG) envisaged by the United Nations, which aim at conserving and using the oceans, seas and their resources for sustainable development. At the same time, there is a need for improved safety in navigation, especially in coastal areas. Up to date, there exist operational services based on advanced technologies, including remote sensing and in situ monitoring networks which provide aid to the navigation and control over the environment for its preservation. Yet, the possibilities offered by crowdsensing have not yet been fully explored. This paper addresses this issue by presenting an app based on a crowdsensing approach for improved safety and awareness at sea. The app can be integrated into more comprehensive systems and frameworks for environmental monitoring as envisaged in our future work.

Purpose: Determine the main theoretical aspects of managing the resilience of an industrial enterprise in conditions of uncertainty. Method: The static control methods include the technology of the matrix aggregate computer (MAC) and the R-lenses, and the dynamic control methods - the technology based on the 4x6 matrix model. All these methods are based on the results of the theory of fuzzy sets and soft computing. Result: A comparative analysis of the resilience of 82 largest industrial enterprises in five industry classes was carried out, R-lenses were constructed for these classes, and the main factors affecting the resilience of industrial companies were evaluated. Conclusions: The central problem points in assessing and ensuring the resilience of enterprises are: a) correct modeling of external disturbances; b) ensuring the statistical homogeneity of the source data array.

Mitigating cyber threats requires adequate understanding of the attacker characteristics in particular their patterns. Such knowledge is essential in addressing the defensive measures that mitigate the attack. If the attacker enters in the network system, the game tree model generates resources by to counter such threat. This is done by altering the parity in the next game tree iteration which yield an adequate response to counter it. If an attacker enters a network system, and a game tree models the resources he must interface with, then that game tree can be altered, by changing the parity on the next to last iteration. This paper analyzes the sequence of patterns based on incoming attacks. The detection of attacker’s pattern and subsequent changes in iterations to counter threat can be viewed as adequate resource or know how in cyber threat mitigations It was realized that changing the game tree of the hacker deprives the attacker of network resources and hence would represent a defensive measure against the attack; that is changing varying or understanding attacker paths, creates an effective defensive measure to protect the system against the incoming threats.. In this paper we analyze a unique combination of CFR and MCTS that attempts to detect the behavior of a hacker. Counterfactual Regret (CFR) is a game theory concept that helps identify patterns of attacks. The pattern recognition concept of Monte Carlo Tree Search (MCTS) is used in harmony with CFR in order to enhance the detection of attacks.

Underwater networks have the potential to allow previously unexplored applications as well as improve our ability to observe and forecast the ocean. Underwater acoustic sensor networks (UASNs) are often deployed in unprecedented and hostile waters and face many security threats. Applications based on UASNs such as coastal defense, pollution monitoring, assisted navigation to name a few, require secure communication. A new set of communication protocols and cooperative coordination algorithms have been proposed to enable collaborative monitoring tasks. However, such protocols overlook security as a key performance indicator. Spoofing, altering, or replaying routing information can affect the entire network, making UASN vulnerable to routing attacks such as selective forwarding, sinkhole attack, Sybil attack, acknowledgement spoofing and HELLO flood attack. The lack of security against such threats is startling if it is observed that security is indeed an important requirement in many emerging civilian and military applications. In this work, the sinkhole attack prevalent among UASNs is looked at and discuss mitigation approaches that can feasibly be implemented in UnetStack3.

We propose a method to let a source and a destination agree on a key that remains secret to a potential eavesdropper in an underwater acoustic network (UWAN). We generate the key from the propagation delay measured over a set of multihop routes: this harvests the randomness in the UWAN topology and turns the slow sound propagation in the water into an advantage for the key agreement protocol. Our scheme relies on a route discovery handshake. During this process, all intermediate relays accumulate message processing delays, so that both the source and the destination can compute the actual propagation delays along each route, and map this information to a string of bits. Finally, via a secret key agreement from the information-theoretic security framework, we obtain an equal set of bits at the source and destination, which is provably secret to a potential eavesdropper located away from both nodes. Our simulation results show that, even for small UWANs of 4 nodes, we obtain 11 secret bits per explored topology, and that the protocol is insensitive to an average node speed of up to 0.5 m/s.

Because the underwater acoustic communication network transmits data through the underwater acoustic wireless link, the Underwater Acoustic Communication Network is easy to suffer from the external artificial interference, in this paper, the detection algorithm of wormhole attack in Underwater Acoustic Communication Network based on Azimuth measurement technology is studied. The existence of wormhole attack is judged by Azimuth or distance outliers, and the security performance of underwater acoustic communication network is evaluated. The influence of different azimuth direction errors on the detection probability of wormhole attack is analyzed by simulation. The simulation results show that this method has a good detection effect for Underwater Acoustic Communication Network.

Underwater networks have the potential to enable unexplored applications and to enhance our ability to observe and predict the ocean. Underwater acoustic sensor networks (UASNs) are often deployed in unprecedented and hostile waters and face many security threats. Applications based on UASNs such as coastal defense, pollution monitoring, assisted navigation to name a few, require secure communication. A new set of communication protocols and cooperative coordination algorithms have been proposed to enable collaborative monitoring tasks. However, such protocols overlook security as a key performance indicator. Spoofing, altering, or replaying routing information can affect the entire network, making UASN vulnerable to routing attacks such as selective forwarding, sinkhole attack, Sybil attack, acknowledgement spoofing and HELLO flood attack. The lack of security against such threats is startling if maintained that security is indeed an important requirement in many emerging civilian and military applications. In this work, we look at one of the most prevalent attacks among UASNs which is Sybill attack and discuss mitigation approaches for it. Then, feasibly implemented the attack in UnetStack3 to simulate real-life scenario.

Hackers create different types of Malware such as Trojans which they use to steal user-confidential information (e.g. credit card details) with a few simple commands, recent malware however has been created intelligently and in an uncontrolled size, which puts malware analysis as one of the top important subjects of information security. This paper proposes an efficient dynamic malware-detection method based on API similarity. This proposed method outperform the traditional signature-based detection method. The experiment evaluated 197 malware samples and the proposed method showed promising results of correctly identified malware.

User attribution process based on human inherent dynamics and preference is one area of research that is capable of elucidating and capturing human dynamics on the Internet. Prior works on user attribution concentrated on behavioral biometrics, 1-to-1 user identification process without consideration for individual preference and human inherent temporal tendencies, which is capable of providing a discriminatory baseline for online users, as well as providing a higher level classification framework for novel user attribution. To address these limitations, the study developed a temporal model, which comprises the human Polyphasia tendency based on Polychronic-Monochronic tendency scale measurement instrument and the extraction of unique human-centric features from server-side network traffic of 48 active users. Several machine-learning algorithms were applied to observe distinct pattern among the classes of the Polyphasia tendency, through which a logistic model tree was observed to provide higher classification accuracy for a 1-to-N user attribution process. The study further developed a high-level attribution model for higher-level user attribution process. The result from this study is relevant in online profiling process, forensic identification and profiling process, e-learning profiling process as well as in social network profiling process.

The globalization of trade is due to the transportation possibilities and the standardization (containerization of freight). The dependency of the economy to the sea and to the merchant navy has increase this last decade. This process forms a worldwide maritime network between the different locations of production and consumption. This network, representing between 80 % and 90% of world traffic is a major economic concern, including freight distribution, raw materials or energy. Rodrigue demonstrates[1] the economic dependency of energy is increasing in the industrialized countries (North America, Europe, East Asia). The inter-regional trade of oil was 31 million bbl/day in 2002 and is expected to grow up to 57 bbl/day in 2030 [2]. Most of the international traffic use a maritime way, where may occur disruptions. For example, the Suez crisis (1956-1957) caused a closure of the canal, reducing the throughput capacity of transportation. This disruption cost a 2 millions of barrels lost per day. This article focuses on vulnerability of the energy supply, and proposes a methodology to formalize and assess the vulnerability of the network by taking into account the spatial structure of maritime territories.